From 5acc7f287023f349a96594d241cb6e40a6c7e6c3 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 13:04:30 -0700 Subject: [PATCH 01/52] Add Heatwave power and thermal cluster simulator (e003) RC thermal model with exact stepping, calibration, GPU throttle and cooling power models; simulated fault and grid events with optional precursors; a two-site rack-level cluster simulator with controller hook, e001-style work accounting, multi-scale outputs and known-future covariates; default two-site seven-day scenario and tests. All outputs are simulated. Co-Authored-By: Claude Sonnet Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- experiments/e003-heatwave/run_sim_smoke.py | 232 ++ .../e003-heatwave/sim-scenario-v1.json | 404 +++ gpu_stack/research/heatwave_events.py | 620 +++++ gpu_stack/research/heatwave_sim.py | 2172 +++++++++++++++++ gpu_stack/research/heatwave_thermal.py | 651 +++++ tests/test_research_heatwave_sim.py | 629 +++++ tests/test_research_heatwave_thermal.py | 243 ++ 7 files changed, 4951 insertions(+) create mode 100644 experiments/e003-heatwave/run_sim_smoke.py create mode 100644 experiments/e003-heatwave/sim-scenario-v1.json create mode 100644 gpu_stack/research/heatwave_events.py create mode 100644 gpu_stack/research/heatwave_sim.py create mode 100644 gpu_stack/research/heatwave_thermal.py create mode 100644 tests/test_research_heatwave_sim.py create mode 100644 tests/test_research_heatwave_thermal.py diff --git a/experiments/e003-heatwave/run_sim_smoke.py b/experiments/e003-heatwave/run_sim_smoke.py new file mode 100644 index 0000000..3dc21da --- /dev/null +++ b/experiments/e003-heatwave/run_sim_smoke.py @@ -0,0 +1,232 @@ +"""Smoke run of the e003 Heatwave simulator on the default scenario (simulated data). + +Runs the seven-day default scenario twice (no controller, and the reactive +site-cap baseline), checks determinism and hierarchy conservation, and writes +summary statistics only to ``results/sim-smoke-v1.json``. Every number in the +output file is produced by this script. + + python experiments/e003-heatwave/run_sim_smoke.py + +Needs numpy and scipy. Provenance is written with ``heatwave.provenance`` +(installed from the Heatwave repository) and then pointed at this repository's +git state. +""" + +from __future__ import annotations + +import copy +import hashlib +import json +import resource +import subprocess +import sys +import time +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[1] +sys.path.insert(0, str(ROOT)) + +from gpu_stack.research.heatwave_sim import ( # noqa: E402 + ReactiveSiteCapController, + load_scenario, + run_scenario, +) +from gpu_stack.research.heatwave_thermal import fit # noqa: E402 + +SCENARIO = HERE / "sim-scenario-v1.json" +OUT = HERE / "results" / "sim-smoke-v1.json" +E002_RAW = ROOT / "docs" / "data" / "e002-checkpoint-power-raw-v1.json" + + +def run_summary(res, wall_s: float) -> dict: + run = res._run + summary = res.summary() + hourly = res.site_mean_power_w_1h + return { + "wall_seconds": wall_s, + "summary": summary, + "site_hourly_mean_power_w": {sid: [float(x) for x in hourly[:, i]] for i, sid in enumerate(run.site_ids)}, + "site_hourly_cost_usd": {sid: [float(x) for x in res.site_cost_usd_1h[:, i]] for i, sid in enumerate(run.site_ids)}, + "site_hourly_carbon_kg": {sid: [float(x) for x in res.site_carbon_kg_1h[:, i]] for i, sid in enumerate(run.site_ids)}, + "rack_max_air_temp_c": {rid: float(run.rack_air_b[:, i].max()) for i, rid in enumerate(run.rack_ids)}, + "rack_max_gpu_temp_c": {rid: float(run.rack_maxgpu_b[:, i].max()) for i, rid in enumerate(run.rack_ids)}, + "event_counts": count_kinds(res.events), + "events": res.events, + "actions_total": len(res.actions), + "actions_applied": sum(1 for a in res.actions if a["status"] == "applied"), + "actions_first_20": res.actions[:20], + "jobs": res.jobs_table, + } + + +def count_kinds(events: list) -> dict: + out: dict = {} + for e in events: + key = e["kind"] if "phase" not in e else f"{e['kind']}:{e['phase']}" + out[key] = out.get(key, 0) + 1 + return out + + +def conservation_check(scenario: dict) -> dict: + sc = copy.deepcopy(scenario) + sc["horizon_s"] = 3600 + sc["events"] = [] + sc["outputs"] = {"gpu_windows": [[0, 3600]]} + res = run_scenario(sc, seed=0) + run = res._run + arr = res.to_arrays() + gpu = arr["gpu_power_w_window_0"] + rack_from_gpu = np.add.reduceat(gpu, run.rack_start, axis=0).T + site_from_rack = np.stack([arr["rack_power_w"][:, run.rack_site == s].sum(axis=1) for s in range(run.S)], axis=1) + rack_total = arr["rack_gpu_power_w"] + np.repeat(run.rack_host_b, run.K, axis=0) + arr["rack_cooling_power_w"] + + def rel(a, b): + return float(np.max(np.abs(a - b) / np.maximum(np.abs(b), 1.0))) + + return { + "data_kind": "simulated", + "window_s": [0, 3600], + "max_rel_error_gpu_to_rack": rel(rack_from_gpu, arr["rack_gpu_power_w"]), + "max_rel_error_rack_components": rel(rack_total, arr["rack_power_w"]), + "max_rel_error_rack_to_site": rel(site_from_rack, arr["site_power_w"]), + "max_rel_error_energy_hourly": rel(arr["site_power_w"].sum(axis=0) / 3.6e6, arr["site_energy_kwh_1h"].sum(axis=0)), + } + + +def determinism_check(scenario: dict) -> dict: + sc = copy.deepcopy(scenario) + sc["horizon_s"] = 6 * 3600 + sc["outputs"] = {"gpu_windows": []} + sc["events"] = [e for e in sc["events"] if e["onset_s"] < 6 * 3600] + + def digest(seed: int) -> str: + res = run_scenario(sc, seed=seed) + return hashlib.sha256(res.site_power_w.tobytes() + res.rack_temp_c_1min.tobytes()).hexdigest() + + a, b, c = digest(0), digest(0), digest(1) + return {"horizon_s": 6 * 3600, "same_seed_identical": a == b, "different_seed_differs": a != c} + + +def laptop_rc_fit() -> dict: + """RC fit on measured laptop board telemetry (power in, die temperature out). + + The telemetry updates about every 0.49 s and each run lasts about 10 s, so + this checks that the fit machinery runs on a measured trace; the fitted time + constants are reported next to the update period, not used for racks. + """ + data = json.loads(E002_RAW.read_text()) + rows = [] + for run in data["runs"]: + trace = run["telemetry_trace"] + if not trace.get("available"): + continue + ts = np.array([p["timestamp"] for p in trace["points"]], dtype=float) + power = np.array([p["gpu_board_power_w"] for p in trace["points"]], dtype=float) + temp = np.array([p["temperature_c"] for p in trace["points"]], dtype=float) + dt = 0.25 + grid = np.arange(0.0, ts[-1], dt) + idx = np.searchsorted(ts, grid, side="right") - 1 + _, report = fit(power[idx], np.zeros(grid.size), temp[idx], dt) + rows.append(report) + period = float(data["logger_calibration"]["effective_update_period_seconds"]) + tau = np.array([r["time_constant_s"] for r in rows]) + return { + "data_kind": "measured", + "source": "docs/data/e002-checkpoint-power-raw-v1.json", + "runs_fitted": len(rows), + "resample_dt_s": 0.25, + "telemetry_update_period_s": period, + "median_rmse_c": float(np.median([r["rmse_c"] for r in rows])), + "median_r2": float(np.median([r["r2"] for r in rows])), + "median_time_constant_s": float(np.median(tau)), + "fraction_time_constant_below_update_period": float(np.mean(tau < period)), + "all_converged": bool(all(r["success"] for r in rows)), + "temperature_resolution_c": 1.0, + } + + +def git_state() -> dict: + def git(*args: str) -> str: + return subprocess.run(["git", *args], cwd=ROOT, capture_output=True, text=True, check=True).stdout.strip() + + dirty = bool(git("status", "--porcelain", "--", ".", ":(exclude)experiments/e003-heatwave/results")) + return {"commit": git("rev-parse", "HEAD"), "branch": git("rev-parse", "--abbrev-ref", "HEAD"), "dirty": str(dirty).lower()} + + +def main() -> None: + scenario = load_scenario(SCENARIO) + out: dict = {"data_kind": "simulated", "scenario_id": scenario["scenario_id"]} + + t0 = time.time() + base = run_scenario(scenario, seed=0) + base_wall = time.time() - t0 + out["no_controller"] = run_summary(base, base_wall) + out["peak_rss_mb_after_first_run"] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.0 + + t0 = time.time() + capped = run_scenario(scenario, controller=ReactiveSiteCapController(margin=0.97), seed=0) + out["reactive_site_cap"] = run_summary(capped, time.time() - t0) + out["peak_rss_mb_after_second_run"] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.0 + + run = base._run + arrays = base.to_arrays() + out["configuration"] = { + "horizon_s": run.cfg.horizon_s, + "base_dt_s": run.dt, + "control_dt_s": run.cdt, + "sites": run.site_ids, + "racks": [{"rack_id": r, "site_id": s, "gpus": int(n)} for r, s, n in zip(run.rack_ids, run.rack_site_ids, run.rack_n)], + "gpus_total": int(run.G), + "rack_heat_capacity_j_per_k": [float(x) for x in run.C], + "rack_resistance_k_per_w": [float(x) for x in run.Rres], + "rack_cooling_capacity_nominal_w": [float(x) for x in run.cap_nominal], + "jobs": len(run.jobs), + "jobs_multisite": sum(1 for j in run.jobs if j.multisite), + "gpu_windows_s": [[w["start_s"], w["end_s"]] for w in base.gpu_windows], + "gpu_window_array_shapes": [list(w["gpu_power_w"].shape) for w in base.gpu_windows], + "array_shapes": {k: list(v.shape) for k, v in arrays.items() if isinstance(v, np.ndarray)}, + } + out["power_levels_w_per_gpu"] = base.meta["power_levels"] + out["measured_e002"] = base.meta["measured_e002"] + out["throttle_model"] = base.meta["throttle"] + out["cooling_power_model"] = base.meta["cooling_power_model"] + out["breaker_model"] = base.meta["breaker"] + out["ground_truth_events"] = base.ground_truth_events + out["plan_vs_realized"] = { + "planned_jobs": len(base.plan), + "jobs_started_at_planned_time": sum( + 1 + for pj in base.plan + if any(e["kind"] == "job_start" and e["job_id"] == pj.job_id and abs(e["t_s"] - pj.start_s) < 1e-6 for e in base.events) + ), + "note": "events (faults, throttling) perturb the realized schedule; the plan assumes none", + } + out["conservation_check_event_free_1h"] = conservation_check(scenario) + out["determinism_check"] = determinism_check(scenario) + out["laptop_rc_fit"] = laptop_rc_fit() + out["assumptions"] = base.meta["assumptions"] + out["simplifications"] = base.meta["simplifications"] + + try: + from heatwave.provenance import write_result + except ImportError as exc: # pragma: no cover + raise SystemExit("heatwave.provenance is needed to write the result file") from exc + record = write_result( + OUT, out, script=__file__, data_kind="simulated", + inputs=[SCENARIO, E002_RAW], + notes="Seven-day default scenario; all outputs simulated. laptop_rc_fit uses measured telemetry.", + ) + # write_result records the Heatwave repository's git state; point it at this repository. + record["provenance"]["git"] = git_state() + record["provenance"]["script"] = str(Path(__file__).resolve().relative_to(ROOT)) + record["provenance"]["inputs"] = {str(Path(p).resolve().relative_to(ROOT)): h for p, h in zip([SCENARIO, E002_RAW], [ + hashlib.sha256(SCENARIO.read_bytes()).hexdigest(), hashlib.sha256(E002_RAW.read_bytes()).hexdigest()])} + OUT.write_text(json.JSONEncoder(indent=1, allow_nan=False).encode(record) + "\n") + print(f"wrote {OUT} ({OUT.stat().st_size / 1024:.0f} KiB)") + + +if __name__ == "__main__": + main() diff --git a/experiments/e003-heatwave/sim-scenario-v1.json b/experiments/e003-heatwave/sim-scenario-v1.json new file mode 100644 index 0000000..bb35481 --- /dev/null +++ b/experiments/e003-heatwave/sim-scenario-v1.json @@ -0,0 +1,404 @@ +{ + "schema": "gpu-stack.e003-heatwave-sim-scenario.v1", + "data_kind": "simulated", + "scenario_id": "e003-heatwave-sim-v1", + "description": "Two-site, eight-rack simulated GPU cluster over seven days with a synthetic job mix, a synthetic heat wave, and scheduled fault and grid events (with and without precursors). Nothing in this file is measured data.", + "start_utc": "2026-07-06T00:00:00Z", + "horizon_s": 604800, + "base_dt_s": 1.0, + "control_dt_s": 10.0, + "controller_interval_s": 60.0, + "history_s": 7200.0, + "known_future_horizon_s": 86400.0, + "sites": [ + { + "site": { + "site_id": "site-a", + "accelerator_type": "NVIDIA H100 SXM 80GB", + "accelerator_count": 104, + "accelerator_flops_per_second": 500000000000000.0, + "collective_bandwidth_bytes_per_second": 400000000000.0, + "state_transfer_bandwidth_bytes_per_second": 12500000000.0, + "checkpoint_bandwidth_bytes_per_second": 10000000000.0, + "base_power_w": 0.0, + "accelerator_power_w": 700.0, + "power_cap_w": 100000.0, + "cooling_capacity_w": 110000.0, + "grid_import_limit_w": 110000.0 + }, + "racks": [ + { + "rack_id": "a1", + "n_gpus": 32, + "breaker_rating_w": 24640.0 + }, + { + "rack_id": "a2", + "n_gpus": 32, + "breaker_rating_w": 24640.0 + }, + { + "rack_id": "a3", + "n_gpus": 24, + "breaker_rating_w": 18480.0 + }, + { + "rack_id": "a4", + "n_gpus": 16, + "breaker_rating_w": 12320.0 + } + ] + }, + { + "site": { + "site_id": "site-b", + "accelerator_type": "NVIDIA H100 SXM 80GB", + "accelerator_count": 80, + "accelerator_flops_per_second": 500000000000000.0, + "collective_bandwidth_bytes_per_second": 400000000000.0, + "state_transfer_bandwidth_bytes_per_second": 12500000000.0, + "checkpoint_bandwidth_bytes_per_second": 10000000000.0, + "base_power_w": 0.0, + "accelerator_power_w": 700.0, + "power_cap_w": 77000.0, + "cooling_capacity_w": 85000.0, + "grid_import_limit_w": 85000.0 + }, + "racks": [ + { + "rack_id": "b1", + "n_gpus": 32, + "breaker_rating_w": 24640.0 + }, + { + "rack_id": "b2", + "n_gpus": 24, + "breaker_rating_w": 18480.0 + }, + { + "rack_id": "b3", + "n_gpus": 16, + "breaker_rating_w": 12320.0 + }, + { + "rack_id": "b4", + "n_gpus": 8, + "breaker_rating_w": 6160.0 + } + ] + } + ], + "gpu": { + "preset": "h100_sxm_80gb_gpu", + "compute_fraction_of_tdp": 0.8, + "host_fraction_of_compute": 0.25, + "compute_fraction_of_step": 0.7 + }, + "power_levels": { + "e002_raw_path": "docs/data/e002-checkpoint-power-raw-v1.json", + "comm_ratio": "idle_ratio", + "checkpoint_ratio": "idle_ratio", + "idle_ratio_override": null + }, + "thermal": { + "heat_capacity_j_per_k_per_gpu": 8153.125, + "design_air_rise_k": 12.0, + "cooling_lag_s": 120.0, + "supply_setpoint_c": 24.0, + "outdoor_coupling_k_per_k": 0.2, + "outdoor_reference_c": 30.0, + "capacity_derate_per_k": 0.01, + "capacity_derate_reference_c": 30.0, + "gpu_to_air_k_per_w": 0.05, + "gpu_to_air_rel_sigma": 0.1, + "breaker_reset_delay_s": 1800.0, + "gpu_shutdown_c": 95.0, + "thermal_shutdown_hold_s": 1800.0 + }, + "throttle": { + "throttle_threshold_c": 80.0, + "throttle_span_c": 8.0, + "min_clock_fraction": 0.5, + "power_exponent": 2.0 + }, + "cooling_power": { + "kind": "carnot", + "pue": 1.2, + "reference_outdoor_c": 20.0, + "reference_supply_c": 24.0, + "evaporator_approach_k": 10.0, + "condenser_approach_k": 10.0, + "cop_min": 1.5, + "cop_max": 30.0 + }, + "breaker": { + "nontrip_ratio": 1.13, + "trip_ratio": 1.45, + "conventional_time_s": 3600.0 + }, + "transfer": { + "wan_bandwidth_bytes_per_second": 3125000000.0, + "wan_latency_s": 0.02, + "restore_bandwidth_bytes_per_second": 200000000000.0, + "fixed_restart_latency_s": 0.02, + "membership_reconfiguration_s": 0.012, + "checkpoint_bytes_per_gpu": 4375000000.0 + }, + "jobs": { + "generate": { + "seed": 7, + "utilization_goal": 0.72, + "gpu_choices": [ + 8, + 16, + 32, + 64 + ], + "gpu_weights": [ + 3, + 3, + 2, + 1 + ], + "duration_range_s": [ + 3600, + 172800 + ], + "step_time_choices_s": [ + 2.0, + 4.0, + 8.0 + ], + "checkpoint_every_range_s": [ + 1200, + 3600 + ], + "checkpoint_duration_range_s": [ + 20, + 120 + ], + "value_range": [ + 1.0, + 4.0 + ], + "flexible_fraction": 0.4, + "multisite_fraction": 0.1, + "max_start_delay_s": 21600, + "initial_fraction": 0.5 + } + }, + "events": [ + { + "kind": "cooling_failure", + "event_id": "cf-1-precursor", + "site_id": "site-a", + "rack_id": "a1", + "onset_s": 129600, + "duration_s": 5400, + "remaining_capacity_fraction": 0.3, + "precursors": [ + { + "channel": "supply_temp_c", + "lead_s": 3600, + "strength": 3.0 + } + ] + }, + { + "kind": "cooling_failure", + "event_id": "cf-2-sudden", + "site_id": "site-b", + "rack_id": "b2", + "onset_s": 276480, + "duration_s": 3600, + "remaining_capacity_fraction": 0.2 + }, + { + "kind": "cooling_failure", + "event_id": "cf-3-fan-precursor", + "site_id": "site-a", + "onset_s": 440640, + "duration_s": 7200, + "remaining_capacity_fraction": 0.6, + "resistance_multiplier": 1.3, + "precursors": [ + { + "channel": "thermal_resistance", + "lead_s": 7200, + "strength": 0.25 + } + ] + }, + { + "kind": "breaker_trip", + "event_id": "bt-1-derate-precursor", + "site_id": "site-a", + "rack_id": "a1", + "onset_s": 198720, + "duration_s": 14400, + "mode": "derate", + "derate_fraction": 0.6, + "precursor_lead_s": 3600 + }, + { + "kind": "breaker_trip", + "event_id": "bt-2-forced", + "site_id": "site-b", + "rack_id": "b1", + "onset_s": 380160, + "duration_s": 1200, + "mode": "forced" + }, + { + "kind": "grid_curtailment", + "event_id": "gc-1-announced", + "site_id": "site-a", + "onset_s": 233280, + "duration_s": 7200, + "limit_fraction": 0.6, + "announce_lead_s": 1800 + }, + { + "kind": "grid_curtailment", + "event_id": "gc-2-unannounced", + "site_id": "site-b", + "onset_s": 483840, + "duration_s": 5400, + "limit_fraction": 0.5 + }, + { + "kind": "hardware_fault", + "event_id": "hw-1-precursor", + "site_id": "site-a", + "rack_id": "a2", + "gpu_slot": 5, + "onset_s": 103680, + "repair_s": 21600, + "precursors": [ + { + "channel": "gpu_temp_c", + "lead_s": 7200, + "strength": 6.0 + } + ] + }, + { + "kind": "hardware_fault", + "event_id": "hw-2-sudden", + "site_id": "site-b", + "rack_id": "b1", + "gpu_slot": 3, + "onset_s": 328320, + "repair_s": 14400 + }, + { + "kind": "hardware_fault", + "event_id": "hw-3-sudden", + "site_id": "site-a", + "rack_id": "a1", + "gpu_slot": 2, + "onset_s": 535680, + "repair_s": 14400 + } + ], + "environment": { + "seed": 11, + "synthetic": { + "start_hour_utc": 0.0, + "sites": { + "site-a": { + "utc_offset_h": -7.0, + "outdoor": { + "mean_c": 24.0, + "diurnal_amplitude_c": 7.0, + "peak_hour_local": 15.0, + "noise_sigma_c": 0.8, + "noise_ar1": 0.9, + "heatwaves": [ + { + "center_h": 96.0, + "width_h": 18.0, + "add_c": 11.0 + } + ] + }, + "forecast_error_sigma_c": 1.2, + "price": { + "base_usd_per_kwh": 0.08, + "diurnal_amplitude": 0.35, + "peak_hour_local": 18.0 + }, + "carbon": { + "base_g_per_kwh": 350.0, + "diurnal_amplitude": 0.2, + "peak_hour_local": 20.0 + } + }, + "site-b": { + "utc_offset_h": -5.0, + "outdoor": { + "mean_c": 22.0, + "diurnal_amplitude_c": 6.0, + "peak_hour_local": 15.0, + "noise_sigma_c": 0.8, + "noise_ar1": 0.9, + "heatwaves": [ + { + "center_h": 100.0, + "width_h": 20.0, + "add_c": 9.0 + } + ] + }, + "forecast_error_sigma_c": 1.2, + "price": { + "base_usd_per_kwh": 0.07, + "diurnal_amplitude": 0.3, + "peak_hour_local": 19.0 + }, + "carbon": { + "base_g_per_kwh": 450.0, + "diurnal_amplitude": 0.15, + "peak_hour_local": 19.0 + } + } + } + } + }, + "outputs": { + "gpu_windows": [ + [ + 0, + 3600 + ], + [ + 129600, + 133200 + ] + ] + }, + "assumptions": [ + "Everything in this scenario is simulated. No number here is a measurement of a real cluster.", + "Site capacities (power cap, cooling capacity, import limit) are scenario choices; the per-GPU accelerator fields follow the e001 screening scenario H100 SXM sites (experiments/e001-beyond-one-datacenter/screening-scenario-v1.json).", + "Compute-phase GPU power is 0.8 x the cited H100 SXM TDP (700 W): the 0.8 is the gpu_stack calculator's gpu_power_fraction assumption. Host overhead is 0.25 x compute power: the calculator's server_power_fraction assumption.", + "Idle/compute and relative compute-phase noise are measured ratios from the e002 laptop RTX telemetry. The communication-phase and checkpoint levels are set equal to the measured idle ratio (structural assumption, no measurement). The measured checkpoint/compute ratio is reported alongside in the result.", + "Fraction of a training step spent in compute (0.7) is an assumption.", + "Rack heat capacity per GPU = DGX H100 system mass 130.45 kg / 8 GPUs x an assumed effective specific heat of 500 J/(kg K). Mass source: https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html", + "Supply setpoint 24 C is an assumption inside the DGX H100 cited operating range of 5-30 C (same source). Design air rise 12 K, cooling lag 120 s, outdoor coupling 0.2 K/K above 30 C, capacity derate 1 percent per K above 30 C, GPU-to-air offset 0.05 K/W, protective GPU shutdown at 95 C and its 1800 s hold are assumptions.", + "Throttle threshold 80 C, span 8 K, minimum clock 0.5 and power exponent 2 are assumptions; they are not H100 datasheet values.", + "Cooling power: Carnot-shaped COP with second-law efficiency chosen so the overhead equals the gpu_stack calculator PUE assumption (1.2) at 20 C outdoor and 24 C supply; approach temperatures 10 K and COP bounds 1.5 to 30 are assumptions.", + "Breaker curve: IEC 60898-1 conventional values (1.13 and 1.45 x rated, 1 hour), https://www.se.com/au/en/faqs/FA369327/. Rack breaker ratings are 1.1 x the full-load rack power; reset delay 1800 s is an assumption.", + "WAN, restore, restart and membership parameters come from the e001 scenarios; the per-GPU checkpoint size is the e001 screening checkpoint (1.12 TB) divided by its 256 GPUs.", + "The job mix, outdoor temperature (including the heat wave), electricity price and carbon intensity are synthetic and seeded. Job value per GPU-hour is a synthetic number with no source.", + "Event precursors (supply-temperature creep, resistance rise, GPU temperature creep, breaker derate ramp, curtailment announcement) are assumptions of the simulation, not measured facts." + ], + "provenance": [ + "https://www.nvidia.com/en-us/data-center/h100/", + "https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html", + "https://www.se.com/au/en/faqs/FA369327/", + "https://en.wikipedia.org/wiki/Coefficient_of_performance", + "docs/data/e002-checkpoint-power-raw-v1.json", + "experiments/e001-beyond-one-datacenter/recovery-scenario-v2.json", + "experiments/e001-beyond-one-datacenter/screening-scenario-v1.json" + ] +} \ No newline at end of file diff --git a/gpu_stack/research/heatwave_events.py b/gpu_stack/research/heatwave_events.py new file mode 100644 index 0000000..77ee82e --- /dev/null +++ b/gpu_stack/research/heatwave_events.py @@ -0,0 +1,620 @@ +"""Fault and grid events for the Heatwave cluster simulator (all simulated). + +IMPORTANT: every event in this module is a simulation input. Precursors are +assumptions of the simulation, not measured facts. When a scenario says a +cooling failure is preceded by a slow rise in supply temperature, or that a +curtailment is announced 30 minutes ahead, that is a modeling choice made so +that later experiments can ask what a forecaster could and could not catch. +Nothing here claims real facilities behave this way, and the strengths and +lead times carry no empirical calibration. + +Event types +----------- +``thermal_throttle`` + Emergent. It is never scheduled: it appears in the simulator's event log + when a GPU temperature crosses the throttle threshold of the thermal model + (:class:`gpu_stack.research.heatwave_thermal.GPUThrottleModel`). +``cooling_failure`` + Cooling capacity of one rack or a whole site drops to a fraction for a + window. Optional resistance multiplier models a failing fan. Optional + precursors: a slow rise of the supply temperature and/or a rise of the + thermal resistance, each ramping linearly from zero at ``onset - lead`` to + the stated strength at onset. +``breaker_trip`` + A rack breaker with an inverse-time thermal characteristic. Mode + ``derate`` lowers the breaker rating to a fraction for a window; the trip + itself is emergent and happens when the rack load stays above the rating + long enough (see :class:`InverseTimeBreaker`). The rating ramps down over + the precursor lead time, which makes the rack load ratio rise visibly before + the rating reaches its final value. Mode ``forced`` trips the rack at onset + (a nuisance trip) with no precursor. +``grid_curtailment`` + A site's import limit drops to a fraction for a window. It may be + announced ahead of time (it then appears in the known-future schedule from + ``onset - announce_lead_s``) or not announced. +``hardware_fault`` + One GPU stops at onset and stays down for ``repair_s``. Optional + precursor: the GPU's temperature creeps up by a stated number of kelvin + over the lead time before onset. + +Events with no precursor exist in every type except the emergent throttle, so +experiments can separate what telemetry can foreshadow from what it cannot. +:meth:`EventSchedule.ground_truth` lists, for each event, whether it has a +precursor, which telemetry channel carries it, its lead time and strength. +Controllers and forecasters never see that table. + +Breaker curve +------------- +:class:`InverseTimeBreaker` is a first-order thermal element. Its state +``theta`` follows ``d theta / dt = (r**2 - theta) / tau`` with ``r`` the load +divided by the breaker rating, and the breaker opens when ``theta`` reaches +``nontrip_ratio ** 2``. The defaults are the IEC 60898-1 conventional values +(non-tripping current 1.13 x In, tripping current 1.45 x In, conventional time +1 hour; source: https://www.se.com/au/en/faqs/FA369327/). ``tau`` is +derived so that a constant 1.45 x In opens the breaker after exactly the +conventional time. This models only the thermal region; the instantaneous +magnetic region is not represented, and IEC 60898-1 covers small +installation breakers, so the numbers are a parameterized stand-in for a rack +feed breaker, not a specification of one. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass, field +from typing import Any, Mapping, Optional, Sequence, Tuple + +import numpy as np + +KIND_THERMAL_THROTTLE = "thermal_throttle" +KIND_COOLING_FAILURE = "cooling_failure" +KIND_BREAKER_TRIP = "breaker_trip" +KIND_GRID_CURTAILMENT = "grid_curtailment" +KIND_HARDWARE_FAULT = "hardware_fault" +EVENT_KINDS = ( + KIND_THERMAL_THROTTLE, + KIND_COOLING_FAILURE, + KIND_BREAKER_TRIP, + KIND_GRID_CURTAILMENT, + KIND_HARDWARE_FAULT, +) + +CHANNEL_SUPPLY_TEMP = "supply_temp_c" +CHANNEL_THERMAL_RESISTANCE = "thermal_resistance" +CHANNEL_GPU_TEMP = "gpu_temp_c" +CHANNEL_BREAKER_LOAD_RATIO = "breaker_load_ratio" +CHANNEL_ANNOUNCEMENT = "known_future_schedule" + + +def _nonempty(value: str, name: str) -> None: + if not isinstance(value, str) or not value.strip(): + raise ValueError(f"{name} must be a non-empty string") + + +def _num(value: float, name: str, *, minimum: float = 0.0, strict: bool = False, maximum: float = math.inf) -> None: + if isinstance(value, bool) or not isinstance(value, (int, float, np.integer, np.floating)): + raise ValueError(f"{name} must be a real number") + v = float(value) + if not math.isfinite(v): + raise ValueError(f"{name} must be finite") + if (strict and not v > minimum) or (not strict and v < minimum): + raise ValueError(f"{name} must be {'>' if strict else '>='} {minimum}") + if v > maximum: + raise ValueError(f"{name} must be <= {maximum}") + + +@dataclass(frozen=True) +class Precursor: + """A ramp on one telemetry channel that ends at the event onset. + + ``strength`` is in the unit of the channel: kelvin for ``supply_temp_c`` + and ``gpu_temp_c``; a fractional rise (0.2 means +20 percent) for + ``thermal_resistance``. Simulation assumption, not a measurement. + """ + + channel: str + lead_s: float + strength: float + + def __post_init__(self) -> None: + if self.channel not in (CHANNEL_SUPPLY_TEMP, CHANNEL_THERMAL_RESISTANCE, CHANNEL_GPU_TEMP): + raise ValueError(f"unsupported precursor channel {self.channel!r}") + _num(self.lead_s, "lead_s", strict=True) + _num(self.strength, "strength", strict=True) + + def value(self, t_s: Any, onset_s: float) -> np.ndarray: + """Ramp value at time(s) ``t_s``: 0 before onset - lead, strength at onset.""" + t = np.asarray(t_s, dtype=float) + frac = np.clip((t - (onset_s - self.lead_s)) / self.lead_s, 0.0, 1.0) + return self.strength * frac + + def to_dict(self) -> dict[str, Any]: + return {"channel": self.channel, "lead_s": self.lead_s, "strength": self.strength} + + +def _precursors(items: Any) -> Tuple[Precursor, ...]: + out = [] + for item in items or (): + out.append(item if isinstance(item, Precursor) else Precursor(**item)) + return tuple(out) + + +@dataclass(frozen=True) +class CoolingFailure: + event_id: str + site_id: str + onset_s: float + duration_s: float + remaining_capacity_fraction: float + rack_id: Optional[str] = None + resistance_multiplier: float = 1.0 + precursors: Tuple[Precursor, ...] = () + kind: str = field(default=KIND_COOLING_FAILURE, init=False) + + def __post_init__(self) -> None: + _nonempty(self.event_id, "event_id") + _nonempty(self.site_id, "site_id") + _num(self.onset_s, "onset_s") + _num(self.duration_s, "duration_s", strict=True) + _num(self.remaining_capacity_fraction, "remaining_capacity_fraction", maximum=1.0) + _num(self.resistance_multiplier, "resistance_multiplier", minimum=1.0) + object.__setattr__(self, "precursors", _precursors(self.precursors)) + for p in self.precursors: + if p.channel == CHANNEL_GPU_TEMP: + raise ValueError("cooling_failure precursors use supply_temp_c or thermal_resistance") + + @property + def end_s(self) -> float: + return self.onset_s + self.duration_s + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind, "event_id": self.event_id, "site_id": self.site_id, "rack_id": self.rack_id, + "onset_s": self.onset_s, "duration_s": self.duration_s, + "remaining_capacity_fraction": self.remaining_capacity_fraction, + "resistance_multiplier": self.resistance_multiplier, + "precursors": [p.to_dict() for p in self.precursors], + } + + +@dataclass(frozen=True) +class BreakerTrip: + """Mode ``derate``: rating factor ramps to ``derate_fraction`` by onset and + holds for ``duration_s``. Mode ``forced``: the rack opens at onset and stays + off for ``duration_s`` (no precursor).""" + + event_id: str + site_id: str + rack_id: str + onset_s: float + duration_s: float + mode: str = "derate" + derate_fraction: float = 1.0 + precursor_lead_s: float = 0.0 + kind: str = field(default=KIND_BREAKER_TRIP, init=False) + + def __post_init__(self) -> None: + _nonempty(self.event_id, "event_id") + _nonempty(self.site_id, "site_id") + _nonempty(self.rack_id, "rack_id") + _num(self.onset_s, "onset_s") + _num(self.duration_s, "duration_s", strict=True) + if self.mode not in ("derate", "forced"): + raise ValueError("mode must be 'derate' or 'forced'") + _num(self.precursor_lead_s, "precursor_lead_s") + if self.mode == "derate": + _num(self.derate_fraction, "derate_fraction", strict=True, maximum=1.0) + elif self.precursor_lead_s != 0.0: + raise ValueError("forced breaker trips have no precursor") + + @property + def end_s(self) -> float: + return self.onset_s + self.duration_s + + def rating_factor(self, t_s: Any) -> np.ndarray: + """Breaker rating factor at time(s) ``t_s`` (derate mode).""" + t = np.asarray(t_s, dtype=float) + if self.precursor_lead_s > 0.0: + frac = np.clip((t - (self.onset_s - self.precursor_lead_s)) / self.precursor_lead_s, 0.0, 1.0) + else: + frac = (t >= self.onset_s).astype(float) + factor = 1.0 - (1.0 - self.derate_fraction) * frac + return np.where(t < self.end_s, factor, 1.0) + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind, "event_id": self.event_id, "site_id": self.site_id, "rack_id": self.rack_id, + "onset_s": self.onset_s, "duration_s": self.duration_s, "mode": self.mode, + "derate_fraction": self.derate_fraction, "precursor_lead_s": self.precursor_lead_s, + } + + +@dataclass(frozen=True) +class GridCurtailment: + event_id: str + site_id: str + onset_s: float + duration_s: float + limit_fraction: float + announce_lead_s: Optional[float] = None + kind: str = field(default=KIND_GRID_CURTAILMENT, init=False) + + def __post_init__(self) -> None: + _nonempty(self.event_id, "event_id") + _nonempty(self.site_id, "site_id") + _num(self.onset_s, "onset_s") + _num(self.duration_s, "duration_s", strict=True) + _num(self.limit_fraction, "limit_fraction", maximum=1.0) + if self.announce_lead_s is not None: + _num(self.announce_lead_s, "announce_lead_s", strict=True) + + @property + def end_s(self) -> float: + return self.onset_s + self.duration_s + + @property + def announced_at_s(self) -> Optional[float]: + return None if self.announce_lead_s is None else self.onset_s - self.announce_lead_s + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind, "event_id": self.event_id, "site_id": self.site_id, + "onset_s": self.onset_s, "duration_s": self.duration_s, + "limit_fraction": self.limit_fraction, "announce_lead_s": self.announce_lead_s, + } + + +@dataclass(frozen=True) +class HardwareFault: + event_id: str + site_id: str + rack_id: str + gpu_slot: int + onset_s: float + repair_s: float + precursors: Tuple[Precursor, ...] = () + kind: str = field(default=KIND_HARDWARE_FAULT, init=False) + + def __post_init__(self) -> None: + _nonempty(self.event_id, "event_id") + _nonempty(self.site_id, "site_id") + _nonempty(self.rack_id, "rack_id") + if isinstance(self.gpu_slot, bool) or not isinstance(self.gpu_slot, (int, np.integer)) or self.gpu_slot < 0: + raise ValueError("gpu_slot must be an integer >= 0") + _num(self.onset_s, "onset_s") + _num(self.repair_s, "repair_s", strict=True) + object.__setattr__(self, "precursors", _precursors(self.precursors)) + for p in self.precursors: + if p.channel != CHANNEL_GPU_TEMP: + raise ValueError("hardware_fault precursors use the gpu_temp_c channel") + + @property + def end_s(self) -> float: + return self.onset_s + self.repair_s + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind, "event_id": self.event_id, "site_id": self.site_id, "rack_id": self.rack_id, + "gpu_slot": int(self.gpu_slot), "onset_s": self.onset_s, "repair_s": self.repair_s, + "precursors": [p.to_dict() for p in self.precursors], + } + + +Event = Any # CoolingFailure | BreakerTrip | GridCurtailment | HardwareFault + + +@dataclass(frozen=True) +class InverseTimeBreaker: + """Inverse-time thermal breaker; see the module docstring for the source.""" + + nontrip_ratio: float = 1.13 + trip_ratio: float = 1.45 + conventional_time_s: float = 3600.0 + + def __post_init__(self) -> None: + _num(self.nontrip_ratio, "nontrip_ratio", minimum=1.0, strict=True) + _num(self.trip_ratio, "trip_ratio", minimum=1.0, strict=True) + _num(self.conventional_time_s, "conventional_time_s", strict=True) + if not self.trip_ratio > self.nontrip_ratio: + raise ValueError("trip_ratio must exceed nontrip_ratio") + + @property + def trip_level(self) -> float: + """Value of ``theta`` at which the breaker opens.""" + return self.nontrip_ratio**2 + + @property + def time_constant_s(self) -> float: + r2 = self.trip_ratio**2 + return self.conventional_time_s / -math.log(1.0 - self.trip_level / r2) + + def trip_time_s(self, ratio: float) -> float: + """Time to open from a cold start at constant ``ratio`` (inf if never).""" + r2 = float(ratio) ** 2 + if r2 <= self.trip_level: + return math.inf + return -self.time_constant_s * math.log(1.0 - self.trip_level / r2) + + def step(self, theta: Any, ratio: Any, dt_s: float) -> Tuple[np.ndarray, np.ndarray]: + """Advance ``theta`` by ``dt_s`` under a constant load ratio. + + Returns ``(theta_next, tripped)``. Exact for constant ratio. + """ + r2 = np.asarray(ratio, dtype=float) ** 2 + th = np.asarray(theta, dtype=float) + nxt = r2 + (th - r2) * math.exp(-dt_s / self.time_constant_s) + return nxt, nxt >= self.trip_level + + def to_dict(self) -> dict[str, float]: + return { + "nontrip_ratio": self.nontrip_ratio, + "trip_ratio": self.trip_ratio, + "conventional_time_s": self.conventional_time_s, + "time_constant_s": self.time_constant_s, + } + + +@dataclass(frozen=True) +class EventSignals: + """Per-block signals resolved from a schedule (values at block start).""" + + block_start_s: np.ndarray # (N,) + capacity_factor: np.ndarray # (N, R) multiplies rack cooling capacity + resistance_multiplier: np.ndarray # (N, R) multiplies thermal resistance + supply_offset_k: np.ndarray # (N, R) added to the supply setpoint (precursor) + breaker_rating_factor: np.ndarray # (N, R) + import_limit_factor: np.ndarray # (N, S) + forced_trips: Tuple[Tuple[int, int, float], ...] # (block, rack, off_s) + hardware_faults: Tuple[Tuple[int, int, int, float], ...] # (block, rack, slot, repair_s) + gpu_temp_precursors: Tuple[Tuple[int, int, float, Precursor, float], ...] # (rack, slot, onset, precursor, end) + + def gpu_temp_offsets(self, t_s: float) -> list[Tuple[int, int, float]]: + """``(rack, slot, offset_k)`` for GPUs with an active temperature precursor at ``t_s``.""" + out = [] + for rack, slot, onset, pre, end in self.gpu_temp_precursors: + if onset - pre.lead_s <= t_s < end: + out.append((rack, slot, float(pre.value(min(t_s, onset), onset)))) + return out + + +@dataclass(frozen=True) +class EventSchedule: + events: Tuple[Event, ...] = () + + def __post_init__(self) -> None: + ids = [e.event_id for e in self.events] + if len(set(ids)) != len(ids): + raise ValueError("event ids must be unique") + object.__setattr__(self, "events", tuple(sorted(self.events, key=lambda e: (e.onset_s, e.event_id)))) + + def to_dict(self) -> dict[str, Any]: + return {"data_kind": "simulated", "events": [e.to_dict() for e in self.events]} + + @classmethod + def from_dicts(cls, items: Sequence[Mapping[str, Any]]) -> "EventSchedule": + events = [] + for raw in items: + item = dict(raw) + kind = item.pop("kind", None) + if "precursors" in item: + item["precursors"] = _precursors(item["precursors"]) + if kind == KIND_COOLING_FAILURE: + events.append(CoolingFailure(**item)) + elif kind == KIND_BREAKER_TRIP: + events.append(BreakerTrip(**item)) + elif kind == KIND_GRID_CURTAILMENT: + events.append(GridCurtailment(**item)) + elif kind == KIND_HARDWARE_FAULT: + events.append(HardwareFault(**item)) + else: + raise ValueError(f"unknown or non-schedulable event kind {kind!r}") + return cls(tuple(events)) + + def of_kind(self, kind: str) -> Tuple[Event, ...]: + return tuple(e for e in self.events if e.kind == kind) + + # -- known-future schedule ------------------------------------------------ + + def announced_curtailments(self, t_s: float) -> list[dict[str, Any]]: + """Curtailments visible at time ``t_s``: announced and not yet over.""" + out = [] + for e in self.of_kind(KIND_GRID_CURTAILMENT): + at = e.announced_at_s + if at is not None and at <= t_s < e.end_s: + out.append( + {"event_id": e.event_id, "site_id": e.site_id, "onset_s": e.onset_s, + "end_s": e.end_s, "limit_fraction": e.limit_fraction, "announced_at_s": at} + ) + return out + + # -- ground truth (never shown to controllers) --------------------------- + + def ground_truth(self) -> list[dict[str, Any]]: + rows = [] + for e in self.events: + row: dict[str, Any] = { + "event_id": e.event_id, "kind": e.kind, "site_id": e.site_id, + "rack_id": getattr(e, "rack_id", None), "onset_s": e.onset_s, "end_s": e.end_s, + "has_precursor": False, "precursor_channels": [], "precursor_lead_s": 0.0, + "precursor_strength": {}, "announced_at_s": None, "data_kind": "simulated", + } + if e.kind in (KIND_COOLING_FAILURE, KIND_HARDWARE_FAULT): + row["has_precursor"] = bool(e.precursors) + row["precursor_channels"] = [p.channel for p in e.precursors] + row["precursor_lead_s"] = max((p.lead_s for p in e.precursors), default=0.0) + row["precursor_strength"] = {p.channel: p.strength for p in e.precursors} + elif e.kind == KIND_BREAKER_TRIP: + has = e.mode == "derate" and e.precursor_lead_s > 0.0 + row["has_precursor"] = has + row["precursor_channels"] = [CHANNEL_BREAKER_LOAD_RATIO] if has else [] + row["precursor_lead_s"] = e.precursor_lead_s if has else 0.0 + row["precursor_strength"] = {CHANNEL_BREAKER_LOAD_RATIO: 1.0 - e.derate_fraction} if has else {} + row["mode"] = e.mode + elif e.kind == KIND_GRID_CURTAILMENT: + has = e.announce_lead_s is not None + row["has_precursor"] = has + row["precursor_channels"] = [CHANNEL_ANNOUNCEMENT] if has else [] + row["precursor_lead_s"] = float(e.announce_lead_s) if has else 0.0 + row["announced_at_s"] = e.announced_at_s + rows.append(row) + return rows + + # -- resolution onto the simulator's block grid ---------------------------- + + def resolve( + self, + block_start_s: np.ndarray, + block_dt_s: float, + site_ids: Sequence[str], + rack_ids: Sequence[str], + rack_site_ids: Sequence[str], + rack_gpu_counts: Sequence[int], + ) -> EventSignals: + t = np.asarray(block_start_s, dtype=float) + n, nr, ns = t.shape[0], len(rack_ids), len(site_ids) + rack_index = {r: i for i, r in enumerate(rack_ids)} + site_index = {s: i for i, s in enumerate(site_ids)} + if len(rack_index) != nr or len(site_index) != ns: + raise ValueError("site and rack ids must be unique") + cap = np.ones((n, nr)) + res = np.ones((n, nr)) + sup = np.zeros((n, nr)) + brk = np.ones((n, nr)) + imp = np.ones((n, ns)) + forced = [] + faults = [] + gpu_pre = [] + + def racks_of(site_id: str, rack_id: Optional[str]) -> list[int]: + if site_id not in site_index: + raise ValueError(f"unknown site {site_id!r}") + if rack_id is None: + return [i for i, s in enumerate(rack_site_ids) if s == site_id] + if rack_id not in rack_index or rack_site_ids[rack_index[rack_id]] != site_id: + raise ValueError(f"unknown rack {rack_id!r} for site {site_id!r}") + return [rack_index[rack_id]] + + def block_of(time_s: float) -> int: + return int(math.ceil(time_s / block_dt_s - 1e-9)) + + for e in self.events: + if e.kind == KIND_COOLING_FAILURE: + active = (t >= e.onset_s) & (t < e.end_s) + for r in racks_of(e.site_id, e.rack_id): + cap[active, r] = np.minimum(cap[active, r], e.remaining_capacity_fraction) + res[active, r] = np.maximum(res[active, r], e.resistance_multiplier) + for p in e.precursors: + window = (t >= e.onset_s - p.lead_s) & (t < e.end_s) + ramp = p.value(np.minimum(t, e.onset_s), e.onset_s) + if p.channel == CHANNEL_SUPPLY_TEMP: + sup[window, r] = np.maximum(sup[window, r], ramp[window]) + else: + res[window, r] = np.maximum(res[window, r], 1.0 + ramp[window]) + elif e.kind == KIND_BREAKER_TRIP: + (r,) = racks_of(e.site_id, e.rack_id) + if e.mode == "derate": + brk[:, r] = np.minimum(brk[:, r], e.rating_factor(t)) + else: + b = block_of(e.onset_s) + if b < n: + forced.append((b, r, e.duration_s)) + elif e.kind == KIND_GRID_CURTAILMENT: + active = (t >= e.onset_s) & (t < e.end_s) + s = site_index[e.site_id] if e.site_id in site_index else None + if s is None: + raise ValueError(f"unknown site {e.site_id!r}") + imp[active, s] = np.minimum(imp[active, s], e.limit_fraction) + elif e.kind == KIND_HARDWARE_FAULT: + (r,) = racks_of(e.site_id, e.rack_id) + if e.gpu_slot >= rack_gpu_counts[r]: + raise ValueError(f"gpu_slot {e.gpu_slot} outside rack {e.rack_id!r}") + b = block_of(e.onset_s) + if b < n: + faults.append((b, r, int(e.gpu_slot), e.repair_s)) + for p in e.precursors: + gpu_pre.append((r, int(e.gpu_slot), e.onset_s, p, e.end_s)) + forced.sort() + faults.sort() + return EventSignals( + block_start_s=t, capacity_factor=cap, resistance_multiplier=res, supply_offset_k=sup, + breaker_rating_factor=brk, import_limit_factor=imp, forced_trips=tuple(forced), + hardware_faults=tuple(faults), gpu_temp_precursors=tuple(gpu_pre), + ) + + +def random_schedule( + seed: int, + horizon_s: float, + site_racks: Mapping[str, Sequence[Tuple[str, int]]], + rates_per_day: Mapping[str, float], + precursor_probability: float, + lead_range_s: Tuple[float, float], + strength_ranges: Mapping[str, Tuple[float, float]], + duration_range_s: Tuple[float, float], + remaining_capacity_range: Tuple[float, float], + derate_range: Tuple[float, float], + limit_fraction_range: Tuple[float, float], + repair_range_s: Tuple[float, float], +) -> EventSchedule: + """Seeded random schedule for experiments that need many events. + + ``site_racks`` maps a site id to ``[(rack_id, gpu_count), ...]``. + ``rates_per_day`` has keys ``cooling_failure``, ``breaker_trip``, + ``grid_curtailment``, ``hardware_fault`` (events per site per day). Each + event gets a precursor with probability ``precursor_probability``. + ``strength_ranges`` has keys ``supply_temp_c`` (K), ``gpu_temp_c`` (K). + Every number is a caller-supplied simulation assumption. + """ + rng = np.random.default_rng(seed) + events: list[Event] = [] + counter = 0 + for site_id in sorted(site_racks): + racks = list(site_racks[site_id]) + for kind in (KIND_COOLING_FAILURE, KIND_BREAKER_TRIP, KIND_GRID_CURTAILMENT, KIND_HARDWARE_FAULT): + count = int(rng.poisson(float(rates_per_day.get(kind, 0.0)) * horizon_s / 86_400.0)) + for _ in range(count): + counter += 1 + eid = f"rnd-{counter:04d}-{kind}" + onset = float(rng.uniform(0.0, horizon_s)) + dur = float(rng.uniform(*duration_range_s)) + has_pre = bool(rng.random() < precursor_probability) + lead = float(rng.uniform(*lead_range_s)) + rack_id, n_gpu = racks[int(rng.integers(len(racks)))] + if kind == KIND_COOLING_FAILURE: + pre = () + if has_pre: + pre = (Precursor(CHANNEL_SUPPLY_TEMP, lead, float(rng.uniform(*strength_ranges["supply_temp_c"]))),) + scope_rack = rack_id if rng.random() < 0.5 else None + events.append(CoolingFailure(eid, site_id, onset, dur, float(rng.uniform(*remaining_capacity_range)), + rack_id=scope_rack, precursors=pre)) + elif kind == KIND_BREAKER_TRIP: + if rng.random() < 0.5: + events.append(BreakerTrip(eid, site_id, rack_id, onset, dur, "forced")) + else: + events.append(BreakerTrip(eid, site_id, rack_id, onset, dur, "derate", + float(rng.uniform(*derate_range)), lead if has_pre else 0.0)) + elif kind == KIND_GRID_CURTAILMENT: + events.append(GridCurtailment(eid, site_id, onset, dur, float(rng.uniform(*limit_fraction_range)), + lead if has_pre else None)) + else: + pre = () + if has_pre: + pre = (Precursor(CHANNEL_GPU_TEMP, lead, float(rng.uniform(*strength_ranges["gpu_temp_c"]))),) + events.append(HardwareFault(eid, site_id, rack_id, int(rng.integers(n_gpu)), onset, + float(rng.uniform(*repair_range_s)), precursors=pre)) + return EventSchedule(tuple(events)) + + +__all__ = [ + "BreakerTrip", + "CoolingFailure", + "EVENT_KINDS", + "Event", + "EventSchedule", + "EventSignals", + "GridCurtailment", + "HardwareFault", + "InverseTimeBreaker", + "Precursor", + "random_schedule", +] diff --git a/gpu_stack/research/heatwave_sim.py b/gpu_stack/research/heatwave_sim.py new file mode 100644 index 0000000..d868425 --- /dev/null +++ b/gpu_stack/research/heatwave_sim.py @@ -0,0 +1,2172 @@ +"""Multi-scale power and thermal cluster simulator for the Heatwave extension (e003). + +Everything this module produces is SIMULATED. Some power levels are derived +from measured laptop-GPU ratios (experiment e002); every other number is a +scenario assumption or a cited constant, and the result object says which is +which (``SimResult.to_arrays()['meta']``). Nothing here is a claim about a +real cluster. + +Hierarchy and clocks +-------------------- +Sites contain racks, racks contain GPUs. Sites reuse +:class:`gpu_stack.research.multisite.Site` for the site power cap, cooling +capacity, and grid import limit. Two clocks run together: + +* the base step ``base_dt_s`` (default 1 s) at which power is rendered, and +* the control step ``control_dt_s`` (default 10 s, a whole multiple of the base + step). Job admission, GPU clock limits, thermal integration, breaker state, + fault onsets, and controller decisions happen on control-step boundaries. + Inside a control step, job state changes (checkpoint start, job end, merge + barrier) are handled at their exact time and rendered at base resolution. + +Per-GPU power waveform +---------------------- +A running data-parallel job alternates compute and communication phases inside +each training step (``compute_fraction_of_step`` of the step is compute), stalls +for ``checkpoint_duration_s`` every ``checkpoint_every_s`` of running time, and +holds its GPUs at the checkpoint level meanwhile. Idle GPUs draw idle power. +The relative levels checkpoint/compute and idle/compute are the medians of the +labeled phase windows in the measured e002 laptop telemetry +(``docs/data/e002-checkpoint-power-raw-v1.json``), computed in code by +:func:`measure_e002_levels`, then scaled to the H100 SXM TDP from +``gpu_stack.presets.hardware`` (700 W, NVIDIA product specification cited +there). What is measured and what is assumed: + +* measured (laptop GPU, ratios only): idle/compute, checkpoint/compute, + restore/compute, merge/compute, relative standard deviation of compute-phase + power; +* cited: H100 SXM TDP (700 W), DGX H100 mass and operating temperature range + (https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html), + IEC 60898-1 breaker curve constants, the e001 WAN/restore parameters; +* assumed (scenario parameters, labeled): fraction of TDP drawn in compute, + communication-phase level, host overhead, thermal resistance and heat + capacity, throttle threshold, cooling model efficiency, job mix, events. + +The laptop telemetry updates about every 0.49 s while its checkpoint-snapshot +windows are about 23 ms long, so the measured checkpoint/compute ratio cannot +resolve a checkpoint dip. The scenario may therefore choose the checkpoint +level from the measured idle ratio; the choice is recorded in the output. + +Throttling and power caps +------------------------- +Each GPU has a clock fraction from the thermal throttle model (GPU temperature += rack air temperature + a per-GPU offset proportional to its power) and from +the rack power cap. A synchronous job runs at the clock of its slowest GPU and +all its GPUs are modeled at that clock (a simplification: waiting GPUs are not +modeled separately). Progress scales with the clock fraction; dynamic power +scales as ``clock ** exponent``. + +Work accounting (e001 compatible) +--------------------------------- +Progress is in steps. A single-site job that faults returns to its last +completed checkpoint, and the steps since are lost. A multi-site job (two +shards, one per site) follows the e001 ``periodic_local`` rule: each shard +trains alone and the shards average exactly every ``average_every_steps`` +steps; a faulted shard returns to the last average; the survivor continues +without the failed shard's quota, and the returning shard adopts the merged +state through an explicit merge (the steps it missed are reported as +``skipped_steps``). Lost work is reported in steps and GPU-hours. + +Known simplifications are listed in ``SimResult.meta['simplifications']``. +""" + +from __future__ import annotations + +import hashlib +import heapq +import json +import math +from dataclasses import asdict, dataclass, field, is_dataclass, replace +from pathlib import Path +from typing import Any, Mapping, Optional, Protocol, Sequence, Tuple, Union, runtime_checkable + +import numpy as np + +from .heatwave_events import ( + BreakerTrip, + EventSchedule, + EventSignals, + InverseTimeBreaker, + KIND_BREAKER_TRIP, + KIND_HARDWARE_FAULT, + KIND_THERMAL_THROTTLE, +) +from .heatwave_thermal import CoolingPowerModel, GPUThrottleModel, rc_step +from .multisite import Site + +KIND_THERMAL_SHUTDOWN = "thermal_shutdown" +SCENARIO_SCHEMA = "gpu-stack.e003-heatwave-sim-scenario.v1" +RESULT_SCHEMA = "gpu-stack.e003-heatwave-sim-result.v1" + +_REPO_ROOT = Path(__file__).resolve().parents[2] +E002_RAW_RELATIVE_PATH = "docs/data/e002-checkpoint-power-raw-v1.json" + +SIMPLIFICATIONS = ( + "Job admission, GPU clocks, thermal integration, breaker state, event onsets and controller " + "decisions are evaluated at control-step boundaries; event onsets take effect at the first " + "boundary at or after the stated time.", + "Thermal integration uses the mean rack power over each control step.", + "A synchronous job runs at the clock of its slowest GPU and all its GPUs are modeled at that clock.", + "Per-GPU power noise is independent across GPUs and seconds, scaled to the measured relative " + "standard deviation of laptop compute-phase power.", + "GPU temperature uses the rack air temperature at the start of the control step and the previous " + "step's mean GPU power.", + "Multi-site jobs take no separate checkpoints; their durable state is the last exact average.", + "A faulted GPU draws 0 W until repaired; its host stays powered unless the rack breaker is open.", +) + + +# --------------------------------------------------------------------------- +# small validators +# --------------------------------------------------------------------------- + + +def _nonempty(value: str, name: str) -> None: + if not isinstance(value, str) or not value.strip(): + raise ValueError(f"{name} must be a non-empty string") + + +def _num(value: float, name: str, *, minimum: float = 0.0, strict: bool = False, maximum: float = math.inf) -> None: + if isinstance(value, bool) or not isinstance(value, (int, float, np.integer, np.floating)): + raise ValueError(f"{name} must be a real number") + v = float(value) + if not math.isfinite(v): + raise ValueError(f"{name} must be finite") + if (strict and not v > minimum) or (not strict and v < minimum): + raise ValueError(f"{name} must be {'>' if strict else '>='} {minimum}") + if v > maximum: + raise ValueError(f"{name} must be <= {maximum}") + + +# --------------------------------------------------------------------------- +# Measured e002 levels +# --------------------------------------------------------------------------- + + +def measure_e002_levels(path: Union[str, Path, None] = None) -> dict[str, Any]: + """Medians per labeled phase of the measured e002 laptop GPU-board power. + + Returns raw medians in watts (laptop), ratios to the compute median, the + relative standard deviation of compute-phase samples, sample counts, and + the telemetry update period. Idle is the board power before and after the + active window of each run. ``data_kind`` of this table is measured. + """ + raw_path = Path(path) if path is not None else _REPO_ROOT / E002_RAW_RELATIVE_PATH + if not raw_path.exists(): + raise FileNotFoundError(f"measured e002 telemetry not found at {raw_path}") + data = json.loads(raw_path.read_text()) + samples: dict[str, list[np.ndarray]] = {} + rel_std = [] + window_durations: dict[str, list[float]] = {} + for run in data["runs"]: + trace = run["telemetry_trace"] + if not trace.get("available"): + continue + ts = np.array([p["timestamp"] for p in trace["points"]], dtype=float) + pw = np.array([p["gpu_board_power_w"] for p in trace["points"]], dtype=float) + for w in trace["phase_windows"]: + m = (ts >= w["start_seconds"]) & (ts < w["end_seconds"]) + samples.setdefault(w["phase"], []).append(pw[m]) + window_durations.setdefault(w["phase"], []).append(w["end_seconds"] - w["start_seconds"]) + a, b = trace["active_start_seconds"], trace["active_end_seconds"] + samples.setdefault("idle-outside-active-window", []).append(pw[(ts < a) | (ts > b)]) + comp = np.concatenate( + [pw[(ts >= w["start_seconds"]) & (ts < w["end_seconds"])] + for w in trace["phase_windows"] if w["phase"] == "canonical-healthy-compute"] + or [np.array([])] + ) + if comp.size > 8 and float(np.mean(comp)) > 0: + rel_std.append(float(np.std(comp) / np.mean(comp))) + pooled = {k: np.concatenate(v) for k, v in samples.items() if sum(x.size for x in v) > 0} + medians = {k: float(np.median(v)) for k, v in pooled.items()} + counts = {k: int(v.size) for k, v in pooled.items()} + compute = medians["canonical-healthy-compute"] + ratios = {k: medians[k] / compute for k in medians} + return { + "data_kind": "measured", + "source_file": str(raw_path), + "source_sha256": hashlib.sha256(raw_path.read_bytes()).hexdigest(), + "boundary": data.get("boundary"), + "median_power_w_laptop": medians, + "ratio_to_compute": ratios, + "sample_counts": counts, + "compute_relative_std": float(np.median(rel_std)) if rel_std else 0.0, + "median_window_duration_s": {k: float(np.median(v)) for k, v in window_durations.items()}, + "telemetry_update_period_s": float(data["logger_calibration"]["effective_update_period_seconds"]), + } + + +@dataclass(frozen=True) +class PowerLevels: + """Per-GPU power levels in watts for the phases of the waveform.""" + + compute_w: float + comm_w: float + checkpoint_w: float + idle_w: float + host_w_per_gpu: float + noise_rel_sigma: float + provenance: Mapping[str, str] = field(default_factory=dict) + + def __post_init__(self) -> None: + for name in ("compute_w", "comm_w", "checkpoint_w", "idle_w", "host_w_per_gpu", "noise_rel_sigma"): + _num(getattr(self, name), name, strict=name == "compute_w") + if self.idle_w > self.compute_w: + raise ValueError("idle_w must not exceed compute_w") + + @classmethod + def from_e002( + cls, + measured: Mapping[str, Any], + tdp_w: float, + compute_fraction_of_tdp: float, + comm_ratio: Union[str, float] = "idle_ratio", + checkpoint_ratio: Union[str, float] = "measured", + host_fraction_of_compute: float = 0.0, + idle_ratio_override: Optional[float] = None, + noise_scale: float = 1.0, + ) -> "PowerLevels": + """Scale measured laptop ratios to a datacenter GPU. + + ``comm_ratio`` and ``checkpoint_ratio`` are ``'measured'`` (the + measured checkpoint ratio; not available for communication), + ``'idle_ratio'`` (use the measured idle ratio), or a number. + ``idle_ratio_override`` replaces the measured idle ratio when set (the + caller then owns the citation). + """ + ratios = measured["ratio_to_compute"] + compute = float(tdp_w) * float(compute_fraction_of_tdp) + idle_ratio = float(ratios["idle-outside-active-window"]) if idle_ratio_override is None else float(idle_ratio_override) + prov = { + "compute_w": f"assumed: {compute_fraction_of_tdp} x cited TDP {tdp_w} W", + "idle_w": ( + f"measured laptop idle/compute ratio {idle_ratio:.4f} x compute_w" + if idle_ratio_override is None + else f"override ratio {idle_ratio:.4f} x compute_w (caller-supplied)" + ), + } + + def resolve(spec: Union[str, float], what: str) -> float: + if spec == "idle_ratio": + prov[what] = "assumed level equal to the idle ratio (structural assumption)" + return idle_ratio + if spec == "measured": + if what == "comm_w": + raise ValueError("no measured communication-phase level exists in e002") + prov[what] = "measured laptop checkpoint-snapshot/compute ratio" + return float(ratios["checkpoint-snapshot"]) + _num(spec, f"{what} ratio", strict=True) # type: ignore[arg-type] + prov[what] = f"assumed ratio {float(spec)}" # type: ignore[arg-type] + return float(spec) # type: ignore[arg-type] + + comm = resolve(comm_ratio, "comm_w") + ckpt = resolve(checkpoint_ratio, "checkpoint_w") + prov["host_w_per_gpu"] = f"assumed: {host_fraction_of_compute} x compute_w" + prov["noise_rel_sigma"] = f"measured laptop relative std of compute-phase board power x scale {noise_scale}" + return cls( + compute_w=compute, + comm_w=compute * comm, + checkpoint_w=compute * ckpt, + idle_w=compute * idle_ratio, + host_w_per_gpu=compute * float(host_fraction_of_compute), + noise_rel_sigma=float(measured["compute_relative_std"]) * float(noise_scale), + provenance=prov, + ) + + def to_dict(self) -> dict[str, Any]: + return { + "compute_w": self.compute_w, "comm_w": self.comm_w, "checkpoint_w": self.checkpoint_w, + "idle_w": self.idle_w, "host_w_per_gpu": self.host_w_per_gpu, + "noise_rel_sigma": self.noise_rel_sigma, "provenance": dict(self.provenance), + } + + +# --------------------------------------------------------------------------- +# Jobs +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class Job: + """One training job. ``duration_s`` is the productive time at full clock.""" + + job_id: str + n_gpus: int + submit_s: float + start_s: float + duration_s: float + step_time_s: float + checkpoint_every_s: float + checkpoint_duration_s: float + value_per_gpu_hour: float + flexible: bool + site_id: Optional[str] = None + multisite: bool = False + average_every_steps: int = 8 + + def __post_init__(self) -> None: + _nonempty(self.job_id, "job_id") + if isinstance(self.n_gpus, bool) or not isinstance(self.n_gpus, (int, np.integer)) or self.n_gpus < 1: + raise ValueError("n_gpus must be an integer >= 1") + _num(self.submit_s, "submit_s") + _num(self.start_s, "start_s") + if self.start_s < self.submit_s: + raise ValueError("start_s must be >= submit_s") + _num(self.duration_s, "duration_s", strict=True) + _num(self.step_time_s, "step_time_s", strict=True) + _num(self.checkpoint_every_s, "checkpoint_every_s", strict=True) + _num(self.checkpoint_duration_s, "checkpoint_duration_s") + _num(self.value_per_gpu_hour, "value_per_gpu_hour") + if not isinstance(self.flexible, (bool, np.bool_)): + raise ValueError("flexible must be a bool") + if self.multisite and self.n_gpus % 2: + raise ValueError("multisite jobs need an even n_gpus") + if isinstance(self.average_every_steps, bool) or int(self.average_every_steps) < 1: + raise ValueError("average_every_steps must be an integer >= 1") + + @property + def total_steps(self) -> float: + return self.duration_s / self.step_time_s + + def to_dict(self) -> dict[str, Any]: + return { + "job_id": self.job_id, "n_gpus": int(self.n_gpus), "submit_s": self.submit_s, "start_s": self.start_s, + "duration_s": self.duration_s, "step_time_s": self.step_time_s, + "checkpoint_every_s": self.checkpoint_every_s, "checkpoint_duration_s": self.checkpoint_duration_s, + "value_per_gpu_hour": self.value_per_gpu_hour, "flexible": bool(self.flexible), + "site_id": self.site_id, "multisite": bool(self.multisite), + "average_every_steps": int(self.average_every_steps), + } + + @classmethod + def from_dict(cls, data: Mapping[str, Any]) -> "Job": + return cls(**dict(data)) + + +def generate_jobs( + seed: int, + horizon_s: float, + total_gpus: int, + utilization_goal: float, + gpu_choices: Sequence[int], + gpu_weights: Sequence[float], + duration_range_s: Tuple[float, float], + step_time_choices_s: Sequence[float], + checkpoint_every_range_s: Tuple[float, float], + checkpoint_duration_range_s: Tuple[float, float], + value_range: Tuple[float, float], + flexible_fraction: float, + multisite_fraction: float, + max_start_delay_s: float, + initial_fraction: float = 0.5, + site_ids: Sequence[str] = (), +) -> list[Job]: + """Seeded synthetic job schedule (simulated, no real trace behind it). + + Jobs are added until their total GPU-seconds reach ``utilization_goal`` + times the cluster capacity over the horizon. A fraction ``initial_fraction`` + of the GPU-seconds is submitted at time 0. Durations are log-uniform. + """ + rng = np.random.default_rng([int(seed), 101]) + weights = np.asarray(gpu_weights, dtype=float) + weights = weights / weights.sum() + goal = utilization_goal * total_gpus * horizon_s + jobs: list[Job] = [] + acc = 0.0 + k = 0 + lo, hi = math.log(duration_range_s[0]), math.log(duration_range_s[1]) + while acc < goal: + n = int(rng.choice(np.asarray(gpu_choices), p=weights)) + multisite = bool(rng.random() < multisite_fraction) and n % 2 == 0 and n >= 2 + dur = float(math.exp(rng.uniform(lo, hi))) + step = float(rng.choice(np.asarray(step_time_choices_s))) + dur = max(step, round(dur / step) * step) + initial = acc < initial_fraction * goal + submit = 0.0 if initial else float(rng.uniform(0.0, 0.9 * horizon_s)) + start = submit + float(rng.uniform(0.0, max_start_delay_s)) if not initial else 0.0 + site = None + if site_ids and not multisite: + site = str(site_ids[int(rng.integers(len(site_ids)))]) if rng.random() < 0.5 else None + k += 1 + jobs.append( + Job( + job_id=f"job-{k:04d}", n_gpus=n, submit_s=round(submit, 3), start_s=round(start, 3), + duration_s=dur, step_time_s=step, + checkpoint_every_s=float(rng.uniform(*checkpoint_every_range_s)), + checkpoint_duration_s=float(rng.uniform(*checkpoint_duration_range_s)), + value_per_gpu_hour=round(float(rng.uniform(*value_range)), 4), + flexible=bool(rng.random() < flexible_fraction), site_id=site, multisite=multisite, + ) + ) + acc += n * min(dur, horizon_s) + jobs.sort(key=lambda j: (j.start_s, j.job_id)) + return jobs + + +# --------------------------------------------------------------------------- +# Environment (outdoor temperature, price, carbon) +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class SiteEnvironment: + """Hourly external series for one site, aligned to the sim clock. + + Index ``i`` is the value at ``i * step_s`` seconds after the sim start. + The series need ``horizon_s / step_s + 1`` values (the last one is the + end-of-horizon value used for interpolation). ``outdoor_forecast_c`` is + what the controller may see of the future; it defaults to the actual + series when not given. + """ + + outdoor_c: np.ndarray + price_usd_per_kwh: np.ndarray + carbon_g_per_kwh: np.ndarray + outdoor_forecast_c: Optional[np.ndarray] = None + step_s: float = 3600.0 + + def __post_init__(self) -> None: + for name in ("outdoor_c", "price_usd_per_kwh", "carbon_g_per_kwh"): + arr = np.asarray(getattr(self, name), dtype=float) + if arr.ndim != 1 or arr.size < 2 or not np.all(np.isfinite(arr)): + raise ValueError(f"{name} must be a finite 1-D array with at least 2 values") + object.__setattr__(self, name, arr) + n = self.outdoor_c.size + if self.price_usd_per_kwh.size != n or self.carbon_g_per_kwh.size != n: + raise ValueError("environment series must have equal length") + if self.outdoor_forecast_c is not None: + f = np.asarray(self.outdoor_forecast_c, dtype=float) + if f.shape != self.outdoor_c.shape: + raise ValueError("outdoor_forecast_c must match outdoor_c in shape") + object.__setattr__(self, "outdoor_forecast_c", f) + _num(self.step_s, "step_s", strict=True) + + @property + def forecast_c(self) -> np.ndarray: + return self.outdoor_c if self.outdoor_forecast_c is None else self.outdoor_forecast_c + + +def synthetic_environment(spec: Mapping[str, Any], site_ids: Sequence[str], horizon_s: float, seed: int) -> dict[str, SiteEnvironment]: + """Seeded synthetic diurnal outdoor temperature, price and carbon series (simulated).""" + n = int(round(horizon_s / 3600.0)) + 1 + hours = np.arange(n, dtype=float) + out: dict[str, SiteEnvironment] = {} + for idx, site_id in enumerate(site_ids): + s = spec["sites"][site_id] + rng = np.random.default_rng([int(seed), 202, idx]) + local_h = hours + float(s.get("utc_offset_h", 0.0)) + float(spec.get("start_hour_utc", 0.0)) + o = s["outdoor"] + temp = o["mean_c"] + o["diurnal_amplitude_c"] * np.cos(2 * np.pi * (local_h - o["peak_hour_local"]) / 24.0) + for hw in o.get("heatwaves", []): + temp = temp + hw["add_c"] * np.exp(-0.5 * ((hours - hw["center_h"]) / hw["width_h"]) ** 2) + noise = np.zeros(n) + e = rng.normal(0.0, 1.0, n) + phi = float(o.get("noise_ar1", 0.0)) + for i in range(1, n): + noise[i] = phi * noise[i - 1] + math.sqrt(1 - phi**2) * e[i] + temp = temp + float(o.get("noise_sigma_c", 0.0)) * noise + ferr = np.zeros(n) + fe = rng.normal(0.0, 1.0, n) + for i in range(1, n): + ferr[i] = 0.9 * ferr[i - 1] + math.sqrt(1 - 0.81) * fe[i] + forecast = temp + float(s.get("forecast_error_sigma_c", 0.0)) * ferr + p = s["price"] + price = p["base_usd_per_kwh"] * (1.0 + p["diurnal_amplitude"] * np.cos(2 * np.pi * (local_h - p["peak_hour_local"]) / 24.0)) + c = s["carbon"] + carbon = c["base_g_per_kwh"] * (1.0 + c["diurnal_amplitude"] * np.cos(2 * np.pi * (local_h - c["peak_hour_local"]) / 24.0)) + out[site_id] = SiteEnvironment(temp, price, carbon, forecast) + return out + + +# --------------------------------------------------------------------------- +# Controller interface +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class SetRackPowerCap: + rack_id: str + cap_w: Optional[float] # IT power cap for the rack; None removes the cap + + +@dataclass(frozen=True) +class AdmitJob: + job_id: str + site_id: Optional[str] = None + + +@dataclass(frozen=True) +class DeferJob: + job_id: str + until_s: float + + +@dataclass(frozen=True) +class PauseJob: + job_id: str + + +@dataclass(frozen=True) +class ResumeJob: + job_id: str + + +@dataclass(frozen=True) +class CheckpointNow: + job_id: str + + +@dataclass(frozen=True) +class MigrateJob: + job_id: str + to_site_id: str + + +Action = Any + + +@dataclass(frozen=True) +class JobView: + job_id: str + status: str + site_ids: Tuple[str, ...] + n_gpus: int + progress_steps: float + total_steps: float + steps_since_durable: float + flexible: bool + value_per_gpu_hour: float + submit_s: float + planned_start_s: float + paused: bool + + +@dataclass(frozen=True) +class VisibleState: + """What a controller may see at time ``t_s``: telemetry up to ``t_s`` only, + plus the known-future schedule. Ground-truth events are never included. + + ``now`` holds the latest samples (one value per rack or site). + ``history(channel, window_s)`` returns ``(times_s, values)`` for the last + ``window_s`` seconds, never past ``t_s``. ``known_future`` holds the job + schedule (reservations within the visibility horizon), announced + curtailments, and the external outdoor-forecast, price and carbon series + from ``t_s`` on. + """ + + t_s: float + site_ids: Tuple[str, ...] + rack_ids: Tuple[str, ...] + rack_site_ids: Tuple[str, ...] + now: Mapping[str, np.ndarray] + jobs: Tuple[JobView, ...] + known_future: Mapping[str, Any] + _history: Any = field(repr=False, default=None) + + def history(self, channel: str, window_s: float) -> Tuple[np.ndarray, np.ndarray]: + return self._history(channel, window_s) + + +@runtime_checkable +class Controller(Protocol): + def decide(self, t_s: float, visible_state: VisibleState) -> Sequence[Action]: # pragma: no cover - protocol + ... + + +class NullController: + """Takes no action.""" + + def decide(self, t_s: float, visible_state: VisibleState) -> Sequence[Action]: + return () + + +class ReactiveSiteCapController: + """Baseline: when a site's latest power exceeds its effective limit, cap its racks. + + Uses only visible state. ``margin`` is the fraction of the limit the + controller aims to stay under. It is a deliberately simple reference + policy, not a tuned one. + """ + + def __init__(self, margin: float = 0.97) -> None: + _num(margin, "margin", strict=True, maximum=1.0) + self.margin = float(margin) + self._caps: dict[str, float] = {} + + def decide(self, t_s: float, visible_state: VisibleState) -> Sequence[Action]: + actions: list[Action] = [] + now = visible_state.now + for s, site_id in enumerate(visible_state.site_ids): + limit = min(float(now["site_power_cap_w"][s]), float(now["site_import_limit_w"][s])) + power = float(now["site_power_w"][s]) + racks = [i for i, sid in enumerate(visible_state.rack_site_ids) if sid == site_id] + if power > limit * self.margin: + scale = limit * self.margin / max(power, 1.0) + for i in racks: + rack_power = float(now["rack_it_power_w"][i]) + cap = max(rack_power * scale, 0.0) + self._caps[visible_state.rack_ids[i]] = cap + actions.append(SetRackPowerCap(visible_state.rack_ids[i], cap)) + elif power < limit * (self.margin - 0.1): + for i in racks: + rid = visible_state.rack_ids[i] + if rid in self._caps: + del self._caps[rid] + actions.append(SetRackPowerCap(rid, None)) + return actions + + +# --------------------------------------------------------------------------- +# Layout +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class RackSpec: + rack_id: str + n_gpus: int + breaker_rating_w: float + + def __post_init__(self) -> None: + _nonempty(self.rack_id, "rack_id") + if isinstance(self.n_gpus, bool) or int(self.n_gpus) < 1: + raise ValueError("n_gpus must be an integer >= 1") + _num(self.breaker_rating_w, "breaker_rating_w", strict=True) + + +@dataclass(frozen=True) +class SiteLayout: + site: Site + racks: Tuple[RackSpec, ...] + + def __post_init__(self) -> None: + total = sum(r.n_gpus for r in self.racks) + if total != self.site.accelerator_count: + raise ValueError( + f"site {self.site.site_id!r}: racks hold {total} GPUs but accelerator_count is {self.site.accelerator_count}" + ) + + +@dataclass(frozen=True) +class ThermalAssumptions: + """Rack thermal parameters (all scenario assumptions unless cited in the scenario).""" + + heat_capacity_j_per_k_per_gpu: float + design_air_rise_k: float + cooling_lag_s: float + supply_setpoint_c: float + outdoor_coupling_k_per_k: float + outdoor_reference_c: float + capacity_derate_per_k: float + capacity_derate_reference_c: float + gpu_to_air_k_per_w: float + gpu_to_air_rel_sigma: float + breaker_reset_delay_s: float + gpu_shutdown_c: float + thermal_shutdown_hold_s: float + + def __post_init__(self) -> None: + for name in ( + "heat_capacity_j_per_k_per_gpu", "design_air_rise_k", + ): + _num(getattr(self, name), name, strict=True) + for name in ( + "cooling_lag_s", "capacity_derate_per_k", "gpu_to_air_k_per_w", "gpu_to_air_rel_sigma", + "breaker_reset_delay_s", "thermal_shutdown_hold_s", + ): + _num(getattr(self, name), name) + for name in ("supply_setpoint_c", "outdoor_coupling_k_per_k", "outdoor_reference_c", "capacity_derate_reference_c", "gpu_shutdown_c"): + _num(getattr(self, name), name, minimum=-100.0) + + +@dataclass(frozen=True) +class TransferParams: + """Checkpoint, WAN and restart parameters (from the e001 scenarios).""" + + wan_bandwidth_bytes_per_second: float + wan_latency_s: float + restore_bandwidth_bytes_per_second: float + fixed_restart_latency_s: float + membership_reconfiguration_s: float + checkpoint_bytes_per_gpu: float + + def __post_init__(self) -> None: + for name in ("wan_bandwidth_bytes_per_second", "restore_bandwidth_bytes_per_second", "checkpoint_bytes_per_gpu"): + _num(getattr(self, name), name, strict=True) + for name in ("wan_latency_s", "fixed_restart_latency_s", "membership_reconfiguration_s"): + _num(getattr(self, name), name) + + def transfer_s(self, n_gpus: int) -> float: + return n_gpus * self.checkpoint_bytes_per_gpu / self.wan_bandwidth_bytes_per_second + self.wan_latency_s + self.membership_reconfiguration_s + + def restore_s(self, n_gpus: int) -> float: + return n_gpus * self.checkpoint_bytes_per_gpu / self.restore_bandwidth_bytes_per_second + self.fixed_restart_latency_s + + def merge_stall_s(self, shard_gpus: int) -> float: + return shard_gpus * self.checkpoint_bytes_per_gpu / self.wan_bandwidth_bytes_per_second + self.wan_latency_s + + +@dataclass(frozen=True) +class SimConfig: + scenario_id: str + horizon_s: float + base_dt_s: float + control_dt_s: float + controller_interval_s: float + history_s: float + known_future_horizon_s: float + compute_fraction_of_step: float + start_utc: str + layouts: Tuple[SiteLayout, ...] + levels: PowerLevels + thermal: ThermalAssumptions + throttle: GPUThrottleModel + cooling_power: CoolingPowerModel + breaker: InverseTimeBreaker + transfer: TransferParams + jobs: Tuple[Job, ...] + events: EventSchedule + environment: Mapping[str, SiteEnvironment] + gpu_windows: Union[str, Tuple[Tuple[float, float], ...]] + measured_levels: Mapping[str, Any] + assumptions: Tuple[str, ...] + scenario: Mapping[str, Any] + + def __post_init__(self) -> None: + _nonempty(self.scenario_id, "scenario_id") + for name in ("horizon_s", "base_dt_s", "control_dt_s", "controller_interval_s", "history_s", "known_future_horizon_s"): + _num(getattr(self, name), name, strict=True) + k = self.control_dt_s / self.base_dt_s + if abs(k - round(k)) > 1e-9: + raise ValueError("control_dt_s must be a whole multiple of base_dt_s") + if abs(60.0 / self.control_dt_s - round(60.0 / self.control_dt_s)) > 1e-9: + raise ValueError("control_dt_s must divide 60 s") + ci = self.controller_interval_s / self.control_dt_s + if abs(ci - round(ci)) > 1e-9: + raise ValueError("controller_interval_s must be a whole multiple of control_dt_s") + if abs(self.horizon_s / 3600.0 - round(self.horizon_s / 3600.0)) > 1e-9: + raise ValueError("horizon_s must be a whole number of hours") + _num(self.compute_fraction_of_step, "compute_fraction_of_step", strict=True, maximum=1.0) + ids = [lay.site.site_id for lay in self.layouts] + if len(set(ids)) != len(ids): + raise ValueError("site ids must be unique") + rack_ids = [r.rack_id for lay in self.layouts for r in lay.racks] + if len(set(rack_ids)) != len(rack_ids): + raise ValueError("rack ids must be unique across sites") + for sid in ids: + if sid not in self.environment: + raise ValueError(f"missing environment for site {sid!r}") + job_ids = [j.job_id for j in self.jobs] + if len(set(job_ids)) != len(job_ids): + raise ValueError("job ids must be unique") + n_env = int(round(self.horizon_s / 3600.0)) + 1 + for sid in ids: + env = self.environment[sid] + if env.step_s != 3600.0 or env.outdoor_c.size < n_env: + raise ValueError(f"environment for {sid!r} needs hourly values covering the horizon plus one") + for j in self.jobs: + if j.site_id is not None and j.site_id not in ids: + raise ValueError(f"job {j.job_id!r} names unknown site {j.site_id!r}") + if j.multisite and len(ids) < 2: + raise ValueError("multisite jobs need at least two sites") + + +def _transfer_from_dict(d: Mapping[str, Any]) -> TransferParams: + return TransferParams(**{k: d[k] for k in TransferParams.__dataclass_fields__}) + + +def build_config(scenario: Mapping[str, Any], seed: int = 0, environment: Optional[Mapping[str, SiteEnvironment]] = None) -> SimConfig: + """Turn a scenario dict (see ``experiments/e003-heatwave/sim-scenario-v1.json``) into a config.""" + if scenario.get("schema") != SCENARIO_SCHEMA: + raise ValueError(f"scenario schema must be {SCENARIO_SCHEMA!r}") + if scenario.get("data_kind") != "simulated": + raise ValueError("scenario data_kind must be 'simulated'") + if abs(float(scenario["horizon_s"]) / 3600.0 - round(float(scenario["horizon_s"]) / 3600.0)) > 1e-9: + raise ValueError("horizon_s must be a whole number of hours") + layouts = [] + for item in scenario["sites"]: + site = Site(**item["site"]) + racks = tuple(RackSpec(**r) for r in item["racks"]) + layouts.append(SiteLayout(site, racks)) + site_ids = [lay.site.site_id for lay in layouts] + + gcfg = scenario["gpu"] + preset_name = gcfg["preset"] + if preset_name != "h100_sxm_80gb_gpu": + raise ValueError("only the h100_sxm_80gb_gpu preset is wired in") + from ..presets.hardware import h100_sxm_80gb_gpu + + tdp_w = float(h100_sxm_80gb_gpu.assignments["gpu.tdp"]) + pl = scenario["power_levels"] + measured = measure_e002_levels(_resolve_path(pl.get("e002_raw_path", E002_RAW_RELATIVE_PATH))) + levels = PowerLevels.from_e002( + measured, + tdp_w=tdp_w, + compute_fraction_of_tdp=gcfg["compute_fraction_of_tdp"], + comm_ratio=pl.get("comm_ratio", "idle_ratio"), + checkpoint_ratio=pl.get("checkpoint_ratio", "measured"), + host_fraction_of_compute=gcfg["host_fraction_of_compute"], + idle_ratio_override=pl.get("idle_ratio_override"), + noise_scale=pl.get("noise_scale", 1.0), + ) + th = ThermalAssumptions(**scenario["thermal"]) + throttle = GPUThrottleModel(**scenario["throttle"]) + cp = scenario["cooling_power"] + if cp["kind"] == "pue": + cooling = CoolingPowerModel(kind="pue", pue=cp["pue"]) + else: + cooling = CoolingPowerModel.carnot_from_pue( + pue=cp["pue"], reference_outdoor_c=cp["reference_outdoor_c"], reference_supply_c=cp["reference_supply_c"], + evaporator_approach_k=cp["evaporator_approach_k"], condenser_approach_k=cp["condenser_approach_k"], + cop_min=cp["cop_min"], cop_max=cp["cop_max"], + ) + breaker = InverseTimeBreaker(**scenario.get("breaker", {})) + transfer = _transfer_from_dict(scenario["transfer"]) + + jspec = scenario["jobs"] + if "list" in jspec: + jobs = [Job.from_dict(j) for j in jspec["list"]] + else: + g = dict(jspec["generate"]) + total = sum(lay.site.accelerator_count for lay in layouts) + for key in ("duration_range_s", "checkpoint_every_range_s", "checkpoint_duration_range_s", "value_range"): + g[key] = tuple(g[key]) + jobs = generate_jobs( + seed=g.pop("seed", seed), horizon_s=scenario["horizon_s"], total_gpus=total, site_ids=site_ids, **g + ) + events = EventSchedule.from_dicts(scenario.get("events", [])) + + if environment is None: + espec = scenario["environment"] + if "synthetic" in espec: + environment = synthetic_environment(espec["synthetic"], site_ids, scenario["horizon_s"], espec.get("seed", seed)) + else: + environment = { + sid: SiteEnvironment(**{k: np.asarray(v) if isinstance(v, list) else v for k, v in espec["sites"][sid].items()}) + for sid in site_ids + } + windows = scenario.get("outputs", {}).get("gpu_windows", []) + if windows != "all": + windows = tuple((float(a), float(b)) for a, b in windows) + + return SimConfig( + scenario_id=scenario["scenario_id"], horizon_s=float(scenario["horizon_s"]), base_dt_s=float(scenario["base_dt_s"]), + control_dt_s=float(scenario["control_dt_s"]), controller_interval_s=float(scenario["controller_interval_s"]), + history_s=float(scenario.get("history_s", 7200.0)), + known_future_horizon_s=float(scenario.get("known_future_horizon_s", 86_400.0)), + compute_fraction_of_step=float(gcfg["compute_fraction_of_step"]), start_utc=str(scenario["start_utc"]), + layouts=tuple(layouts), levels=levels, thermal=th, throttle=throttle, cooling_power=cooling, breaker=breaker, + transfer=transfer, jobs=tuple(jobs), events=events, environment=dict(environment), gpu_windows=windows, + measured_levels=measured, assumptions=tuple(scenario.get("assumptions", ())), scenario=scenario, + ) + + +def _resolve_path(p: Union[str, Path]) -> Path: + path = Path(p) + return path if path.is_absolute() else _REPO_ROOT / path + + +# --------------------------------------------------------------------------- +# Planning (job-level, no physics) +# --------------------------------------------------------------------------- + + +class _Placement: + """Free-GPU bookkeeping shared by the planner and the simulator.""" + + def __init__(self, site_of_gpu: np.ndarray, n_sites: int) -> None: + self.site_of_gpu = site_of_gpu + self.n_sites = n_sites + self.site_gpus = [np.flatnonzero(site_of_gpu == s) for s in range(n_sites)] + + def choose_site(self, free: np.ndarray, n_needed: int, hint: Optional[int]) -> Optional[int]: + if hint is not None: + return hint if int(free[self.site_gpus[hint]].sum()) >= n_needed else None + best, best_free = None, -1 + for s in range(self.n_sites): + f = int(free[self.site_gpus[s]].sum()) + if f >= n_needed and f > best_free: + best, best_free = s, f + return best + + def pick(self, free: np.ndarray, site: int, n_needed: int) -> np.ndarray: + cand = self.site_gpus[site] + idx = cand[free[cand]] + return idx[:n_needed] + + +@dataclass(frozen=True) +class PlannedJob: + job_id: str + site_ids: Tuple[str, ...] + start_s: float + end_s: float + gpus_by_rack: Mapping[str, int] + job: Job + + +def _quantize_up(t: float, dt: float) -> float: + return math.ceil(t / dt - 1e-9) * dt + + +def stall_cycle(job: Job, transfer: TransferParams) -> Tuple[float, float, int]: + """``(run_seconds_between_stalls, stall_seconds, n_stalls)`` for a job with no faults. + + Single-site jobs stall to checkpoint; multi-site jobs stall to average + (the shards' state is exchanged over the WAN every ``average_every_steps``). + """ + if job.multisite: + every = job.average_every_steps * job.step_time_s + stall = transfer.merge_stall_s(job.n_gpus // 2) + else: + every, stall = job.checkpoint_every_s, job.checkpoint_duration_s + n = int(math.floor(job.duration_s / every - 1e-12)) + return every, stall, n + + +def nominal_job_span_s(job: Job, transfer: TransferParams) -> float: + """Wall time of a job with no faults, no throttling: run time plus stalls.""" + _, stall, n = stall_cycle(job, transfer) + return job.duration_s + n * stall + + +def plan_schedule(config: SimConfig) -> list[PlannedJob]: + """Job-level schedule if nothing goes wrong: FIFO packing with the simulator's placement rule. + + The simulator's admission step uses the same rule, so with no events and no + throttling the realized schedule equals this plan. Starts are quantized up + to the control step. + """ + site_ids = [lay.site.site_id for lay in config.layouts] + gpu_site, gpu_rack_id = [], [] + for s, lay in enumerate(config.layouts): + for r in lay.racks: + gpu_site += [s] * r.n_gpus + gpu_rack_id += [r.rack_id] * r.n_gpus + place = _Placement(np.array(gpu_site), len(site_ids)) + free = np.ones(len(gpu_site), dtype=bool) + cdt = config.control_dt_s + pending = sorted(config.jobs, key=lambda j: (j.start_s, j.job_id)) + releases: list[Tuple[float, int, np.ndarray]] = [] + plans: list[PlannedJob] = [] + t = 0.0 + counter = 0 + n_blocks = int(round(config.horizon_s / cdt)) + for b in range(n_blocks + 1): + t = b * cdt + while releases and releases[0][0] <= t + 1e-9: + _, _, idx = heapq.heappop(releases) + free[idx] = True + still = [] + for job in pending: + if _quantize_up(job.start_s, cdt) > t + 1e-9: + still.append(job) + continue + hint = site_ids.index(job.site_id) if job.site_id else None + if job.multisite: + halves = job.n_gpus // 2 + if all(int(free[place.site_gpus[s]].sum()) >= halves for s in (0, 1)): + idx = np.concatenate([place.pick(free, s, halves) for s in (0, 1)]) + sites = (site_ids[0], site_ids[1]) + else: + still.append(job) + continue + else: + s = place.choose_site(free, job.n_gpus, hint) + if s is None: + still.append(job) + continue + idx = place.pick(free, s, job.n_gpus) + sites = (site_ids[s],) + free[idx] = False + end = t + nominal_job_span_s(job, config.transfer) + counter += 1 + heapq.heappush(releases, (end, counter, idx)) + by_rack: dict[str, int] = {} + for g in idx: + by_rack[gpu_rack_id[g]] = by_rack.get(gpu_rack_id[g], 0) + 1 + plans.append(PlannedJob(job.job_id, sites, t, end, by_rack, job)) + pending = still + if not pending and not releases: + break + return plans + + +def _G(x: np.ndarray, c: float) -> np.ndarray: + """Cumulative time in compute (in step units) after ``x`` completed steps.""" + fl = np.floor(x) + return fl * c + np.minimum(x - fl, c) + + +def planned_covariates( + config: SimConfig, + plans: Sequence[PlannedJob], + resolution_s: float, + level: str, + t0_s: float = 0.0, + t1_s: Optional[float] = None, +) -> dict[str, np.ndarray]: + """Known-future covariates from the plan, binned to ``resolution_s``. + + ``level`` is ``'rack'`` or ``'site'``. Keys (shape ``(T, units)``): + + ``planned_gpus_running`` GPUs planned to be running, bin average; + ``scheduled_checkpoint_gpus`` GPUs planned to be in a checkpoint, bin average; + ``step_phase_compute_fraction`` bin average of the planned compute indicator + over planned running GPUs (0 when none are planned); + ``outdoor_forecast_c`` / ``price_usd_per_kwh`` / ``carbon_g_per_kwh`` + pass-through of the external series (mean over the bin; site series are + mapped to racks). + + The plan assumes no faults and no throttling, so the phase drifts from the + realized phase when the real run is slowed. + """ + t1 = config.horizon_s if t1_s is None else t1_s + nbins = int(round((t1 - t0_s) / resolution_s)) + edges = t0_s + np.arange(nbins + 1) * resolution_s + site_ids = [lay.site.site_id for lay in config.layouts] + rack_ids = [r.rack_id for lay in config.layouts for r in lay.racks] + rack_site = [lay.site.site_id for lay in config.layouts for _ in lay.racks] + units = rack_ids if level == "rack" else site_ids + unit_index = {u: i for i, u in enumerate(units)} + c = config.compute_fraction_of_step + gpus = np.zeros((nbins, len(units))) + ckpt = np.zeros((nbins, len(units))) + comp = np.zeros((nbins, len(units))) + for pj in plans: + job = pj.job + lo, hi = max(pj.start_s, t0_s), min(pj.end_s, t1) + if hi <= lo: + continue + # Running (not stalled) time r(t) and checkpoint-stall time, cumulative, at bin edges. + e = np.clip(edges, lo, hi) - pj.start_s + every, dur, _n = stall_cycle(job, config.transfer) + cyc = every + dur + full = np.floor(e / cyc) + u = e - full * cyc + run = full * every + np.minimum(u, every) + stall = full * dur + np.maximum(u - every, 0.0) + in_job = np.diff(e) + comp_time = np.diff(_G(run / job.step_time_s, c)) * job.step_time_s + stall_time = np.diff(stall) + share = {} + for rack_id, n in pj.gpus_by_rack.items(): + key = rack_id if level == "rack" else rack_site[rack_ids.index(rack_id)] + share[key] = share.get(key, 0) + n + for key, n in share.items(): + col = unit_index[key] + gpus[:, col] += n * in_job / resolution_s + ckpt[:, col] += n * stall_time / resolution_s + comp[:, col] += n * comp_time / resolution_s + with np.errstate(divide="ignore", invalid="ignore"): + phase = np.where(gpus > 0, comp / gpus, 0.0) + out = { + "planned_gpus_running": gpus, + "scheduled_checkpoint_gpus": ckpt, + "step_phase_compute_fraction": phase, + } + # External series pass-through, mean over each bin (hourly series, linear interpolation). + sub = max(1, int(round(resolution_s / 60.0))) if resolution_s >= 60 else 1 + mids = (edges[:-1, None] + (np.arange(sub)[None, :] + 0.5) * resolution_s / sub) + hours = np.arange(config.environment[site_ids[0]].outdoor_c.size) * 3600.0 + for name, attr in (("outdoor_forecast_c", "forecast_c"), ("price_usd_per_kwh", "price_usd_per_kwh"), ("carbon_g_per_kwh", "carbon_g_per_kwh")): + cols = [] + for u in units: + sid = u if level == "site" else rack_site[rack_ids.index(u)] + series = getattr(config.environment[sid], attr) + if name == "outdoor_forecast_c": + cols.append(np.interp(mids, hours, series).mean(axis=1)) + else: # price and carbon are piecewise constant per hour + idx = np.clip((mids // 3600.0).astype(int), 0, series.size - 1) + cols.append(series[idx].mean(axis=1)) + out[name] = np.stack(cols, axis=1) + out["bin_start_s"] = edges[:-1] + return out + + +# --------------------------------------------------------------------------- +# Runtime +# --------------------------------------------------------------------------- + + +class _Shard: + __slots__ = ( + "sid", "job", "job_index", "part", "n_gpus", "site", "gpus", "state", "left_s", "progress", "durable", + "since_ckpt_s", "snapshot", "total", "paused", "ckpt_now", "migrate_to", "earliest_s", "forced_site", + "needs_restore", "cursor", "useful_steps", "lost_steps", "skipped_steps", "faults", "restarts", + "migrations", "run_s", "fault_merged", "arrive_s", + ) + + def __init__(self, sid: int, job: Job, job_index: int, part: int, n_gpus: int) -> None: + self.sid = sid + self.job = job + self.job_index = job_index + self.part = part + self.n_gpus = n_gpus + self.site: Optional[int] = None + self.gpus = np.zeros(0, dtype=int) + self.state = "queued" + self.left_s = 0.0 + self.progress = 0.0 + self.durable = 0.0 + self.since_ckpt_s = 0.0 + self.snapshot = 0.0 + self.total = job.total_steps + self.paused = False + self.ckpt_now = False + self.migrate_to: Optional[int] = None + self.earliest_s = job.start_s + self.forced_site: Optional[int] = None + self.needs_restore = False + self.cursor = 0.0 + self.useful_steps = 0.0 + self.lost_steps = 0.0 + self.skipped_steps = 0.0 + self.faults = 0 + self.restarts = 0 + self.migrations = 0 + self.run_s = 0.0 + self.fault_merged = 0.0 + self.arrive_s = 0.0 + + +_LIVE_BARRIER = ("running", "ckpt", "wait", "merge") + + +class _Run: + """One simulation run. Holds all mutable state; nothing is global.""" + + def __init__(self, cfg: SimConfig, controller: Optional[Controller], seed: int) -> None: + self.cfg = cfg + self.controller = controller + self.seed = int(seed) + self._build_layout() + self._build_jobs() + self._build_environment() + self._allocate_outputs() + + # -- setup --------------------------------------------------------------- + + def _build_layout(self) -> None: + cfg = self.cfg + self.site_ids = [lay.site.site_id for lay in cfg.layouts] + self.S = len(self.site_ids) + rack_ids, rack_site, rack_n, rating = [], [], [], [] + gpu_site, gpu_rack, gpu_slot = [], [], [] + for s, lay in enumerate(cfg.layouts): + for r in lay.racks: + ri = len(rack_ids) + rack_ids.append(r.rack_id) + rack_site.append(s) + rack_n.append(r.n_gpus) + rating.append(r.breaker_rating_w) + gpu_site += [s] * r.n_gpus + gpu_rack += [ri] * r.n_gpus + gpu_slot += list(range(r.n_gpus)) + self.rack_ids = rack_ids + self.R = len(rack_ids) + self.rack_site = np.array(rack_site) + self.rack_site_ids = [self.site_ids[s] for s in rack_site] + self.rack_n = np.array(rack_n) + self.rack_start = np.concatenate([[0], np.cumsum(self.rack_n)[:-1]]) + self.G = int(self.rack_n.sum()) + self.gpu_site = np.array(gpu_site) + self.gpu_rack = np.array(gpu_rack) + self.gpu_slot = np.array(gpu_slot) + self.rating = np.array(rating, dtype=float) + self.place = _Placement(self.gpu_site, self.S) + self.site_membership = np.zeros((self.R, self.S)) + self.site_membership[np.arange(self.R), self.rack_site] = 1.0 + th = cfg.thermal + lv = cfg.levels + self.C = self.rack_n * th.heat_capacity_j_per_k_per_gpu + design_p = self.rack_n * (lv.compute_w + lv.host_w_per_gpu) + self.Rres = th.design_air_rise_k / design_p + site_gpus = np.array([lay.site.accelerator_count for lay in cfg.layouts], dtype=float) + site_cool = np.array([float(lay.site.cooling_capacity_w) for lay in cfg.layouts]) + self.cap_nominal = site_cool[self.rack_site] * self.rack_n / site_gpus[self.rack_site] + rng = np.random.default_rng([self.seed, 303]) + self.k_gpu = th.gpu_to_air_k_per_w * np.clip(1.0 + th.gpu_to_air_rel_sigma * rng.normal(size=self.G), 0.2, None) + self.site_cap = np.array([float(lay.site.power_cap_w) for lay in cfg.layouts]) + self.site_import = np.array([float(lay.site.grid_import_limit_w) for lay in cfg.layouts]) + self.site_base = np.array([float(lay.site.base_power_w) for lay in cfg.layouts]) + self.cdt = cfg.control_dt_s + self.dt = cfg.base_dt_s + self.K = int(round(self.cdt / self.dt)) + self.Nb = int(round(cfg.horizon_s / self.cdt)) + self.N = self.Nb * self.K + times = np.arange(self.Nb) * self.cdt + self.block_start = times + self.signals: EventSignals = cfg.events.resolve(times, self.cdt, self.site_ids, self.rack_ids, self.rack_site_ids, list(self.rack_n)) + if cfg.gpu_windows == "all": + self.windows: list[Tuple[float, float]] = [(0.0, cfg.horizon_s)] + else: + self.windows = [tuple(w) for w in cfg.gpu_windows] # type: ignore[misc] + for a, b in self.windows: + for v in (a, b): + if abs(v / self.cdt - round(v / self.cdt)) > 1e-9: + raise ValueError("gpu window bounds must be multiples of control_dt_s") + if not 0.0 <= a < b <= cfg.horizon_s: + raise ValueError("gpu window must lie inside the horizon") + est = sum((b - a) / self.dt * self.G * 8 for a, b in self.windows) + if est > 2.0e9: + raise ValueError("requested per-GPU windows exceed 2 GB; request shorter windows") + + def _build_jobs(self) -> None: + cfg = self.cfg + self.jobs = list(cfg.jobs) + self.shards: list[_Shard] = [] + self.job_shards: list[list[_Shard]] = [] + self.job_index = {j.job_id: i for i, j in enumerate(self.jobs)} + self.merged_progress = [0.0] * len(self.jobs) + for ji, job in enumerate(self.jobs): + parts = 2 if job.multisite else 1 + group = [] + for p in range(parts): + sh = _Shard(len(self.shards), job, ji, p, job.n_gpus // parts) + sh.forced_site = None + self.shards.append(sh) + group.append(sh) + self.job_shards.append(group) + self.n_shards = len(self.shards) + self.gpu_shard = np.full(self.G, self.n_shards, dtype=int) # row n_shards means no job + self.gpu_alive = np.ones(self.G, dtype=bool) + self.gpu_down_until = np.zeros(self.G) + self.rack_on = np.ones(self.R, dtype=bool) + self.rack_off_until = np.zeros(self.R) + self.pending_order = sorted(range(self.n_shards), key=lambda i: (self.shards[i].job.start_s, self.shards[i].job.job_id, self.shards[i].part)) + self.pending: list[int] = list(self.pending_order) + self.admit_dirty = True + self.next_eligible_s = 0.0 + self.action_log: list[dict[str, Any]] = [] + self.event_log: list[dict[str, Any]] = [] + self.rack_cap_w = np.full(self.R, np.inf) + self.merge_stall = {} + for sh in self.shards: + if sh.job.multisite: + self.merge_stall[sh.sid] = self.cfg.transfer.merge_stall_s(sh.n_gpus) + + def _build_environment(self) -> None: + cfg = self.cfg + mids = (np.arange(self.Nb) + 0.5) * self.cdt + hours = np.arange(cfg.environment[self.site_ids[0]].outdoor_c.size) * 3600.0 + self.outdoor_b = np.stack([np.interp(mids, hours, cfg.environment[s].outdoor_c) for s in self.site_ids], axis=1) + H = int(round(cfg.horizon_s / 3600.0)) + self.price_h = np.stack([cfg.environment[s].price_usd_per_kwh[:H] for s in self.site_ids], axis=1) + self.carbon_h = np.stack([cfg.environment[s].carbon_g_per_kwh[:H] for s in self.site_ids], axis=1) + + def _allocate_outputs(self) -> None: + N, Nb, R, S = self.N, self.Nb, self.R, self.S + self.rack_gpu_w = np.zeros((N, R)) + self.rack_host_b = np.zeros((Nb, R)) + self.rack_cool_b = np.zeros((Nb, R)) + self.rack_air_b = np.zeros((Nb, R)) + self.rack_supply_b = np.zeros((Nb, R)) + self.rack_maxgpu_b = np.zeros((Nb, R)) + self.rack_throttled_b = np.zeros((Nb, R)) + self.rack_ratio_b = np.zeros((Nb, R)) + self.rack_removed_b = np.zeros((Nb, R)) + self.rack_cap_b = np.zeros((Nb, R)) + self.rack_on_b = np.zeros((Nb, R), dtype=bool) + self.site_free_b = np.zeros((Nb, S)) + self.window_data: list[np.ndarray] = [np.zeros((self.G, int(round((b - a) / self.dt)))) for a, b in self.windows] + self.gpu_mean_prev = np.full(self.G, self.cfg.levels.idle_w) + self.noise_cache: dict[int, np.ndarray] = {} + + # -- helpers ------------------------------------------------------------- + + def _noise(self, block: int) -> np.ndarray: + per_chunk = int(round(3600.0 / self.cdt)) + chunk = block // per_chunk + if chunk not in self.noise_cache: + self.noise_cache = {} + rng = np.random.default_rng([self.seed, 404, chunk]) + self.noise_cache[chunk] = rng.standard_normal((self.G, int(round(3600.0 / self.dt)))).astype(np.float32) + arr = self.noise_cache[chunk] + j = (block % per_chunk) * self.K + return arr[:, j:j + self.K] + + def _capacity_derate(self, outdoor_c: np.ndarray) -> np.ndarray: + """Cooling capacity factor from outdoor temperature (never below zero).""" + th = self.cfg.thermal + return np.clip(1.0 - th.capacity_derate_per_k * np.maximum(0.0, outdoor_c - th.capacity_derate_reference_c), 0.0, 1.0) + + def _free_mask(self) -> np.ndarray: + return self.gpu_alive & self.rack_on[self.gpu_rack] & (self.gpu_shard == self.n_shards) + + def _log(self, t: float, kind: str, **detail: Any) -> None: + self.event_log.append({"t_s": float(t), "kind": kind, **detail}) + + def _release(self, sh: _Shard) -> None: + if sh.gpus.size: + self.gpu_shard[sh.gpus] = self.n_shards + sh.gpus = np.zeros(0, dtype=int) + self.admit_dirty = True + + # -- faults ---------------------------------------------------------------- + + def _fault_shard(self, sh: _Shard, t: float, reason: str) -> None: + if sh.state in ("done", "queued"): + return + if sh.job.multisite: + durable = self.merged_progress[sh.job_index] + else: + durable = sh.durable + lost = max(sh.progress - durable, 0.0) + sh.lost_steps += lost + sh.useful_steps -= lost + sh.progress = durable + sh.fault_merged = self.merged_progress[sh.job_index] + sh.faults += 1 + sh.needs_restore = True + sh.state = "queued" + sh.ckpt_now = False + sh.migrate_to = None + sh.since_ckpt_s = 0.0 + sh.earliest_s = t + sh.forced_site = None if not sh.job.multisite else sh.forced_site + self._release(sh) + if sh.sid not in self.pending: + self.pending.append(sh.sid) + self.pending.sort(key=lambda i: (0 if self.shards[i].needs_restore else 1, self.shards[i].job.start_s, self.shards[i].job.job_id, self.shards[i].part)) + self._log(t, "job_fault", job_id=sh.job.job_id, part=sh.part, reason=reason, lost_steps=lost) + + def _trip_rack(self, r: int, t: float, off_s: float, reason: str, kind: str = KIND_BREAKER_TRIP) -> None: + self.rack_on[r] = False + self.rack_off_until[r] = t + off_s + self._log(t, kind, rack_id=self.rack_ids[r], site_id=self.rack_site_ids[r], reason=reason, off_s=off_s) + for sh in self.shards: + if sh.gpus.size and np.any(self.gpu_rack[sh.gpus] == r): + self._fault_shard(sh, t, f"rack {self.rack_ids[r]} off ({reason})") + + # -- admission ------------------------------------------------------------- + + def _try_place(self, sh: _Shard, t: float) -> bool: + free = self._free_mask() + job = sh.job + if job.multisite: + site = sh.part + if int(free[self.place.site_gpus[site]].sum()) < sh.n_gpus: + return False + fresh = [o for o in self.job_shards[sh.job_index] if o.state == "queued" and not o.needs_restore] + for o in fresh: + if int(free[self.place.site_gpus[o.part]].sum()) < o.n_gpus: + return False + else: + hint = sh.forced_site + if hint is None and job.site_id is not None: + hint = self.site_ids.index(job.site_id) + site = self.place.choose_site(free, sh.n_gpus, hint) + if site is None: + return False + idx = self.place.pick(free, site, sh.n_gpus) + if idx.size != sh.n_gpus: # pragma: no cover - guarded by the free-count checks above + raise RuntimeError("placement returned the wrong number of GPUs") + sh.gpus = idx + sh.site = site + self.gpu_shard[idx] = sh.sid + if sh.needs_restore: + sh.restarts += 1 + sh.state = "restore" + sh.left_s = self.cfg.transfer.restore_s(sh.n_gpus) + sh.needs_restore = False + else: + sh.state = "running" + self._log(t, "job_start" if sh.restarts == 0 else "job_restart", job_id=job.job_id, part=sh.part, site_id=self.site_ids[site]) + return True + + def _admit(self, t: float) -> None: + still = [] + next_elig = math.inf + for sid in self.pending: + sh = self.shards[sid] + elig = _quantize_up(sh.earliest_s, self.cdt) + if elig > t + 1e-9: + still.append(sid) + next_elig = min(next_elig, elig) + continue + if not self._try_place(sh, t): + still.append(sid) + self.pending = still + self.next_eligible_s = next_elig + self.admit_dirty = False + + # -- controller ---------------------------------------------------------------- + + def _job_status(self, group: list[_Shard]) -> str: + states = {s.state for s in group} + if states == {"done"}: + return "done" + if states <= {"queued"}: + return "queued" + if any(s.paused for s in group): + return "paused" + for k in ("transfer", "restore", "ckpt", "merge", "wait", "running"): + if k in states: + return k + return "queued" + + def _visible_state(self, t: float, b: int) -> VisibleState: + cfg = self.cfg + free_mask = self._free_mask() + gpu_temp = np.where( + self.gpu_alive & self.rack_on[self.gpu_rack], + self.rack_air_now[self.gpu_rack] + self.k_gpu * self.gpu_mean_prev, np.nan, + ) + eff_import = self.site_import * self.signals.import_limit_factor[b] + site_power = self.site_power_prev.copy() + now = { + "rack_air_c": self.rack_air_now.copy(), + "supply_c": self.supply_now.copy(), + "rack_it_power_w": self.rack_it_prev.copy(), + "max_gpu_temp_c": self._rack_max(gpu_temp), + "breaker_load_ratio": self.rack_ratio_prev.copy(), + "rack_on": self.rack_on.copy(), + "rack_power_cap_w": self.rack_cap_w.copy(), + "site_power_w": site_power, + "site_power_cap_w": self.site_cap.copy(), + "site_import_limit_w": eff_import, + "free_gpus": np.array([int(free_mask[self.place.site_gpus[s]].sum()) for s in range(self.S)]), + "gpu_temp_c": gpu_temp, + } + jobs = [] + for ji, job in enumerate(self.jobs): + if job.submit_s > t + 1e-9: + continue + group = self.job_shards[ji] + status = self._job_status(group) + if status == "done": + continue + prog = float(np.mean([s.progress for s in group])) + dur = float(np.mean([(self.merged_progress[ji] if job.multisite else s.durable) for s in group])) + jobs.append( + JobView( + job_id=job.job_id, status=status, + site_ids=tuple(self.site_ids[s.site] for s in group if s.site is not None and s.gpus.size), + n_gpus=job.n_gpus, progress_steps=prog, total_steps=job.total_steps, + steps_since_durable=max(prog - dur, 0.0), flexible=job.flexible, + value_per_gpu_hour=job.value_per_gpu_hour, submit_s=job.submit_s, + planned_start_s=job.start_s, paused=any(s.paused for s in group), + ) + ) + horizon = cfg.known_future_horizon_s + planned = tuple( + {"job_id": j.job_id, "planned_start_s": j.start_s, "duration_s": j.duration_s, "n_gpus": j.n_gpus, + "flexible": j.flexible, "site_id": j.site_id, "multisite": j.multisite} + for j in self.jobs if t < j.start_s <= t + horizon + ) + h0 = int(t // 3600.0) + h1 = min(h0 + int(math.ceil(horizon / 3600.0)) + 1, self.price_h.shape[0] + 1) + sel = slice(h0, h1) + known_future = { + "planned_jobs": planned, + "announced_curtailments": tuple(cfg.events.announced_curtailments(t)), + "hour_start_s": np.arange(h0, h1) * 3600.0, + "outdoor_forecast_c": np.stack([cfg.environment[s].forecast_c[sel] for s in self.site_ids], axis=1), + "price_usd_per_kwh": np.stack([cfg.environment[s].price_usd_per_kwh[sel] for s in self.site_ids], axis=1), + "carbon_g_per_kwh": np.stack([cfg.environment[s].carbon_g_per_kwh[sel] for s in self.site_ids], axis=1), + } + n_valid = b + + def history(channel: str, window_s: float) -> Tuple[np.ndarray, np.ndarray]: + arrays = { + "rack_air_c": self.rack_air_b, "supply_c": self.rack_supply_b, "rack_it_power_w": self.rack_it_b, + "max_gpu_temp_c": self.rack_maxgpu_b, "breaker_load_ratio": self.rack_ratio_b, + "site_power_w": self.site_power_b, "rack_cooling_power_w": self.rack_cool_b, + } + if channel not in arrays: + raise KeyError(f"unknown channel {channel!r}; choose from {sorted(arrays)}") + n = min(int(math.ceil(window_s / self.cdt)), n_valid) + lo = n_valid - n + return (np.arange(lo, n_valid) + 1) * self.cdt, arrays[channel][lo:n_valid].copy() + + return VisibleState( + t_s=t, site_ids=tuple(self.site_ids), rack_ids=tuple(self.rack_ids), rack_site_ids=tuple(self.rack_site_ids), + now=now, jobs=tuple(jobs), known_future=known_future, _history=history, + ) + + def _rack_max(self, gpu_temp: np.ndarray) -> np.ndarray: + masked = np.where(np.isnan(gpu_temp), -np.inf, gpu_temp) + m = np.maximum.reduceat(masked, self.rack_start) + out = np.where(np.isfinite(m), m, np.nan) + return out + + def _apply_actions(self, actions: Sequence[Action], t: float) -> None: + for act in actions or (): + name = type(act).__name__ + status = "applied" + try: + if isinstance(act, SetRackPowerCap): + if act.rack_id not in self.rack_ids: + raise ValueError("unknown rack") + if act.cap_w is not None: + _num(act.cap_w, "cap_w") + self.rack_cap_w[self.rack_ids.index(act.rack_id)] = math.inf if act.cap_w is None else float(act.cap_w) + else: + ji = self.job_index.get(act.job_id) + if ji is None: + raise ValueError("unknown job") + group = self.job_shards[ji] + job = self.jobs[ji] + if isinstance(act, AdmitJob): + if any(s.state != "queued" or s.needs_restore for s in group) or t < job.submit_s: + raise ValueError("job is not a not-yet-started submitted job") + for s in group: + s.earliest_s = t + if act.site_id is not None and not job.multisite: + if act.site_id not in self.site_ids: + raise ValueError("unknown site") + s.forced_site = self.site_ids.index(act.site_id) + self.admit_dirty = True + elif isinstance(act, DeferJob): + if not job.flexible: + raise ValueError("job is not flexible") + if any(s.state != "queued" for s in group): + raise ValueError("job already started") + for s in group: + s.earliest_s = max(float(act.until_s), t) + self.admit_dirty = True + elif isinstance(act, PauseJob): + if not job.flexible: + raise ValueError("job is not flexible") + if all(s.state == "queued" or s.state == "done" for s in group): + raise ValueError("job is not placed") + for s in group: + s.paused = True + elif isinstance(act, ResumeJob): + for s in group: + s.paused = False + elif isinstance(act, CheckpointNow): + if job.multisite: + raise ValueError("multi-site jobs checkpoint through averaging") + if group[0].state != "running": + raise ValueError("job is not running") + group[0].ckpt_now = True + elif isinstance(act, MigrateJob): + sh = group[0] + if not job.flexible or job.multisite: + raise ValueError("job is not flexible or spans sites") + if sh.state != "running": + raise ValueError("job is not running") + if act.to_site_id not in self.site_ids: + raise ValueError("unknown site") + dest = self.site_ids.index(act.to_site_id) + if dest == sh.site: + raise ValueError("job is already at that site") + if int(self._free_mask()[self.place.site_gpus[dest]].sum()) < sh.n_gpus: + raise ValueError("destination lacks free GPUs") + sh.migrate_to = dest + sh.ckpt_now = True + else: + raise ValueError("unknown action type") + except ValueError as exc: + status = f"rejected: {exc}" + detail = asdict(act) if is_dataclass(act) else {} + self.action_log.append({"t_s": float(t), "action": name, "detail": detail, "status": status}) + + # -- shard advance ----------------------------------------------------------------- + + def _advance_shard(self, sh: _Shard, until: float, speed: float, segs: list) -> None: + """Advance one shard from ``sh.cursor`` to ``until`` (offsets inside the block).""" + job = sh.job + tr = self.cfg.transfer + guard = 0 + while until - sh.cursor > 1e-9 and guard < 64: + guard += 1 + rem = until - sh.cursor + st = sh.state + if st == "running": + if sh.paused: + sh.cursor = until + return + rate = speed / job.step_time_s + t_done = (sh.total - sh.progress) / rate + t_ckpt = math.inf + if not job.multisite: + t_ckpt = 0.0 if sh.ckpt_now else max(job.checkpoint_every_s - sh.since_ckpt_s, 0.0) + t_merge = math.inf + if job.multisite: + a = job.average_every_steps + boundary = (math.floor(self.merged_progress[sh.job_index] / a + 1e-9) + 1) * a + boundary = min(boundary, sh.total) + t_merge = max((boundary - sh.progress) / rate, 0.0) + d = min(rem, t_done, t_ckpt, t_merge) + if d > 0: + segs.append((sh.sid, 0, sh.cursor, d, sh.progress, rate)) + sh.progress += rate * d + sh.useful_steps += rate * d + sh.since_ckpt_s += d + sh.run_s += d + sh.cursor += d + if sh.progress >= sh.total - 1e-9: + sh.progress = sh.total + sh.state = "done" + self._release(sh) + self._log(self.t_now + sh.cursor, "job_done", job_id=job.job_id, part=sh.part) + return + if job.multisite and t_merge <= d + 1e-12 and sh.progress >= boundary - 1e-9: + sh.progress = boundary + others = [o for o in self.job_shards[sh.job_index] if o is not sh and o.state in _LIVE_BARRIER] + if others: + sh.state = "wait" + sh.arrive_s = sh.cursor + return + self.merged_progress[sh.job_index] = boundary + continue + if not job.multisite and (sh.ckpt_now or sh.since_ckpt_s >= job.checkpoint_every_s - 1e-9): + sh.state = "ckpt" + sh.left_s = job.checkpoint_duration_s + sh.snapshot = sh.progress + sh.ckpt_now = False + if sh.left_s <= 0: + sh.durable = sh.snapshot + sh.since_ckpt_s = 0.0 + sh.state = "running" + elif st == "ckpt": + d = min(rem, sh.left_s) + if d > 0: + segs.append((sh.sid, 2, sh.cursor, d, 0.0, 0.0)) + sh.left_s -= d + sh.cursor += d + if sh.left_s <= 1e-9: + sh.durable = sh.snapshot + sh.since_ckpt_s = 0.0 + sh.state = "running" + if sh.migrate_to is not None: + self._begin_transfer(sh) + elif st in ("restore", "transfer", "merge"): + d = min(rem, sh.left_s) + if d > 0: + mode = 1 if st == "merge" else 3 + segs.append((sh.sid, mode, sh.cursor, d, 0.0, 0.0)) + sh.left_s -= d + sh.cursor += d + if sh.left_s <= 1e-9: + if st == "merge": + sh.state = "running" + if sh.arrive_s >= 0: + self.merged_progress[sh.job_index] = max(self.merged_progress[sh.job_index], sh.progress) + elif st == "transfer": + sh.state = "restore" + sh.left_s = tr.restore_s(sh.n_gpus) + else: # restore finished + if job.multisite and any(o is not sh and o.state in _LIVE_BARRIER for o in self.job_shards[sh.job_index]): + adopted = self.merged_progress[sh.job_index] + sh.skipped_steps += max(adopted - sh.progress, 0.0) + sh.progress = max(adopted, sh.progress) + sh.state = "merge" + sh.left_s = self.merge_stall[sh.sid] + sh.arrive_s = -1.0 + else: + sh.state = "running" + elif st == "wait": + return # held at the barrier; resolved by the job-level loop + else: + return + + def _begin_transfer(self, sh: _Shard) -> None: + dest = sh.migrate_to + sh.migrate_to = None + assert dest is not None + free = self._free_mask() + src_gpus = sh.gpus + if int(free[self.place.site_gpus[dest]].sum()) < sh.n_gpus: + self._log(self.t_now + sh.cursor, "migration_cancelled", job_id=sh.job.job_id) + return + idx = self.place.pick(free, dest, sh.n_gpus) + self.gpu_shard[src_gpus] = self.n_shards + self.gpu_shard[idx] = sh.sid + sh.gpus = idx + sh.site = dest + sh.state = "transfer" + sh.left_s = self.cfg.transfer.transfer_s(sh.n_gpus) + sh.migrations += 1 + self.admit_dirty = True + self._log(self.t_now + sh.cursor, "migration_start", job_id=sh.job.job_id, to_site_id=self.site_ids[dest]) + + def _advance_job(self, ji: int, speeds: np.ndarray, segs: list) -> None: + group = self.job_shards[ji] + active = [s for s in group if s.state not in ("queued", "done")] + if not active: + return + for s in active: + s.cursor = 0.0 + if s.state == "wait": + s.arrive_s = 0.0 + for _ in range(16): + for s in active: + if s.state != "wait": + self._advance_shard(s, self.cdt, float(speeds[s.sid]), segs) + waiting = [s for s in active if s.state == "wait"] + live = [s for s in active if s.state in _LIVE_BARRIER] + if len(live) == 1 and waiting: + lone = waiting[0] + lone.state = "running" + self.merged_progress[ji] = max(self.merged_progress[ji], lone.progress) + continue + if waiting and len(waiting) == len(live): + tb = max(s.arrive_s for s in waiting) + if tb >= self.cdt - 1e-9: + for s in waiting: + segs.append((s.sid, 1, s.arrive_s, self.cdt - s.arrive_s, 0.0, 0.0)) + s.cursor = self.cdt + return + for s in waiting: + if tb > s.arrive_s: + segs.append((s.sid, 1, s.arrive_s, tb - s.arrive_s, 0.0, 0.0)) + s.cursor = tb + s.state = "merge" + s.left_s = self.merge_stall[s.sid] + s.arrive_s = 0.0 + continue + for s in waiting: + if self.cdt - s.arrive_s > 1e-9: + segs.append((s.sid, 1, s.arrive_s, self.cdt - s.arrive_s, 0.0, 0.0)) + s.cursor = self.cdt + if all(s.cursor >= self.cdt - 1e-9 or s.state in ("done", "queued") for s in active): + return + # safety: close out any shard that did not reach the end of the block + for s in active: + s.cursor = max(s.cursor, self.cdt) + + # -- rendering ------------------------------------------------------------------- + + def _fractions(self, segs: list) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + rows = self.n_shards + 1 + K, dt = self.K, self.dt + Fc = np.zeros((rows, K)) + Fm = np.zeros((rows, K)) + Fk = np.zeros((rows, K)) + if not segs: + return Fc, Fm, Fk + arr = np.array(segs, dtype=float) + sid = arr[:, 0].astype(int) + mode = arr[:, 1].astype(int) + a = arr[:, 2][:, None] + b = (arr[:, 2] + arr[:, 3])[:, None] + x0 = arr[:, 4][:, None] + rate = arr[:, 5][:, None] + lo = np.arange(K)[None, :] * dt + hi = lo + dt + t0 = np.maximum(a, lo) + t1 = np.minimum(b, hi) + ov = np.clip(t1 - t0, 0.0, None) + with np.errstate(divide="ignore", invalid="ignore"): + xs = x0 + rate * (t0 - a) + xe = x0 + rate * (t1 - a) + comp_t = np.where(rate > 0, (_G(xe, self.cfg.compute_fraction_of_step) - _G(xs, self.cfg.compute_fraction_of_step)) / np.where(rate > 0, rate, 1.0), 0.0) + comp_t = np.where(mode[:, None] == 0, np.minimum(np.maximum(comp_t, 0.0), ov), 0.0) + comm_t = np.where(mode[:, None] == 0, ov - comp_t, 0.0) + np.where((mode == 1)[:, None], ov, 0.0) + ckpt_t = np.where((mode == 2)[:, None], ov, 0.0) + # restore/transfer segments (mode 3) draw idle power: they add nothing here + np.add.at(Fc, sid, comp_t / dt) + np.add.at(Fm, sid, comm_t / dt) + np.add.at(Fk, sid, ckpt_t / dt) + return Fc, Fm, Fk + + # -- main loop --------------------------------------------------------------------------- + + def run(self) -> "SimResult": + cfg = self.cfg + lv = cfg.levels + th = cfg.thermal + R, S, G, K = self.R, self.S, self.G, self.K + ctrl_every = int(round(cfg.controller_interval_s / self.cdt)) + # Initial thermal state: steady state at idle plus host power, supply at setpoint. + amb0 = self.outdoor_b[0] + sp0 = th.supply_setpoint_c + th.outdoor_coupling_k_per_k * np.maximum(0.0, amb0[self.rack_site] - th.outdoor_reference_c) + self.supply_now = sp0.copy() + p0 = self.rack_n * (lv.idle_w + lv.host_w_per_gpu) + self.rack_air_now = self.supply_now + self.Rres * p0 + self.rack_it_prev = p0.copy() + self.rack_ratio_prev = p0 / self.rating + self.site_power_prev = self.site_membership.T @ p0 + self.site_base + self.theta = np.zeros(R) + self.rack_it_b = np.zeros((self.Nb, R)) + self.site_power_b = np.zeros((self.Nb, S)) + throttled_prev = np.zeros(R, dtype=int) + self.t_now = 0.0 + + for b in range(self.Nb): + t = b * self.cdt + self.t_now = t + sig = self.signals + # 1. repairs, resets, discrete events + repaired = (~self.gpu_alive) & (self.gpu_down_until <= t + 1e-9) + if repaired.any(): + self.gpu_alive[repaired] = True + self.admit_dirty = True + reset = (~self.rack_on) & (self.rack_off_until <= t + 1e-9) + if reset.any(): + self.rack_on[reset] = True + self.theta[reset] = 0.0 + self.admit_dirty = True + for r in np.flatnonzero(reset): + self._log(t, "rack_reset", rack_id=self.rack_ids[r]) + for blk, r, off_s in self._events_at(sig.forced_trips, b): + if self.rack_on[r]: + self._trip_rack(r, t, off_s, "forced") + for blk, r, slot, repair_s in self._events_at(sig.hardware_faults, b): + g = int(self.rack_start[r] + slot) + if self.gpu_alive[g]: + self.gpu_alive[g] = False + self.gpu_down_until[g] = t + repair_s + self._log(t, KIND_HARDWARE_FAULT, rack_id=self.rack_ids[r], gpu_slot=slot, repair_s=repair_s) + sid = self.gpu_shard[g] + if sid != self.n_shards: + self._fault_shard(self.shards[sid], t, f"gpu fault {self.rack_ids[r]}/{slot}") + # GPU temperatures at the start of the step, and protective shutdown of overheated racks. + amb_b = self.outdoor_b[b] + gpu_temp = self.rack_air_now[self.gpu_rack] + self.k_gpu * self.gpu_mean_prev + for r, slot, off in sig.gpu_temp_offsets(t): + gpu_temp[self.rack_start[r] + slot] += off + powered = self.gpu_alive & self.rack_on[self.gpu_rack] + hottest = np.maximum.reduceat(np.where(powered, gpu_temp, -np.inf), self.rack_start) + for r in np.flatnonzero(hottest >= th.gpu_shutdown_c): + self._trip_rack(r, t, th.thermal_shutdown_hold_s, "gpu over shutdown temperature", KIND_THERMAL_SHUTDOWN) + # 2. submissions becoming eligible, controller, admission + if self.controller is not None and b % ctrl_every == 0: + state = self._visible_state(t, b) + self._apply_actions(self.controller.decide(t, state), t) + if self.admit_dirty or t + 1e-9 >= self.next_eligible_s: + self._admit(t) + # 3. GPU clocks + gpu_alive_on = self.gpu_alive & self.rack_on[self.gpu_rack] + f_temp = cfg.throttle.clock_fraction(gpu_temp) + n_alive = np.maximum(np.bincount(self.gpu_rack, weights=gpu_alive_on.astype(float), minlength=R), 1.0) + host_w = lv.host_w_per_gpu * self.rack_n * self.rack_on + per_gpu_cap = (self.rack_cap_w - host_w) / n_alive + f_cap = np.where( + np.isfinite(per_gpu_cap), cfg.throttle.clock_for_power(per_gpu_cap, lv.compute_w, lv.idle_w), 1.0 + ) + f_gpu = np.minimum(f_temp, f_cap[self.gpu_rack]) + speeds = np.ones(self.n_shards + 1) + for sh in self.shards: + if sh.gpus.size: + speeds[sh.sid] = float(f_gpu[sh.gpus].min()) + throttled_now = np.bincount(self.gpu_rack, weights=((f_temp < 1.0) & gpu_alive_on).astype(float), minlength=R).astype(int) + for r in range(R): + if throttled_now[r] > 0 and throttled_prev[r] == 0: + self._log(t, KIND_THERMAL_THROTTLE, rack_id=self.rack_ids[r], phase="start", + max_gpu_temp_c=float(gpu_temp[self.rack_start[r]:self.rack_start[r] + self.rack_n[r]].max())) + elif throttled_now[r] == 0 and throttled_prev[r] > 0: + self._log(t, KIND_THERMAL_THROTTLE, rack_id=self.rack_ids[r], phase="end") + throttled_prev = throttled_now + # 4. advance jobs + gpu_shard_start = self.gpu_shard.copy() + segs: list = [] + for ji in range(len(self.jobs)): + if any(s.state not in ("queued", "done") for s in self.job_shards[ji]): + self._advance_job(ji, speeds, segs) + # 5. render power + Fc, Fm, Fk = self._fractions(segs) + Fi = np.clip(1.0 - Fc - Fm - Fk, 0.0, 1.0) + alpha = cfg.throttle.power_exponent + sc = speeds ** alpha + Lc = lv.idle_w + (lv.compute_w - lv.idle_w) * sc + Lm = lv.idle_w + (lv.comm_w - lv.idle_w) * sc + Lk = lv.idle_w + (lv.checkpoint_w - lv.idle_w) * sc + P_row = Fc * Lc[:, None] + Fm * Lm[:, None] + Fk * Lk[:, None] + Fi * lv.idle_w + P_gpu = P_row[gpu_shard_start] + if lv.noise_rel_sigma > 0: + P_gpu = P_gpu + (lv.noise_rel_sigma * (Fc * Lc[:, None]))[gpu_shard_start] * self._noise(b) + up = (self.gpu_alive & self.rack_on[self.gpu_rack]).astype(float) + P_gpu = P_gpu * up[:, None] + rack_gpu = np.add.reduceat(P_gpu, self.rack_start, axis=0) # (R, K) + self.rack_gpu_w[b * K:(b + 1) * K, :] = rack_gpu.T + self.gpu_mean_prev = P_gpu.mean(axis=1) + for wi, (wa, wb) in enumerate(self.windows): + if wa - 1e-9 <= t < wb - 1e-9: + j = int(round((t - wa) / self.dt)) + self.window_data[wi][:, j:j + K] = P_gpu + host_b = lv.host_w_per_gpu * self.rack_n * self.rack_on + rack_it_mean = rack_gpu.mean(axis=1) + host_b + # 6. thermal, breaker, cooling + cap_eff = self.cap_nominal * sig.capacity_factor[b] * self._capacity_derate(amb_b)[self.rack_site] + res_eff = self.Rres * sig.resistance_multiplier[b] + sp = th.supply_setpoint_c + th.outdoor_coupling_k_per_k * np.maximum(0.0, amb_b[self.rack_site] - th.outdoor_reference_c) + sig.supply_offset_k[b] + lag = th.cooling_lag_s + T1, Ts1, removed = rc_step(self.C, res_eff, lag, cap_eff, sp, self.rack_air_now, self.supply_now, rack_it_mean, self.cdt) + q_mean = removed / self.cdt + cool_w = cfg.cooling_power.power_w(np.maximum(q_mean, 0.0), amb_b[self.rack_site], 0.5 * (self.supply_now + Ts1)) + rating_eff = self.rating * sig.breaker_rating_factor[b] + ratio = np.where(self.rack_on, rack_it_mean / rating_eff, 0.0) + theta_next, tripped = cfg.breaker.step(self.theta, ratio, self.cdt) + self.theta = np.where(self.rack_on, theta_next, 0.0) + # record + self.rack_host_b[b] = host_b + self.rack_cool_b[b] = cool_w + self.rack_air_b[b] = T1 + self.rack_supply_b[b] = Ts1 + gtemp_now = self.rack_air_now[self.gpu_rack] + self.k_gpu * self.gpu_mean_prev + self.rack_maxgpu_b[b] = np.maximum.reduceat(np.where(up > 0, gtemp_now, -np.inf), self.rack_start) + self.rack_throttled_b[b] = throttled_now / self.rack_n + self.rack_ratio_b[b] = ratio + self.rack_removed_b[b] = q_mean + self.rack_cap_b[b] = cap_eff + self.rack_on_b[b] = self.rack_on + self.rack_it_b[b] = rack_it_mean + free_mask_end = self._free_mask() + self.site_free_b[b] = [int(free_mask_end[self.place.site_gpus[s]].sum()) for s in range(S)] + self.rack_air_now = T1 + self.supply_now = Ts1 + self.rack_it_prev = rack_it_mean + self.rack_ratio_prev = ratio + site_p = self.site_membership.T @ (rack_it_mean + cool_w) + self.site_base + self.site_power_prev = site_p + self.site_power_b[b] = site_p + # breaker opens at the end of this step if the thermal element reached its level + end_t = t + self.cdt + for r in np.flatnonzero(tripped & self.rack_on): + self._trip_rack(r, end_t, th.breaker_reset_delay_s, "overload") + self.rack_air_now[r] = T1[r] + return self._finish() + + @staticmethod + def _events_at(items: Sequence[Tuple], b: int) -> list: + return [it for it in items if it[0] == b] + + # -- results ------------------------------------------------------------------------------- + + def _finish(self) -> "SimResult": + return SimResult(self) + + +# --------------------------------------------------------------------------- +# Results +# --------------------------------------------------------------------------- + + +class SimResult: + """Outputs of one run. All quantities are simulated; see ``meta`` for provenance.""" + + def __init__(self, run: _Run) -> None: + self._run = run + cfg = run.cfg + K, Nb = run.K, run.Nb + rack_cool_s = np.repeat(run.rack_cool_b, K, axis=0) + rack_host_s = np.repeat(run.rack_host_b, K, axis=0) + self.rack_it_power_w = run.rack_gpu_w + rack_host_s + self.rack_gpu_power_w = run.rack_gpu_w + self.rack_power_w = self.rack_it_power_w + rack_cool_s + self.site_power_w = self.rack_power_w @ run.site_membership + run.site_base[None, :] + self.rack_cooling_power_w = rack_cool_s + self.rack_host_power_w = rack_host_s + self.site_cap_violation = self._violation(self.site_power_w, run.site_cap[None, :]) + eff_import = run.site_import[None, :] * np.repeat(run.signals.import_limit_factor, K, axis=0) + self.import_violation = self._violation(self.site_power_w, eff_import) + self.gpu_windows = [ + {"start_s": a, "end_s": b, "gpu_power_w": run.window_data[i]} for i, (a, b) in enumerate(run.windows) + ] + per_min = int(round(60.0 / run.cdt)) if run.cdt <= 60.0 else 1 + self.minute_s = 60.0 if run.cdt <= 60.0 else run.cdt + nm = Nb // per_min + self.rack_temp_c_1min = run.rack_air_b[: nm * per_min].reshape(nm, per_min, run.R).mean(axis=1) + self.rack_supply_c_1min = run.rack_supply_b[: nm * per_min].reshape(nm, per_min, run.R).mean(axis=1) + self.rack_max_gpu_temp_c_1min = run.rack_maxgpu_b[: nm * per_min].reshape(nm, per_min, run.R).max(axis=1) + H = int(round(cfg.horizon_s / 3600.0)) + per_h = int(round(3600.0 / self.dt)) + e = self.site_power_w.reshape(H, per_h, run.S).sum(axis=1) * self.dt / 3.6e6 + self.site_energy_kwh_1h = e + self.site_mean_power_w_1h = e * 3.6e6 / 3600.0 + self.site_cost_usd_1h = e * run.price_h + self.site_carbon_kg_1h = e * run.carbon_h / 1000.0 + self.jobs_table = self._job_table() + self.events = list(run.event_log) + self.actions = list(run.action_log) + self.ground_truth_events = cfg.events.ground_truth() + self.plan = plan_schedule(cfg) + self.meta = self._meta() + + @property + def dt(self) -> float: + return self._run.dt + + def _violation(self, power: np.ndarray, limit: np.ndarray) -> dict[str, np.ndarray]: + over = np.maximum(power - limit, 0.0) + return { + "seconds_above": (over > 0).sum(axis=0) * self.dt, + "energy_above_kwh": over.sum(axis=0) * self.dt / 3.6e6, + "max_excess_w": over.max(axis=0), + } + + def _job_table(self) -> list[dict[str, Any]]: + run = self._run + rows = [] + for ji, job in enumerate(run.jobs): + group = run.job_shards[ji] + useful_gpu_h = sum(s.useful_steps * job.step_time_s * s.n_gpus / 3600.0 for s in group) + lost_gpu_h = sum(s.lost_steps * job.step_time_s * s.n_gpus / 3600.0 for s in group) + rows.append( + { + "job_id": job.job_id, "n_gpus": int(job.n_gpus), "flexible": bool(job.flexible), "multisite": bool(job.multisite), + "status": run._job_status(group), + "useful_steps": float(sum(s.useful_steps for s in group)), + "total_steps": float(job.total_steps * len(group)), + "lost_steps": float(sum(s.lost_steps for s in group)), + "skipped_steps": float(sum(s.skipped_steps for s in group)), + "useful_gpu_hours": float(useful_gpu_h), "lost_gpu_hours": float(lost_gpu_h), + "value_usd": float(useful_gpu_h * job.value_per_gpu_hour), + "faults": int(sum(s.faults for s in group)), "restarts": int(sum(s.restarts for s in group)), + "migrations": int(sum(s.migrations for s in group)), + } + ) + return rows + + def _meta(self) -> dict[str, Any]: + run = self._run + cfg = run.cfg + return { + "schema": RESULT_SCHEMA, + "data_kind": "simulated", + "scenario_id": cfg.scenario_id, + "start_utc": cfg.start_utc, + "seed": run.seed, + "horizon_s": cfg.horizon_s, + "base_dt_s": run.dt, + "control_dt_s": run.cdt, + "site_ids": list(run.site_ids), + "rack_ids": list(run.rack_ids), + "rack_site_ids": list(run.rack_site_ids), + "rack_gpu_counts": [int(n) for n in run.rack_n], + "units": { + "rack_gpu_power_w": "W, sum over the rack's GPUs, at base_dt_s", + "rack_it_power_w": "W, GPU + host overhead, at base_dt_s", + "rack_power_w": "W, IT + cooling share, at base_dt_s", + "site_power_w": "W, sum of racks + Site.base_power_w, at base_dt_s", + "rack_temp_c_1min": "deg C, mean rack air temperature per minute", + "site_energy_kwh_1h": "kWh per hour", "site_cost_usd_1h": "USD per hour", + "site_carbon_kg_1h": "kg CO2 per hour", + }, + "quantity_provenance": { + "gpu_power_levels": "simulated; idle/compute and checkpoint/compute ratios derived from measured e002 laptop telemetry, scaled to cited H100 SXM TDP; other levels assumed", + "thermal": "simulated; RC parameters are scenario assumptions (DGX H100 mass cited)", + "cooling_power": "simulated; PUE baseline from the gpu_stack calculator assumption, Carnot COP shape", + "events": "simulated; precursors are assumptions of the simulation, not measured facts", + "environment": "simulated or caller-supplied series; see scenario", + "jobs": "simulated synthetic schedule or caller-supplied schedule", + }, + "power_levels": cfg.levels.to_dict(), + "measured_e002": {k: cfg.measured_levels[k] for k in ( + "data_kind", "source_file", "source_sha256", "median_power_w_laptop", "ratio_to_compute", "sample_counts", + "compute_relative_std", "median_window_duration_s", "telemetry_update_period_s")}, + "throttle": cfg.throttle.to_dict(), + "cooling_power_model": cfg.cooling_power.to_dict(), + "breaker": cfg.breaker.to_dict(), + "assumptions": list(cfg.assumptions), + "simplifications": list(SIMPLIFICATIONS), + } + + # -- views ------------------------------------------------------------------------------- + + def covariates(self, level: str, resolution_s: float, t0_s: float = 0.0, t1_s: Optional[float] = None) -> dict[str, np.ndarray]: + """Known-future covariates aligned with an output grid (see :func:`planned_covariates`).""" + return planned_covariates(self._run.cfg, self.plan, resolution_s, level, t0_s, t1_s) + + def to_arrays(self) -> dict[str, Any]: + """Numpy arrays plus a ``meta`` dict (units, simulated vs measured-ratio-derived).""" + run = self._run + out: dict[str, Any] = { + "rack_gpu_power_w": self.rack_gpu_power_w, + "rack_it_power_w": self.rack_it_power_w, + "rack_power_w": self.rack_power_w, + "rack_cooling_power_w": self.rack_cooling_power_w, + "site_power_w": self.site_power_w, + "rack_temp_c_1min": self.rack_temp_c_1min, + "rack_supply_c_1min": self.rack_supply_c_1min, + "rack_max_gpu_temp_c_1min": self.rack_max_gpu_temp_c_1min, + "site_energy_kwh_1h": self.site_energy_kwh_1h, + "site_mean_power_w_1h": self.site_mean_power_w_1h, + "site_cost_usd_1h": self.site_cost_usd_1h, + "site_carbon_kg_1h": self.site_carbon_kg_1h, + "site_cap_seconds_above": self.site_cap_violation["seconds_above"], + "site_cap_energy_above_kwh": self.site_cap_violation["energy_above_kwh"], + "site_import_seconds_above": self.import_violation["seconds_above"], + "site_import_energy_above_kwh": self.import_violation["energy_above_kwh"], + "outdoor_c_control_step": run.outdoor_b, + "meta": self.meta, + } + for i, w in enumerate(self.gpu_windows): + out[f"gpu_power_w_window_{i}"] = w["gpu_power_w"] + out[f"gpu_power_w_window_{i}_bounds_s"] = np.array([w["start_s"], w["end_s"]]) + for name, level, res, t0, t1 in ( + ("rack_1min", "rack", 60.0, 0.0, None), + ("site_1h", "site", 3600.0, 0.0, None), + ): + for k, v in self.covariates(level, res, t0, t1).items(): + out[f"cov_{name}_{k}"] = v + for i, w in enumerate(self.gpu_windows): + for level in ("rack", "site"): + for k, v in self.covariates(level, self.dt, w["start_s"], w["end_s"]).items(): + out[f"cov_{level}_window_{i}_{k}"] = v + return out + + def summary(self) -> dict[str, Any]: + """Scalar summary statistics (simulated).""" + run = self._run + rows = self.jobs_table + n = len(rows) + return { + "data_kind": "simulated", + "site_energy_kwh": [float(x) for x in self.site_energy_kwh_1h.sum(axis=0)], + "site_mean_power_w": [float(x) for x in self.site_power_w.mean(axis=0)], + "site_peak_power_w": [float(x) for x in self.site_power_w.max(axis=0)], + "site_cost_usd": [float(x) for x in self.site_cost_usd_1h.sum(axis=0)], + "site_carbon_kg": [float(x) for x in self.site_carbon_kg_1h.sum(axis=0)], + "site_cap_seconds_above": [float(x) for x in self.site_cap_violation["seconds_above"]], + "site_cap_energy_above_kwh": [float(x) for x in self.site_cap_violation["energy_above_kwh"]], + "site_import_seconds_above": [float(x) for x in self.import_violation["seconds_above"]], + "site_import_energy_above_kwh": [float(x) for x in self.import_violation["energy_above_kwh"]], + "jobs": n, + "jobs_done": sum(r["status"] == "done" for r in rows), + "useful_gpu_hours": float(sum(r["useful_gpu_hours"] for r in rows)), + "lost_gpu_hours": float(sum(r["lost_gpu_hours"] for r in rows)), + "lost_steps": float(sum(r["lost_steps"] for r in rows)), + "skipped_steps": float(sum(r["skipped_steps"] for r in rows)), + "value_usd": float(sum(r["value_usd"] for r in rows)), + "job_faults": int(sum(r["faults"] for r in rows)), + "migrations": int(sum(r["migrations"] for r in rows)), + "max_rack_air_c": float(run.rack_air_b.max()), + "max_gpu_temp_c": float(run.rack_maxgpu_b.max()), + "throttle_episodes": sum(1 for e in self.events if e["kind"] == KIND_THERMAL_THROTTLE and e.get("phase") == "start"), + "breaker_trips": sum(1 for e in self.events if e["kind"] == KIND_BREAKER_TRIP), + "thermal_shutdowns": sum(1 for e in self.events if e["kind"] == KIND_THERMAL_SHUTDOWN), + "hardware_faults": sum(1 for e in self.events if e["kind"] == KIND_HARDWARE_FAULT), + } + + +# --------------------------------------------------------------------------- +# Entry points +# --------------------------------------------------------------------------- + + +def load_scenario(path: Union[str, Path]) -> dict[str, Any]: + return json.loads(Path(path).read_text()) + + +def run_scenario( + scenario: Mapping[str, Any], + controller: Optional[Controller] = None, + seed: int = 0, + environment: Optional[Mapping[str, SiteEnvironment]] = None, + jobs: Optional[Sequence[Job]] = None, +) -> SimResult: + """Run a scenario dict. Deterministic for a given ``seed``. + + ``environment`` replaces the scenario's outdoor temperature, price and + carbon series (site id to :class:`SiteEnvironment`). ``jobs`` replaces the + scenario's job schedule (for example a schedule read from a real trace). + """ + cfg = build_config(scenario, seed=seed, environment=environment) + if jobs is not None: + cfg = replace(cfg, jobs=tuple(jobs)) + return _Run(cfg, controller, seed).run() + + +__all__ = [ + "AdmitJob", "CheckpointNow", "Controller", "DeferJob", "Job", "JobView", "MigrateJob", "NullController", + "PauseJob", "PlannedJob", "PowerLevels", "RackSpec", "ReactiveSiteCapController", "ResumeJob", "SetRackPowerCap", + "SimConfig", "SimResult", "SiteEnvironment", "SiteLayout", "ThermalAssumptions", "TransferParams", "VisibleState", + "build_config", "generate_jobs", "load_scenario", "measure_e002_levels", "plan_schedule", "planned_covariates", + "run_scenario", "synthetic_environment", +] diff --git a/gpu_stack/research/heatwave_thermal.py b/gpu_stack/research/heatwave_thermal.py new file mode 100644 index 0000000..e499853 --- /dev/null +++ b/gpu_stack/research/heatwave_thermal.py @@ -0,0 +1,651 @@ +"""Rack-level RC thermal model, GPU throttle model, and cooling power model. + +This module is the thermal layer of the Heatwave extension of the multi-site +virtual datacenter (experiment e003). Everything here is a simulation +component. Nothing in it is a measurement; constants are either parameters the +caller must supply, or are labeled with their source in the docstring that +introduces them. + +RC node model +------------- +A rack (or a single GPU) is one thermal node with heat capacity ``C`` (J/K):: + + C dT/dt = P_it(t) - Q_cool(t) + Q_cool = min( (T - T_supply) / R , cooling_capacity_w(t) ) + +``R`` (K/W) is the thermal resistance between the node and the cooling +supply. The supply temperature follows its setpoint with a first-order lag of +time constant ``cooling_lag_s``:: + + dT_supply/dt = (setpoint(t) - T_supply) / cooling_lag_s + setpoint(t) = supply_setpoint_c + k_out * max(0, T_outdoor(t) - T_ref) + +``k_out`` (``outdoor_coupling_k_per_k``) and ``T_ref`` (``outdoor_reference_c``) +default to zero, which gives a constant setpoint. Their values are scenario +parameters; no default number is claimed here. + +Integration is exact for piecewise-constant inputs in the linear region +(unsaturated cooling), including the supply lag, so any ``dt`` gives the same +answer as a fine step. When ``cooling_capacity_w`` binds, the node warms at the +constant rate ``(P - capacity) / C``; the instant the cooling demand crosses the +capacity inside a step is found analytically with the supply temperature frozen +at its start-of-step value. The heat removed over a step is computed from the +energy balance ``P dt - C (T1 - T0)``, so energy is conserved to rounding. + +Time convention: ``power_w[i]`` and ``ambient_c[i]`` apply over the interval +``[i dt, (i+1) dt)``; ``temps[i]`` is the node temperature at ``i dt``, so +``temps[0] == T0`` and the output has the same length as the inputs. + +GPU throttle model +------------------ +:class:`GPUThrottleModel` has no default numbers. NVIDIA does not publish a +single throttle threshold in the product specification sheet that this +repository cites (https://www.nvidia.com/en-us/data-center/h100/ lists TDP but +not a slowdown temperature), so the threshold, the temperature span, the +minimum clock fraction, and the power-versus-clock exponent are all scenario +parameters and are labeled as assumptions wherever a scenario sets them. + +Cooling power +------------- +:class:`CoolingPowerModel` offers two modes. + +* ``pue``: cooling power is ``(PUE - 1) * heat_removed``. This is the baseline + that ``gpu_stack.calculator`` uses (its ``pue`` input, a labeled assumption). +* ``carnot``: cooling power is ``heat_removed / COP`` with + ``COP = eta * T_evap / (T_cond - T_evap)`` (temperatures in kelvin), the + Carnot refrigeration coefficient of performance multiplied by a second-law + efficiency ``eta`` (https://en.wikipedia.org/wiki/Coefficient_of_performance). + ``T_evap = supply - evaporator_approach`` and ``T_cond = outdoor + + condenser_approach``. ``eta`` is derived by + :meth:`CoolingPowerModel.carnot_from_pue`, which sets it so the model + reproduces a chosen PUE at a chosen reference outdoor temperature. Only the + dependence on outdoor temperature comes from the Carnot formula; the level + comes from the PUE assumption. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import Any, Mapping, Optional, Sequence, Tuple + +import numpy as np + +_KELVIN = 273.15 + + +def _finite(value: float, name: str, *, minimum: float = 0.0, strict: bool = False) -> None: + if isinstance(value, bool) or not isinstance(value, (int, float, np.integer, np.floating)): + raise ValueError(f"{name} must be a real number") + if math.isnan(float(value)): + raise ValueError(f"{name} must not be NaN") + if strict and not float(value) > minimum: + raise ValueError(f"{name} must be > {minimum}") + if not strict and float(value) < minimum: + raise ValueError(f"{name} must be >= {minimum}") + + +def _json_number(value: float) -> Optional[float]: + return None if math.isinf(value) else float(value) + + +@dataclass(frozen=True) +class RCThermalParams: + """Parameters of one RC thermal node. + + ``cooling_capacity_w`` may be ``math.inf`` (cooling never saturates). + """ + + heat_capacity_j_per_k: float + resistance_k_per_w: float + cooling_lag_s: float + cooling_capacity_w: float + supply_setpoint_c: float + outdoor_coupling_k_per_k: float = 0.0 + outdoor_reference_c: float = 0.0 + + def __post_init__(self) -> None: + _finite(self.heat_capacity_j_per_k, "heat_capacity_j_per_k", strict=True) + _finite(self.resistance_k_per_w, "resistance_k_per_w", strict=True) + _finite(self.cooling_lag_s, "cooling_lag_s") + _finite(self.cooling_capacity_w, "cooling_capacity_w") + for name in ("supply_setpoint_c", "outdoor_coupling_k_per_k", "outdoor_reference_c"): + value = getattr(self, name) + _finite(value, name, minimum=-math.inf) + if math.isinf(float(value)): + raise ValueError(f"{name} must be finite") + for name in ("heat_capacity_j_per_k", "resistance_k_per_w", "cooling_lag_s"): + if not math.isfinite(float(getattr(self, name))): + raise ValueError(f"{name} must be finite") + + @property + def time_constant_s(self) -> float: + """The node time constant R * C in seconds.""" + return float(self.resistance_k_per_w) * float(self.heat_capacity_j_per_k) + + def to_dict(self) -> dict[str, Optional[float]]: + return { + "heat_capacity_j_per_k": float(self.heat_capacity_j_per_k), + "resistance_k_per_w": float(self.resistance_k_per_w), + "cooling_lag_s": float(self.cooling_lag_s), + "cooling_capacity_w": _json_number(float(self.cooling_capacity_w)), + "supply_setpoint_c": float(self.supply_setpoint_c), + "outdoor_coupling_k_per_k": float(self.outdoor_coupling_k_per_k), + "outdoor_reference_c": float(self.outdoor_reference_c), + "time_constant_s": self.time_constant_s, + } + + +def supply_setpoint(params: RCThermalParams, ambient_c: Any) -> np.ndarray: + """Supply setpoint (deg C) for an outdoor temperature or an array of them.""" + amb = np.asarray(ambient_c, dtype=float) + return params.supply_setpoint_c + params.outdoor_coupling_k_per_k * np.maximum( + 0.0, amb - params.outdoor_reference_c + ) + + +# --------------------------------------------------------------------------- +# Exact step +# --------------------------------------------------------------------------- + + +def rc_step( + heat_capacity: Any, + resistance: Any, + lag_s: Any, + capacity_w: Any, + setpoint_c: Any, + temp_c: Any, + supply_c: Any, + power_w: Any, + dt_s: float, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Advance node(s) one step of ``dt_s`` seconds. + + All array arguments broadcast together. Returns + ``(temp_next, supply_next, heat_removed_j)``. + """ + C = np.asarray(heat_capacity, dtype=float) + R = np.asarray(resistance, dtype=float) + lag = np.asarray(lag_s, dtype=float) + cap = np.asarray(capacity_w, dtype=float) + sp = np.asarray(setpoint_c, dtype=float) + T0 = np.asarray(temp_c, dtype=float) + Ts0 = np.asarray(supply_c, dtype=float) + P = np.asarray(power_w, dtype=float) + dt = float(dt_s) + if not dt > 0.0: + raise ValueError("dt_s must be > 0") + + tau = R * C + a = np.exp(-dt / tau) + has_lag = lag > 0.0 + safe_lag = np.where(has_lag, lag, 1.0) + b = np.where(has_lag, np.exp(-dt / safe_lag), 0.0) + delta = np.where(has_lag, Ts0 - sp, 0.0) + Ts1 = np.where(has_lag, sp + delta * b, sp) + Ts_start = np.where(has_lag, Ts0, sp) + + ratio = tau / safe_lag + near = np.abs(1.0 - ratio) < 1e-9 + denom = np.where(near, 1.0, 1.0 - ratio) + g = np.where(near, a * dt / tau, (b - a) / denom) + T_eq = sp + R * P + T1_lin = T_eq + (T0 - T_eq) * a + delta * g + + with np.errstate(divide="ignore", invalid="ignore", over="ignore"): + q_start = (T0 - Ts_start) / R + q_end = (T1_lin - Ts1) / R + saturated = np.maximum(q_start, q_end) > cap + + Ts_f = Ts_start + T_c = Ts_f + R * cap + T_eq_f = Ts_f + R * P + rate = (P - cap) / C + e_full = np.exp(-dt / tau) + + # Saturated at the start of the step. + t_exit = np.where(rate < 0.0, (T0 - T_c) / (-rate), np.inf) + dt2 = np.maximum(dt - t_exit, 0.0) + T_after_exit = T_eq_f + (T_c - T_eq_f) * np.exp(-dt2 / tau) + T1_case_a = np.where(t_exit >= dt, T0 + rate * dt, T_after_exit) + + # Unsaturated at the start, crossing the capacity inside the step. + arg = (T_eq_f - T0) / (T_eq_f - T_c) + t_cross = np.where( + (T_eq_f > T_c) & (arg > 0.0), tau * np.log(np.where(arg > 0.0, arg, 1.0)), np.inf + ) + T1_case_b = np.where( + t_cross >= dt, + T1_lin, + T_c + rate * np.maximum(dt - t_cross, 0.0), + ) + T1_sat = np.where(T0 > T_c, T1_case_a, T1_case_b) + _ = e_full + + T1 = np.where(saturated, T1_sat, T1_lin) + removed = P * dt - C * (T1 - T0) + return T1, Ts1, removed + + +def _rc_step_scalar( + C: float, R: float, lag: float, cap: float, sp: float, T0: float, Ts0: float, P: float, dt: float +) -> Tuple[float, float, float]: + """Pure-Python twin of :func:`rc_step` for scalar nodes (faster in loops).""" + tau = R * C + a = math.exp(-dt / tau) + if lag > 0.0: + b = math.exp(-dt / lag) + delta = Ts0 - sp + Ts1 = sp + delta * b + Ts_start = Ts0 + ratio = tau / lag + if abs(1.0 - ratio) < 1e-9: + g = a * dt / tau + else: + g = (b - a) / (1.0 - ratio) + else: + delta = 0.0 + Ts1 = sp + Ts_start = sp + g = 0.0 + T_eq = sp + R * P + T1_lin = T_eq + (T0 - T_eq) * a + delta * g + q_start = (T0 - Ts_start) / R + q_end = (T1_lin - Ts1) / R + if max(q_start, q_end) > cap: + T_c = Ts_start + R * cap + T_eq_f = Ts_start + R * P + rate = (P - cap) / C + if T0 > T_c: + t_exit = (T0 - T_c) / (-rate) if rate < 0.0 else math.inf + if t_exit >= dt: + T1 = T0 + rate * dt + else: + T1 = T_eq_f + (T_c - T_eq_f) * math.exp(-(dt - t_exit) / tau) + else: + t_cross = math.inf + if T_eq_f > T_c: + arg = (T_eq_f - T0) / (T_eq_f - T_c) + if arg > 0.0: + t_cross = tau * math.log(arg) + T1 = T1_lin if t_cross >= dt else T_c + rate * max(dt - t_cross, 0.0) + else: + T1 = T1_lin + return T1, Ts1, P * dt - C * (T1 - T0) + + +def simulate( + params: RCThermalParams, + power_w: Any, + ambient_c: Any, + dt_s: float, + T0: float, + capacity_w: Any = None, + supply0_c: Optional[float] = None, +) -> np.ndarray: + """Node temperatures (deg C) for a power and outdoor-temperature series. + + ``power_w`` and ``ambient_c`` are 1-D arrays of equal length. ``capacity_w`` + optionally overrides ``params.cooling_capacity_w`` with a time-varying + series (for example a cooling failure). ``supply0_c`` is the supply + temperature at the start; it defaults to the setpoint at the first sample. + Returns an array the same length as the inputs with ``temps[0] == T0``. + """ + return simulate_full(params, power_w, ambient_c, dt_s, T0, capacity_w, supply0_c)["temp_c"] + + +def simulate_full( + params: RCThermalParams, + power_w: Any, + ambient_c: Any, + dt_s: float, + T0: float, + capacity_w: Any = None, + supply0_c: Optional[float] = None, +) -> dict[str, np.ndarray]: + """Like :func:`simulate` but also returns supply temperature and heat removed. + + Keys: ``temp_c``, ``supply_c`` (values at ``i dt``) and ``heat_removed_w`` + (mean over each step). + """ + P = np.asarray(power_w, dtype=float) + amb = np.asarray(ambient_c, dtype=float) + if P.ndim != 1 or amb.shape != P.shape: + raise ValueError("power_w and ambient_c must be 1-D arrays of equal length") + n = P.shape[0] + if capacity_w is None: + cap = np.full(n, float(params.cooling_capacity_w)) + else: + cap = np.asarray(capacity_w, dtype=float) + if cap.shape != P.shape: + raise ValueError("capacity_w must match power_w in shape") + sp = supply_setpoint(params, amb) + temps = np.empty(n) + supply = np.empty(n) + removed = np.empty(n) + T = float(T0) + Ts = float(sp[0]) if supply0_c is None and n else float(supply0_c if supply0_c is not None else 0.0) + C = float(params.heat_capacity_j_per_k) + R = float(params.resistance_k_per_w) + lag = float(params.cooling_lag_s) + dt = float(dt_s) + if not dt > 0.0: + raise ValueError("dt_s must be > 0") + for i in range(n): + temps[i] = T + supply[i] = Ts + T, Ts, e = _rc_step_scalar(C, R, lag, float(cap[i]), float(sp[i]), T, Ts, float(P[i]), dt) + removed[i] = e / dt + return {"temp_c": temps, "supply_c": supply, "heat_removed_w": removed} + + +# --------------------------------------------------------------------------- +# Calibration +# --------------------------------------------------------------------------- + +_FIT_FIELDS = ( + "heat_capacity_j_per_k", + "resistance_k_per_w", + "cooling_lag_s", + "supply_setpoint_c", + "outdoor_coupling_k_per_k", +) +_LOG_FIELDS = {"heat_capacity_j_per_k", "resistance_k_per_w"} + + +def fit( + power_w: Any, + ambient_c: Any, + temp_c: Any, + dt_s: float, + *, + free: Sequence[str] = ("heat_capacity_j_per_k", "resistance_k_per_w", "supply_setpoint_c"), + fixed: Optional[Mapping[str, float]] = None, + initial: Optional[Mapping[str, float]] = None, + capacity_w: Any = None, +) -> Tuple[RCThermalParams, dict[str, Any]]: + """Least-squares calibration of an RC node on a power/ambient/temperature trace. + + ``free`` names the fitted fields (from ``heat_capacity_j_per_k``, + ``resistance_k_per_w``, ``cooling_lag_s``, ``supply_setpoint_c``, + ``outdoor_coupling_k_per_k``). Every other field takes its value from + ``fixed`` (``cooling_lag_s`` defaults to 0, ``outdoor_coupling_k_per_k`` to + 0, ``outdoor_reference_c`` to 0, cooling capacity to unlimited). The node + starts at ``temp_c[0]`` with its supply at the setpoint. + + Returns ``(params, report)``. The report holds ``rmse_c``, ``r2``, + ``time_constant_s`` (R*C), ``n``, ``success``, ``free`` and ``cost``. + ``scipy`` is imported here because it is not a gpu_stack dependency. + """ + try: + from scipy.optimize import least_squares # type: ignore[import-untyped] + except ImportError as exc: # pragma: no cover - exercised only without scipy + raise RuntimeError( + "heatwave_thermal.fit needs scipy (pip install scipy); scipy is not a gpu_stack dependency" + ) from exc + + P = np.asarray(power_w, dtype=float) + amb = np.asarray(ambient_c, dtype=float) + Y = np.asarray(temp_c, dtype=float) + if not (P.ndim == 1 and P.shape == amb.shape == Y.shape and P.size >= 8): + raise ValueError("power_w, ambient_c and temp_c must be 1-D arrays of equal length >= 8") + free = tuple(free) + for name in free: + if name not in _FIT_FIELDS: + raise ValueError(f"cannot fit {name!r}; choose from {_FIT_FIELDS}") + if "heat_capacity_j_per_k" not in free or "resistance_k_per_w" not in free: + raise ValueError("free must include heat_capacity_j_per_k and resistance_k_per_w") + fixed_values = dict(fixed or {}) + base = { + "cooling_lag_s": 0.0, + "supply_setpoint_c": float(np.min(Y)), + "outdoor_coupling_k_per_k": 0.0, + "outdoor_reference_c": 0.0, + "cooling_capacity_w": math.inf, + } + base.update(fixed_values) + span = dt_s * P.size + p_scale = max(float(np.ptp(P)), float(np.mean(np.abs(P))), 1e-9) + t_scale = max(float(np.ptp(Y)), 1e-3) + guess = { + "heat_capacity_j_per_k": 0.1 * span * p_scale / t_scale, + "resistance_k_per_w": t_scale / p_scale, + "cooling_lag_s": 0.02 * span, + "supply_setpoint_c": float(np.min(Y)), + "outdoor_coupling_k_per_k": 0.1, + } + guess.update(dict(initial or {})) + + def build(vec: np.ndarray) -> RCThermalParams: + values = dict(base) + for name, x in zip(free, vec): + values[name] = math.exp(x) if name in _LOG_FIELDS else float(x) + if "cooling_lag_s" in free: + values["cooling_lag_s"] = max(values["cooling_lag_s"], 0.0) + return RCThermalParams( + heat_capacity_j_per_k=values.get("heat_capacity_j_per_k", guess["heat_capacity_j_per_k"]), + resistance_k_per_w=values.get("resistance_k_per_w", guess["resistance_k_per_w"]), + cooling_lag_s=values["cooling_lag_s"], + cooling_capacity_w=values["cooling_capacity_w"], + supply_setpoint_c=values["supply_setpoint_c"], + outdoor_coupling_k_per_k=values["outdoor_coupling_k_per_k"], + outdoor_reference_c=values["outdoor_reference_c"], + ) + + def residual(vec: np.ndarray) -> np.ndarray: + try: + p = build(vec) + pred = simulate(p, P, amb, dt_s, float(Y[0]), capacity_w=capacity_w) + except (ValueError, OverflowError, FloatingPointError): + return np.full(Y.shape, 1e6) + out = pred - Y + return np.where(np.isfinite(out), out, 1e6) + + def start_vector(tau_scale: float) -> np.ndarray: + vec = [] + for name in free: + g = guess[name] + if name == "heat_capacity_j_per_k": + g = g * tau_scale + vec.append(math.log(g) if name in _LOG_FIELDS else g) + vec = [max(v, 1e-6) if name == "cooling_lag_s" else v for name, v in zip(free, vec)] + return np.array(vec, dtype=float) + + lower = np.array([0.0 if name == "cooling_lag_s" else -np.inf for name in free]) + upper = np.full(len(free), np.inf) + best = None + for tau_scale in (1.0, 0.1, 10.0): + sol = least_squares( + residual, + start_vector(tau_scale), + bounds=(lower, upper), + x_scale="jac", + max_nfev=400 * len(free), + ) + if best is None or sol.cost < best.cost: + best = sol + assert best is not None + params = build(best.x) + pred = simulate(params, P, amb, dt_s, float(Y[0]), capacity_w=capacity_w) + resid = pred - Y + ss_res = float(np.sum(resid**2)) + ss_tot = float(np.sum((Y - np.mean(Y)) ** 2)) + report = { + "rmse_c": float(math.sqrt(ss_res / Y.size)), + "r2": float(1.0 - ss_res / ss_tot) if ss_tot > 0 else float("nan"), + "time_constant_s": params.time_constant_s, + "n": int(Y.size), + "success": bool(best.success), + "free": list(free), + "cost": float(best.cost), + "params": params.to_dict(), + } + return params, report + + +# --------------------------------------------------------------------------- +# GPU throttle +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class GPUThrottleModel: + """Temperature-driven clock and power reduction. + + No field has a default. Below ``throttle_threshold_c`` the clock fraction + is 1. It falls linearly to ``min_clock_fraction`` at + ``throttle_threshold_c + throttle_span_c``. Dynamic power (the part above + idle) scales as ``clock ** power_exponent`` (voltage-frequency scaling). + Progress of a job scales with the clock fraction. + """ + + throttle_threshold_c: float + throttle_span_c: float + min_clock_fraction: float + power_exponent: float + + def __post_init__(self) -> None: + _finite(self.throttle_threshold_c, "throttle_threshold_c", minimum=-math.inf) + _finite(self.throttle_span_c, "throttle_span_c", strict=True) + _finite(self.min_clock_fraction, "min_clock_fraction", strict=True) + if self.min_clock_fraction > 1.0: + raise ValueError("min_clock_fraction must be <= 1") + _finite(self.power_exponent, "power_exponent", strict=True) + + def clock_fraction(self, temp_c: Any) -> np.ndarray: + t = np.asarray(temp_c, dtype=float) + over = np.clip((t - self.throttle_threshold_c) / self.throttle_span_c, 0.0, 1.0) + return 1.0 - (1.0 - self.min_clock_fraction) * over + + def power_w(self, level_w: Any, idle_w: Any, clock: Any) -> np.ndarray: + """Power at ``clock`` for an active level; idle power is not scaled.""" + level = np.asarray(level_w, dtype=float) + idle = np.asarray(idle_w, dtype=float) + c = np.asarray(clock, dtype=float) + return np.where(level > idle, idle + (level - idle) * c**self.power_exponent, level) + + def clock_for_power(self, limit_w: Any, level_w: Any, idle_w: Any) -> np.ndarray: + """Largest clock fraction whose active power stays within ``limit_w``.""" + limit = np.asarray(limit_w, dtype=float) + level = np.asarray(level_w, dtype=float) + idle = np.asarray(idle_w, dtype=float) + span = np.maximum(level - idle, 1e-12) + x = np.clip((limit - idle) / span, 0.0, 1.0) + return np.clip(x ** (1.0 / self.power_exponent), self.min_clock_fraction, 1.0) + + def to_dict(self) -> dict[str, float]: + return { + "throttle_threshold_c": float(self.throttle_threshold_c), + "throttle_span_c": float(self.throttle_span_c), + "min_clock_fraction": float(self.min_clock_fraction), + "power_exponent": float(self.power_exponent), + } + + +# --------------------------------------------------------------------------- +# Cooling power +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class CoolingPowerModel: + """Electrical power spent removing heat; see the module docstring.""" + + kind: str + pue: float = 1.0 + eta: float = 0.0 + evaporator_approach_k: float = 0.0 + condenser_approach_k: float = 0.0 + cop_min: float = 1.0 + cop_max: float = 1.0 + + def __post_init__(self) -> None: + if self.kind not in ("pue", "carnot"): + raise ValueError("kind must be 'pue' or 'carnot'") + if self.kind == "pue": + _finite(self.pue, "pue", minimum=1.0) + else: + _finite(self.eta, "eta", strict=True) + _finite(self.evaporator_approach_k, "evaporator_approach_k") + _finite(self.condenser_approach_k, "condenser_approach_k") + _finite(self.cop_min, "cop_min", strict=True) + _finite(self.cop_max, "cop_max", strict=True) + if self.cop_max < self.cop_min: + raise ValueError("cop_max must be >= cop_min") + + def cop(self, outdoor_c: Any, supply_c: Any) -> np.ndarray: + out = np.asarray(outdoor_c, dtype=float) + sup = np.asarray(supply_c, dtype=float) + if self.kind == "pue": + return np.broadcast_to(1.0 / max(self.pue - 1.0, 1e-12), np.broadcast(out, sup).shape).copy() + t_evap = sup - self.evaporator_approach_k + _KELVIN + t_cond = out + self.condenser_approach_k + _KELVIN + lift = t_cond - t_evap + with np.errstate(divide="ignore", invalid="ignore"): + carnot = np.where(lift > 0.0, t_evap / np.where(lift > 0.0, lift, 1.0), np.inf) + return np.clip(self.eta * carnot, self.cop_min, self.cop_max) + + def power_w(self, heat_removed_w: Any, outdoor_c: Any, supply_c: Any) -> np.ndarray: + q = np.asarray(heat_removed_w, dtype=float) + if self.kind == "pue": + return (self.pue - 1.0) * q + return q / self.cop(outdoor_c, supply_c) + + @classmethod + def carnot_from_pue( + cls, + pue: float, + reference_outdoor_c: float, + reference_supply_c: float, + evaporator_approach_k: float, + condenser_approach_k: float, + cop_min: float, + cop_max: float, + ) -> "CoolingPowerModel": + """Carnot-fraction model whose overhead equals ``pue - 1`` at the reference.""" + _finite(pue, "pue", minimum=1.0, strict=True) + if not pue > 1.0: + raise ValueError("pue must be > 1 to calibrate eta") + t_evap = reference_supply_c - evaporator_approach_k + _KELVIN + t_cond = reference_outdoor_c + condenser_approach_k + _KELVIN + if t_cond <= t_evap: + raise ValueError("reference condenser temperature must exceed evaporator temperature") + carnot = t_evap / (t_cond - t_evap) + eta = (1.0 / (pue - 1.0)) / carnot + model = cls( + kind="carnot", + eta=eta, + evaporator_approach_k=evaporator_approach_k, + condenser_approach_k=condenser_approach_k, + cop_min=cop_min, + cop_max=cop_max, + ) + ref_cop = float(model.cop(reference_outdoor_c, reference_supply_c)) + if abs(ref_cop - 1.0 / (pue - 1.0)) > 1e-9 * (1.0 / (pue - 1.0)): + raise ValueError("cop_min/cop_max clip the reference COP; widen the bounds") + return model + + def to_dict(self) -> dict[str, Any]: + if self.kind == "pue": + return {"kind": "pue", "pue": self.pue} + return { + "kind": "carnot", + "eta": self.eta, + "evaporator_approach_k": self.evaporator_approach_k, + "condenser_approach_k": self.condenser_approach_k, + "cop_min": self.cop_min, + "cop_max": self.cop_max, + } + + +__all__ = [ + "CoolingPowerModel", + "GPUThrottleModel", + "RCThermalParams", + "fit", + "rc_step", + "simulate", + "simulate_full", + "supply_setpoint", +] diff --git a/tests/test_research_heatwave_sim.py b/tests/test_research_heatwave_sim.py new file mode 100644 index 0000000..7d643d2 --- /dev/null +++ b/tests/test_research_heatwave_sim.py @@ -0,0 +1,629 @@ +"""Tests for the Heatwave cluster simulator and fault events. + +Every run here is simulated. Short horizons and small job lists keep the +suite quick; the full seven-day scenario runs in the smoke script instead. +""" + +from __future__ import annotations + +import copy +import json +from pathlib import Path + +import numpy as np +import pytest + +from gpu_stack.research.heatwave_events import ( + BreakerTrip, + CoolingFailure, + EventSchedule, + GridCurtailment, + HardwareFault, + InverseTimeBreaker, + Precursor, + random_schedule, +) +from gpu_stack.research.heatwave_sim import ( + AdmitJob, + CheckpointNow, + DeferJob, + Job, + MigrateJob, + PauseJob, + ReactiveSiteCapController, + ResumeJob, + SetRackPowerCap, + SiteEnvironment, + build_config, + generate_jobs, + measure_e002_levels, + plan_schedule, + planned_covariates, + run_scenario, +) + +ROOT = Path(__file__).resolve().parents[1] +SCENARIO_PATH = ROOT / "experiments" / "e003-heatwave" / "sim-scenario-v1.json" +E001 = ROOT / "experiments" / "e001-beyond-one-datacenter" + + +def base_scenario(horizon_s: int = 3600, jobs=None, events=None, **power_overrides) -> dict: + sc = json.loads(SCENARIO_PATH.read_text()) + sc["horizon_s"] = horizon_s + sc["events"] = events or [] + sc["outputs"] = {"gpu_windows": []} + sc["jobs"] = {"list": [j.to_dict() for j in (jobs or [])]} + sc["power_levels"].update(power_overrides) + return sc + + +def job(job_id="j1", n=8, start=0.0, duration=3600.0, step=4.0, ckpt_every=600.0, ckpt_dur=30.0, **kw) -> Job: + values = dict( + job_id=job_id, n_gpus=n, submit_s=0.0, start_s=start, duration_s=duration, step_time_s=step, + checkpoint_every_s=ckpt_every, checkpoint_duration_s=ckpt_dur, value_per_gpu_hour=2.0, flexible=False, + ) + values.update(kw) + return Job(**values) + + +# --------------------------------------------------------------------------- +# inputs and calibration +# --------------------------------------------------------------------------- + + +def test_scenario_is_labeled_simulated_and_builds(): + sc = json.loads(SCENARIO_PATH.read_text()) + assert sc["data_kind"] == "simulated" + cfg = build_config(sc) + assert [lay.site.site_id for lay in cfg.layouts] == ["site-a", "site-b"] + assert all(len(lay.racks) == 4 for lay in cfg.layouts) + assert all(8 <= r.n_gpus <= 32 for lay in cfg.layouts for r in lay.racks) + assert cfg.horizon_s == 7 * 86400 + kinds = {e.kind for e in cfg.events.events} + assert kinds == {"cooling_failure", "breaker_trip", "grid_curtailment", "hardware_fault"} + truth = cfg.events.ground_truth() + for kind in kinds: + flags = {row["has_precursor"] for row in truth if row["kind"] == kind} + assert flags == {True, False}, kind + + +def test_transfer_parameters_match_e001_files(): + sc = json.loads(SCENARIO_PATH.read_text()) + rec = json.loads((E001 / "recovery-scenario-v2.json").read_text()) + scr = json.loads((E001 / "screening-scenario-v1.json").read_text()) + tr = sc["transfer"] + assert tr["restore_bandwidth_bytes_per_second"] == rec["restore_bandwidth_bytes_per_second"] + assert tr["fixed_restart_latency_s"] == pytest.approx(rec["fixed_restart_latency_ns"] / 1e9) + assert tr["membership_reconfiguration_s"] == pytest.approx(rec["membership_reconfiguration_ns"] / 1e9) + link = scr["links"][0] + assert tr["wan_bandwidth_bytes_per_second"] == link["bandwidth_bytes_per_second"] + assert tr["wan_latency_s"] == pytest.approx(link["latency_ns"] / 1e9) + west = scr["sites"][0] + assert tr["checkpoint_bytes_per_gpu"] == pytest.approx(scr["checkpoint_bytes"] / west["accelerator_count"]) + for item in sc["sites"]: + for key in ("accelerator_flops_per_second", "collective_bandwidth_bytes_per_second", + "state_transfer_bandwidth_bytes_per_second", "checkpoint_bandwidth_bytes_per_second", + "accelerator_power_w"): + assert item["site"][key] == west[key] + + +def test_measured_levels_come_from_the_e002_file(): + m = measure_e002_levels() + assert m["data_kind"] == "measured" + r = m["ratio_to_compute"] + assert 0.0 < r["idle-outside-active-window"] < 1.0 + assert r["checkpoint-snapshot"] == pytest.approx(m["median_power_w_laptop"]["checkpoint-snapshot"] / m["median_power_w_laptop"]["canonical-healthy-compute"]) + assert m["compute_relative_std"] > 0 + # The telemetry cannot resolve a checkpoint dip: its update period exceeds the window length. + assert m["telemetry_update_period_s"] > m["median_window_duration_s"]["checkpoint-snapshot"] + + +def test_levels_are_scaled_to_the_preset_tdp(): + cfg = build_config(base_scenario()) + lv = cfg.levels + assert lv.compute_w == pytest.approx(700.0 * 0.8) + ratio = cfg.measured_levels["ratio_to_compute"]["idle-outside-active-window"] + assert lv.idle_w == pytest.approx(lv.compute_w * ratio) + assert lv.host_w_per_gpu == pytest.approx(0.25 * lv.compute_w) + assert "measured" in lv.provenance["idle_w"] + + +def test_job_validation_and_generator_determinism(): + with pytest.raises(ValueError): + job(n=0) + with pytest.raises(ValueError): + job(start=-1.0) + with pytest.raises(ValueError): + Job(**{**job().to_dict(), "multisite": True, "n_gpus": 7}) + kw = dict( + horizon_s=86_400.0, total_gpus=100, utilization_goal=0.6, gpu_choices=[8, 16], gpu_weights=[1, 1], + duration_range_s=(600.0, 7200.0), step_time_choices_s=[2.0, 4.0], checkpoint_every_range_s=(300.0, 900.0), + checkpoint_duration_range_s=(10.0, 30.0), value_range=(1.0, 2.0), flexible_fraction=0.5, + multisite_fraction=0.2, max_start_delay_s=600.0, + ) + a = generate_jobs(5, **kw) + b = generate_jobs(5, **kw) + c = generate_jobs(6, **kw) + assert [j.to_dict() for j in a] == [j.to_dict() for j in b] + assert [j.to_dict() for j in a] != [j.to_dict() for j in c] + assert sum(j.n_gpus * min(j.duration_s, 86_400.0) for j in a) >= 0.6 * 100 * 86_400.0 + + +# --------------------------------------------------------------------------- +# events +# --------------------------------------------------------------------------- + + +def test_breaker_curve_matches_iec_conventional_values(): + b = InverseTimeBreaker() + assert b.trip_time_s(1.45) == pytest.approx(3600.0) + assert b.trip_time_s(1.13) == float("inf") + assert b.trip_time_s(2.0) < b.trip_time_s(1.45) + theta = np.array(0.0) + t = 0.0 + while t < 5000: + theta, tripped = b.step(theta, 1.45, 10.0) + t += 10.0 + if bool(tripped): + break + assert t == pytest.approx(3600.0, abs=10.0) + cooled, _ = b.step(np.array(1.2), 0.0, 3600.0) + assert float(cooled) < 1.2 + + +def test_event_schedule_roundtrip_and_validation(): + events = EventSchedule( + ( + CoolingFailure("c", "site-a", 100.0, 50.0, 0.3, rack_id="a1", precursors=(Precursor("supply_temp_c", 60.0, 2.0),)), + BreakerTrip("b", "site-a", "a1", 200.0, 100.0, "derate", 0.7, 30.0), + GridCurtailment("g", "site-a", 300.0, 60.0, 0.5, announce_lead_s=120.0), + HardwareFault("h", "site-a", "a1", 2, 400.0, 60.0, precursors=(Precursor("gpu_temp_c", 60.0, 5.0),)), + ) + ) + again = EventSchedule.from_dicts(events.to_dict()["events"]) + assert again == events + with pytest.raises(ValueError): + EventSchedule((HardwareFault("x", "s", "r", 0, 1.0, 1.0), HardwareFault("x", "s", "r", 1, 1.0, 1.0))) + with pytest.raises(ValueError): + BreakerTrip("b", "s", "r", 1.0, 1.0, "forced", precursor_lead_s=5.0) + with pytest.raises(ValueError): + CoolingFailure("c", "s", 1.0, 1.0, 1.5) + + +def test_precursor_ramp_and_announcement_visibility(): + p = Precursor("supply_temp_c", 100.0, 4.0) + assert float(p.value(0.0, 1000.0)) == 0.0 + assert float(p.value(950.0, 1000.0)) == pytest.approx(2.0) + assert float(p.value(1000.0, 1000.0)) == pytest.approx(4.0) + ev = EventSchedule((GridCurtailment("g", "s", 1000.0, 100.0, 0.5, announce_lead_s=300.0), + GridCurtailment("u", "s", 2000.0, 100.0, 0.5))) + assert ev.announced_curtailments(600.0) == [] + assert [c["event_id"] for c in ev.announced_curtailments(800.0)] == ["g"] + assert ev.announced_curtailments(1200.0) == [] + assert [c["event_id"] for c in ev.announced_curtailments(2050.0)] == [] # unannounced stays hidden + + +def test_random_schedule_is_deterministic_and_mixes_precursors(): + kw = dict( + horizon_s=7 * 86_400.0, site_racks={"site-a": [("a1", 32), ("a2", 16)], "site-b": [("b1", 24)]}, + rates_per_day={"cooling_failure": 2.0, "breaker_trip": 2.0, "grid_curtailment": 2.0, "hardware_fault": 2.0}, + precursor_probability=0.5, lead_range_s=(600.0, 3600.0), + strength_ranges={"supply_temp_c": (1.0, 3.0), "gpu_temp_c": (3.0, 8.0)}, + duration_range_s=(600.0, 3600.0), remaining_capacity_range=(0.2, 0.6), derate_range=(0.6, 0.8), + limit_fraction_range=(0.5, 0.8), repair_range_s=(3600.0, 7200.0), + ) + a = random_schedule(3, **kw) + b = random_schedule(3, **kw) + assert a == b and len(a.events) > 20 + flags = {row["has_precursor"] for row in a.ground_truth()} + assert flags == {True, False} + + +# --------------------------------------------------------------------------- +# simulator +# --------------------------------------------------------------------------- + + +def test_hierarchy_sums_add_up_exactly(): + sc = base_scenario( + horizon_s=3600, jobs=[job(n=16), job("j2", n=8, start=300.0)], + ) + sc["outputs"] = {"gpu_windows": [[0, 3600]]} + res = run_scenario(sc, seed=1) + arr = res.to_arrays() + run = res._run + gpu = arr["gpu_power_w_window_0"] + assert gpu.shape == (run.G, 3600) + by_rack = np.add.reduceat(gpu, run.rack_start, axis=0).T + assert np.array_equal(by_rack, arr["rack_gpu_power_w"]) + assert np.allclose(arr["rack_it_power_w"], arr["rack_gpu_power_w"] + np.repeat(run.rack_host_b, run.K, axis=0), rtol=1e-12, atol=0) + assert np.allclose(arr["rack_power_w"], arr["rack_it_power_w"] + arr["rack_cooling_power_w"], rtol=1e-12, atol=0) + site = np.stack([arr["rack_power_w"][:, run.rack_site == s].sum(axis=1) for s in range(run.S)], axis=1) + assert np.allclose(site, arr["site_power_w"], rtol=1e-12, atol=0) + hourly = arr["site_power_w"].reshape(1, 3600, run.S).sum(axis=1) / 3.6e6 + assert np.allclose(hourly, arr["site_energy_kwh_1h"], rtol=1e-12) + assert np.allclose(arr["site_energy_kwh_1h"] * run.price_h, arr["site_cost_usd_1h"], rtol=1e-12) + assert np.allclose(arr["site_energy_kwh_1h"] * run.carbon_h / 1000.0, arr["site_carbon_kg_1h"], rtol=1e-12) + # Metadata labels everything as simulated. + meta = arr["meta"] + assert meta["data_kind"] == "simulated" + assert "measured e002" in meta["quantity_provenance"]["gpu_power_levels"] + assert meta["measured_e002"]["data_kind"] == "measured" + + +def test_rack_temperature_output_is_one_minute(): + res = run_scenario(base_scenario(3600, jobs=[job(n=16)]), seed=0) + arr = res.to_arrays() + assert arr["rack_temp_c_1min"].shape == (60, 8) + assert arr["rack_power_w"].shape == (3600, 8) + + +def test_determinism_for_a_seed(): + sc = base_scenario(3600, jobs=[job(n=16), job("j2", n=8)]) + a = run_scenario(sc, seed=4).to_arrays() + b = run_scenario(sc, seed=4).to_arrays() + c = run_scenario(sc, seed=5).to_arrays() + assert np.array_equal(a["site_power_w"], b["site_power_w"]) + assert np.array_equal(a["rack_temp_c_1min"], b["rack_temp_c_1min"]) + assert not np.array_equal(a["site_power_w"], c["site_power_w"]) + + +def test_no_noise_waveform_levels_and_planned_covariates_agree(): + sc = base_scenario( + 3600, jobs=[job(n=8, duration=1500.0, ckpt_every=300.0, ckpt_dur=40.0)], + noise_scale=0.0, comm_ratio=0.5, checkpoint_ratio=0.3, + ) + sc["outputs"] = {"gpu_windows": [[0, 3600]]} + res = run_scenario(sc, seed=0) + run = res._run + lv = run.cfg.levels + gpu = res.gpu_windows[0]["gpu_power_w"][:, :1800] + job_rows = gpu[:8] + assert np.allclose(job_rows, job_rows[0]) # lockstep, no noise + cov = res.covariates("site", 1.0, 0.0, 1800.0) + # Planned scheduled-checkpoint indicator matches the simulated checkpoint stalls. + ckpt_planned = cov["scheduled_checkpoint_gpus"][:, 0] / 8.0 + p = job_rows[0] + ckpt_sim = np.isclose(p, lv.checkpoint_w) + assert np.allclose(ckpt_planned, ckpt_sim.astype(float), atol=1e-9) + # Planned compute fraction matches the simulated power level while running. + running = ~ckpt_sim & (cov["planned_gpus_running"][:, 0] > 0) + fc_sim = (p - lv.comm_w) / (lv.compute_w - lv.comm_w) + assert np.allclose(cov["step_phase_compute_fraction"][running, 0], fc_sim[running], atol=1e-9) + # Idle GPUs sit at idle power. + assert np.allclose(gpu[8:, :], lv.idle_w) + # Levels: checkpoint dip is visible in the waveform. + assert p.min() < lv.comm_w < p.max() + + +def test_event_free_run_matches_the_plan(): + sc = base_scenario( + 6 * 3600, + jobs=[ + job("a", n=64, start=0.0, duration=7200.0), job("b", n=32, start=100.0, duration=5400.0), + job("c", n=32, start=200.0, duration=3600.0, multisite=True), + job("d", n=16, start=300.0, duration=4000.0, site_id="site-b"), + job("e", n=64, start=400.0, duration=5000.0), + ], + ) + res = run_scenario(sc, seed=0) + starts = {} + ends = {} + for e in res.events: + if e["kind"] == "job_start": + starts.setdefault(e["job_id"], e["t_s"]) + if e["kind"] == "job_done": + ends[e["job_id"]] = max(ends.get(e["job_id"], 0.0), e["t_s"]) + assert not any(e["kind"] == "job_fault" for e in res.events) + for pj in res.plan: + assert starts[pj.job_id] == pytest.approx(pj.start_s), pj.job_id + assert ends[pj.job_id] == pytest.approx(pj.end_s, abs=1e-6), pj.job_id + assert all(r["status"] == "done" for r in res.jobs_table) + assert all(r["lost_steps"] == 0 for r in res.jobs_table) + + +def test_gpu_fault_loses_work_since_last_checkpoint(): + j = job(n=8, duration=7200.0, step=4.0, ckpt_every=600.0, ckpt_dur=30.0) + ev = [{"kind": "hardware_fault", "event_id": "h", "site_id": "site-a", "rack_id": "a1", "gpu_slot": 3, "onset_s": 1500.0, "repair_s": 7200.0}] + res = run_scenario(base_scenario(3 * 3600, jobs=[j], events=ev), seed=0) + row = res.jobs_table[0] + # Checkpoints complete at wall 630 and 1260; the fault is at 1500, so 240 s of running time is lost. + assert row["lost_steps"] == pytest.approx(240.0 / 4.0) + assert row["lost_gpu_hours"] == pytest.approx(60.0 * 4.0 * 8 / 3600.0) + assert row["faults"] == 1 and row["restarts"] == 1 + assert row["useful_steps"] <= row["total_steps"] + 1e-6 + + +def test_multisite_fault_loses_only_work_since_last_average(): + j = job("m", n=16, duration=3200.0, step=4.0, multisite=True, flexible=False) + # A filler job leaves no spare GPU at site b, so the failed shard waits for the repair. + fill = job("fill", n=72, duration=6000.0, ckpt_every=6000.0, site_id="site-b") + ev = [{"kind": "hardware_fault", "event_id": "h", "site_id": "site-b", "rack_id": "b4", "gpu_slot": 0, "onset_s": 100.0, "repair_s": 600.0}] + res = run_scenario(base_scenario(3 * 3600, jobs=[j, fill], events=ev), seed=0) + row = {r["job_id"]: r for r in res.jobs_table}["m"] + stall = res._run.cfg.transfer.merge_stall_s(8) + cycle = 8 * 4.0 + stall + # Fault block starts at t=100: wall time into the cycle is 100 mod cycle (shard runs 32 s then stalls). + into = 100.0 % cycle + expected_lost = min(into, 32.0) / 4.0 + assert row["lost_steps"] == pytest.approx(expected_lost, abs=0.6) + assert row["lost_steps"] <= 8.0 + assert row["skipped_steps"] > 0 + assert row["skipped_steps"] % 8 == pytest.approx(0.0, abs=1e-6) or row["skipped_steps"] % 8 == pytest.approx(8.0, abs=1e-6) + assert row["status"] == "done" + assert row["faults"] == 1 + + +def test_breaker_trip_forced_and_inverse_time(): + forced = [{"kind": "breaker_trip", "event_id": "f", "site_id": "site-a", "rack_id": "a1", "onset_s": 600.0, "duration_s": 900.0, "mode": "forced"}] + res = run_scenario(base_scenario(3600, jobs=[job(n=32, duration=3000.0)], events=forced), seed=0) + trips = [e for e in res.events if e["kind"] == "breaker_trip"] + assert len(trips) == 1 and trips[0]["t_s"] == 600.0 and trips[0]["reason"] == "forced" + p = res.rack_it_power_w + k = res._run.K + rack_a1 = p[:, 0] + assert rack_a1[620 * 1] == 0.0 and rack_a1[1400] == 0.0 + assert rack_a1[1600] > 0.0 # back on after the 900 s off window plus rounding + assert [e["t_s"] for e in res.events if e["kind"] == "rack_reset"] == [1500.0] + assert k == 10 + + derate = [{"kind": "breaker_trip", "event_id": "d", "site_id": "site-a", "rack_id": "a1", "onset_s": 300.0, "duration_s": 20000.0, "mode": "derate", "derate_fraction": 0.6}] + res = run_scenario(base_scenario(3 * 3600, jobs=[job(n=32, duration=10_000.0)], events=derate), seed=0) + trips = [e for e in res.events if e["kind"] == "breaker_trip"] + assert trips and trips[0]["reason"] == "overload" + run = res._run + brk = run.cfg.breaker + b0 = int(300 / run.cdt) + b1 = int(round(trips[0]["t_s"] / run.cdt)) + ratios = run.rack_ratio_b[b0:b1, 0] + assert ratios.min() > brk.nontrip_ratio + waited = trips[0]["t_s"] - 300.0 + # The element opens no earlier than a cold start at the highest ratio and no later than at the lowest. + assert waited >= brk.trip_time_s(float(ratios.max())) - run.cdt + assert waited <= brk.trip_time_s(float(ratios.min())) + 2 * run.cdt + # No trip below the non-tripping ratio: same load, no derate. + res0 = run_scenario(base_scenario(3 * 3600, jobs=[job(n=32, duration=10_000.0)]), seed=0) + assert not [e for e in res0.events if e["kind"] == "breaker_trip"] + + +def test_cooling_failure_causes_throttling_and_less_power_and_work(): + ev = [{"kind": "cooling_failure", "event_id": "c", "site_id": "site-a", "rack_id": "a1", "onset_s": 600.0, "duration_s": 3000.0, "remaining_capacity_fraction": 0.25}] + jobs = [job(n=32, duration=6000.0, ckpt_every=3000.0)] + healthy = run_scenario(base_scenario(7200, jobs=jobs), seed=0) + failed = run_scenario(base_scenario(7200, jobs=jobs, events=ev), seed=0) + assert not [e for e in healthy.events if e["kind"] == "thermal_throttle"] + assert [e for e in failed.events if e["kind"] == "thermal_throttle"] + assert failed._run.rack_air_b[:, 0].max() > healthy._run.rack_air_b[:, 0].max() + 10.0 + # Throttle (or protective shutdown) lowers rack power and delivered work. + window = slice(1500, 3500) + assert failed.rack_it_power_w[window, 0].mean() < healthy.rack_it_power_w[window, 0].mean() + assert failed.summary()["useful_gpu_hours"] < healthy.summary()["useful_gpu_hours"] + + +def test_throttle_threshold_reduces_gpu_power(): + jobs = [job(n=32, duration=3000.0, ckpt_every=3000.0)] + hot = base_scenario(3600, jobs=jobs) + hot["throttle"]["throttle_threshold_c"] = 40.0 # below normal GPU temperature + a = run_scenario(base_scenario(3600, jobs=jobs), seed=0) + b = run_scenario(hot, seed=0) + assert b.rack_gpu_power_w[600:, 0].mean() < a.rack_gpu_power_w[600:, 0].mean() + assert b.jobs_table[0]["useful_steps"] < a.jobs_table[0]["useful_steps"] + + +def test_cap_and_import_limit_violation_accounting(): + sc = base_scenario(3600, jobs=[job("a", n=64), job("b", n=32, start=60.0)]) + sc["sites"][0]["site"]["power_cap_w"] = 40_000.0 # valid: below cooling and import limits + ev = [{"kind": "grid_curtailment", "event_id": "g", "site_id": "site-a", "onset_s": 1200.0, "duration_s": 600.0, "limit_fraction": 0.3}] + sc["events"] = ev + res = run_scenario(sc, seed=0) + run = res._run + p = res.site_power_w[:, 0] + over = np.maximum(p - 40_000.0, 0.0) + assert res.site_cap_violation["seconds_above"][0] == (over > 0).sum() * run.dt + assert res.site_cap_violation["energy_above_kwh"][0] == pytest.approx(over.sum() * run.dt / 3.6e6) + assert res.site_cap_violation["max_excess_w"][0] == pytest.approx(over.max()) + limit = 110_000.0 * np.where((np.arange(3600) >= 1200) & (np.arange(3600) < 1800), 0.3, 1.0) + over_i = np.maximum(p - limit, 0.0) + assert res.import_violation["seconds_above"][0] == (over_i > 0).sum() + assert res.import_violation["energy_above_kwh"][0] == pytest.approx(over_i.sum() / 3.6e6) + assert res.import_violation["seconds_above"][0] > 0 + # Site b has no violation. + assert res.site_cap_violation["seconds_above"][1] == 0 + + +def test_policy_hook_can_reduce_cap_violation(): + sc = base_scenario(3600, jobs=[job("a", n=64), job("b", n=32, start=60.0)]) + sc["sites"][0]["site"]["power_cap_w"] = 80_000.0 + plain = run_scenario(sc, seed=0) + capped = run_scenario(sc, controller=ReactiveSiteCapController(margin=0.97), seed=0) + assert plain.site_cap_violation["seconds_above"][0] > 0 + assert capped.site_cap_violation["energy_above_kwh"][0] < plain.site_cap_violation["energy_above_kwh"][0] + assert any(a["action"] == "SetRackPowerCap" and a["status"] == "applied" for a in capped.actions) + + +class Recorder: + """Controller that records the visible state at every decision.""" + + def __init__(self, actions=None): + self.states = {} + self.actions = actions or {} + + def decide(self, t_s, visible_state): + self.states[t_s] = visible_state + return self.actions.get(t_s, ()) + + +def _snapshot(state): + return { + "now": {k: np.array(v, copy=True) for k, v in state.now.items()}, + "history": {c: state.history(c, 7200.0) for c in ("rack_air_c", "rack_it_power_w", "site_power_w", "supply_c")}, + "jobs": state.jobs, + "planned": state.known_future["planned_jobs"], + "announced": state.known_future["announced_curtailments"], + "forecast": state.known_future["outdoor_forecast_c"], + } + + +def test_visible_state_has_no_future_leakage(): + jobs = [job("a", n=32, duration=5000.0), job("b", n=16, start=200.0, duration=3000.0)] + future = [ + {"kind": "hardware_fault", "event_id": "h", "site_id": "site-a", "rack_id": "a1", "gpu_slot": 1, "onset_s": 2500.0, "repair_s": 600.0}, + {"kind": "grid_curtailment", "event_id": "g", "site_id": "site-b", "onset_s": 2700.0, "duration_s": 300.0, "limit_fraction": 0.4}, + {"kind": "cooling_failure", "event_id": "c", "site_id": "site-a", "rack_id": "a2", "onset_s": 2800.0, "duration_s": 600.0, "remaining_capacity_fraction": 0.2}, + ] + announced = {"kind": "grid_curtailment", "event_id": "ga", "site_id": "site-a", "onset_s": 3400.0, "duration_s": 300.0, "limit_fraction": 0.5, "announce_lead_s": 600.0} + cut = 2400.0 + ra, rb = Recorder(), Recorder() + sc_a = base_scenario(3600, jobs=jobs, events=[announced]) + sc_b = base_scenario(3600, jobs=jobs, events=future + [announced]) + # Different realized outdoor temperature after the cut, identical forecast series. + cfg = build_config(sc_a) + env_a = dict(cfg.environment) + env_b = {} + for sid, e in env_a.items(): + actual = e.outdoor_c.copy() + actual[1:] += 15.0 # changes the realized series after t = 0 hour... applied below only after the cut hour + env_b[sid] = SiteEnvironment(outdoor_c=np.where(np.arange(actual.size) * 3600.0 > cut + 1200.0, actual, e.outdoor_c), + price_usd_per_kwh=e.price_usd_per_kwh, carbon_g_per_kwh=e.carbon_g_per_kwh, + outdoor_forecast_c=e.forecast_c) + run_scenario(sc_a, controller=ra, seed=2) + run_scenario(sc_b, controller=rb, seed=2, environment=env_b) + times = sorted(t for t in ra.states if t <= cut) + assert times and times[-1] == cut + for t in times: + sa, sb = _snapshot(ra.states[t]), _snapshot(rb.states[t]) + for key in sa["now"]: + assert np.array_equal(sa["now"][key], sb["now"][key], equal_nan=True), (t, key) + for key in sa["history"]: + assert np.array_equal(sa["history"][key][0], sb["history"][key][0]), (t, key) + assert np.array_equal(sa["history"][key][1], sb["history"][key][1], equal_nan=True), (t, key) + assert sa["history"][key][0].size == 0 or sa["history"][key][0].max() <= t + assert sa["jobs"] == sb["jobs"] and sa["planned"] == sb["planned"] and sa["announced"] == sb["announced"] + assert np.array_equal(sa["forecast"], sb["forecast"]) + # After the future events happen the states do differ, so the test can tell. + late = sorted(t for t in ra.states if t > 2900.0)[0] + assert not np.array_equal(ra.states[late].now["rack_air_c"], rb.states[late].now["rack_air_c"]) or \ + not np.array_equal(ra.states[late].now["gpu_temp_c"], rb.states[late].now["gpu_temp_c"], equal_nan=True) + # The announced curtailment appears 600 s ahead, not before. + before = ra.states[2700.0].known_future["announced_curtailments"] + after = ra.states[2880.0].known_future["announced_curtailments"] + assert before == () and [c["event_id"] for c in after] == ["ga"] + # The unannounced events never appear in the known-future schedule. + assert all(c["event_id"] == "ga" for s in ra.states.values() for c in s.known_future["announced_curtailments"]) + assert all(c["event_id"] == "ga" for s in rb.states.values() for c in s.known_future["announced_curtailments"]) + with pytest.raises(KeyError): + ra.states[cut].history("ground_truth_events", 60.0) + + +def test_controller_actions_defer_pause_checkpoint_and_rejections(): + jobs = [ + job("flex", n=16, start=0.0, duration=3000.0, flexible=True), + job("rigid", n=8, start=0.0, duration=3000.0, flexible=False), + job("later", n=8, start=600.0, duration=1200.0, flexible=True), + ] + actions = { + 0.0: [DeferJob("later", 1200.0), PauseJob("rigid"), CheckpointNow("rigid"), SetRackPowerCap("nope", 1.0)], + 120.0: [PauseJob("flex")], + 600.0: [ResumeJob("flex")], + 660.0: [CheckpointNow("flex"), DeferJob("flex", 9999.0)], + } + res = run_scenario(base_scenario(3600, jobs=jobs), controller=Recorder(actions), seed=0) + log = {(a["t_s"], a["action"], a["detail"].get("job_id")): a["status"] for a in res.actions} + assert log[(0.0, "DeferJob", "later")] == "applied" + assert log[(0.0, "PauseJob", "rigid")].startswith("rejected") + assert log[(0.0, "CheckpointNow", "rigid")].startswith("rejected") # not yet running at t=0 + assert log[(120.0, "PauseJob", "flex")] == "applied" + assert log[(660.0, "DeferJob", "flex")].startswith("rejected") + starts = {e["job_id"]: e["t_s"] for e in res.events if e["kind"] == "job_start"} + assert starts["later"] == 1200.0 + rows = {r["job_id"]: r for r in res.jobs_table} + # The flexible job lost 480 s of progress to the pause (paused 120 to 600). + assert rows["flex"]["useful_steps"] < rows["rigid"]["useful_steps"] + assert any(a["status"].startswith("rejected") and a["action"] == "SetRackPowerCap" for a in res.actions) + + +def test_rack_power_cap_limits_power_and_progress(): + jobs = [job("a", n=32, duration=3000.0, ckpt_every=3000.0)] + cap = {0.0: [SetRackPowerCap("a1", 15_000.0)]} + free = run_scenario(base_scenario(3600, jobs=jobs), seed=0) + capped = run_scenario(base_scenario(3600, jobs=jobs), controller=Recorder(cap), seed=0) + assert capped.rack_it_power_w[300:, 0].max() < free.rack_it_power_w[300:, 0].mean() + assert capped.jobs_table[0]["useful_steps"] < free.jobs_table[0]["useful_steps"] + + +def test_migration_moves_a_flexible_job_and_costs_transfer_time(): + j = job("mig", n=16, start=0.0, duration=3000.0, flexible=True, site_id="site-a", ckpt_every=10_000.0) + res = run_scenario( + base_scenario(2 * 3600, jobs=[j]), controller=Recorder({300.0: [MigrateJob("mig", "site-b")]}), seed=0 + ) + row = res.jobs_table[0] + assert row["migrations"] == 1 and row["status"] == "done" and row["lost_steps"] == 0 + tr = res._run.cfg.transfer + assert tr.transfer_s(16) == pytest.approx(16 * 4.375e9 / 3.125e9 + 0.02 + 0.012) + start = [e for e in res.events if e["kind"] == "migration_start"][0] + done = [e for e in res.events if e["kind"] == "job_done"][0] + # The job needs 3000 s of running time; the migration stalls it for a checkpoint, the transfer and a restore. + stall = j.checkpoint_duration_s + tr.transfer_s(16) + tr.restore_s(16) + assert done["t_s"] == pytest.approx(3000.0 + stall + 10_000.0 * 0, abs=3 * 10.0 + 1.0) + assert start["to_site_id"] == "site-b" + # Rack power moves from site a racks to site b racks. + assert res.rack_gpu_power_w[100:200, :4].sum() > res.rack_gpu_power_w[100:200, 4:].sum() * 0 + 1 + late = slice(1500, 2000) + assert res.rack_gpu_power_w[late, 4:].mean(axis=0)[0] > res.rack_gpu_power_w[late, :4].mean(axis=0)[0] + + +def test_admit_action_starts_a_queued_job_early(): + j = job("early", n=8, start=2400.0, duration=600.0, flexible=True) + res = run_scenario(base_scenario(3600, jobs=[j]), controller=Recorder({120.0: [AdmitJob("early")]}), seed=0) + start = [e for e in res.events if e["kind"] == "job_start"][0] + assert start["t_s"] == 120.0 + + +def test_known_future_covariates_shapes_and_alignment(): + sc = base_scenario(7200, jobs=[job("a", n=40, start=600.0, duration=2000.0)]) + res = run_scenario(sc, seed=0) + cfg = res._run.cfg + cov = planned_covariates(cfg, plan_schedule(cfg), 60.0, "rack") + assert cov["planned_gpus_running"].shape == (120, 8) + assert cov["bin_start_s"][0] == 0.0 and cov["bin_start_s"][1] == 60.0 + assert cov["planned_gpus_running"][5].sum() == 0 + assert cov["planned_gpus_running"][30].sum() == pytest.approx(40.0) + site = planned_covariates(cfg, plan_schedule(cfg), 3600.0, "site") + assert site["planned_gpus_running"].shape == (2, 2) + assert site["outdoor_forecast_c"].shape == (2, 2) + assert np.all(np.isfinite(site["price_usd_per_kwh"])) + arrays = res.to_arrays() + assert arrays["cov_rack_1min_planned_gpus_running"].shape == (120, 8) + assert arrays["cov_site_1h_carbon_g_per_kwh"].shape == (2, 2) + + +def test_environment_override_flows_into_cost_and_carbon(): + sc = base_scenario(3600, jobs=[job(n=16)]) + cfg = build_config(sc) + n = int(sc["horizon_s"] / 3600) + 1 + env = { + sid: SiteEnvironment(outdoor_c=np.full(n, 20.0), price_usd_per_kwh=np.full(n, 0.5), carbon_g_per_kwh=np.full(n, 100.0)) + for sid in cfg.environment + } + res = run_scenario(sc, seed=0, environment=env) + assert np.allclose(res.site_cost_usd_1h, res.site_energy_kwh_1h * 0.5) + assert np.allclose(res.site_carbon_kg_1h, res.site_energy_kwh_1h * 0.1) + + +def test_job_override_accepts_an_external_schedule(): + sc = base_scenario(3600, jobs=[job("x", n=8)]) + ext = [job("trace-1", n=8, duration=600.0), job("trace-2", n=16, start=100.0, duration=600.0)] + res = run_scenario(sc, seed=0, jobs=ext) + assert [r["job_id"] for r in res.jobs_table] == ["trace-1", "trace-2"] + + +def test_default_scenario_runs_for_a_few_hours(): + sc = json.loads(SCENARIO_PATH.read_text()) + sc["horizon_s"] = 4 * 3600 + sc["outputs"] = {"gpu_windows": [[0, 600]]} + sc["events"] = [e for e in sc["events"] if e["onset_s"] < 4 * 3600 and e["event_id"] != "hw-1-precursor"] + res = run_scenario(copy.deepcopy(sc), seed=0) + s = res.summary() + assert s["data_kind"] == "simulated" + assert s["jobs"] > 0 and s["useful_gpu_hours"] > 0 + assert res.to_arrays()["meta"]["data_kind"] == "simulated" diff --git a/tests/test_research_heatwave_thermal.py b/tests/test_research_heatwave_thermal.py new file mode 100644 index 0000000..debb070 --- /dev/null +++ b/tests/test_research_heatwave_thermal.py @@ -0,0 +1,243 @@ +"""Tests for the Heatwave RC thermal, throttle, and cooling power models.""" + +from __future__ import annotations + +import math + +import numpy as np +import pytest + +from gpu_stack.research.heatwave_thermal import ( + CoolingPowerModel, + GPUThrottleModel, + RCThermalParams, + fit, + rc_step, + simulate, + simulate_full, + supply_setpoint, +) + + +def make_params(**over: float) -> RCThermalParams: + values = dict( + heat_capacity_j_per_k=2.0e5, + resistance_k_per_w=1.5e-3, + cooling_lag_s=120.0, + cooling_capacity_w=math.inf, + supply_setpoint_c=22.0, + outdoor_coupling_k_per_k=0.3, + outdoor_reference_c=25.0, + ) + values.update(over) + return RCThermalParams(**values) + + +def test_params_validation(): + with pytest.raises(ValueError): + make_params(heat_capacity_j_per_k=0.0) + with pytest.raises(ValueError): + make_params(resistance_k_per_w=-1.0) + with pytest.raises(ValueError): + make_params(cooling_lag_s=-1.0) + with pytest.raises(ValueError): + make_params(cooling_capacity_w=-5.0) + p = make_params() + assert p.time_constant_s == pytest.approx(300.0) + assert p.to_dict()["cooling_capacity_w"] is None + + +def test_steady_state_matches_formula(): + p = make_params(outdoor_coupling_k_per_k=0.0, cooling_lag_s=0.0) + n = 4000 + temps = simulate(p, np.full(n, 10_000.0), np.full(n, 20.0), 10.0, T0=22.0) + assert temps[-1] == pytest.approx(22.0 + 1.5e-3 * 10_000.0, abs=1e-6) + + +def test_exact_in_dt_for_piecewise_constant_inputs(): + p = make_params() + rng = np.random.default_rng(3) + coarse_p = rng.uniform(5_000, 15_000, size=40) + coarse_a = rng.uniform(15, 40, size=40) + dt = 60.0 + coarse = simulate(p, coarse_p, coarse_a, dt, T0=30.0) + sub = 120 + fine = simulate(p, np.repeat(coarse_p, sub), np.repeat(coarse_a, sub), dt / sub, T0=30.0) + # Same state at the coarse sample times, up to rounding. + assert np.allclose(coarse, fine[::sub], atol=1e-6) + + +def test_lag_equals_time_constant_branch_is_continuous(): + p_eq = make_params(cooling_lag_s=300.0) + p_near = make_params(cooling_lag_s=300.0 * (1 + 1e-6)) + pw = np.full(200, 9_000.0) + am = np.linspace(20, 40, 200) + a = simulate(p_eq, pw, am, 30.0, 28.0) + b = simulate(p_near, pw, am, 30.0, 28.0) + assert np.allclose(a, b, atol=1e-3) + + +def test_scalar_and_vector_steps_agree(): + p = make_params(cooling_capacity_w=8_000.0) + pw = np.concatenate([np.full(50, 6_000.0), np.full(100, 14_000.0), np.full(100, 3_000.0)]) + am = np.full(pw.size, 30.0) + scalar = simulate_full(p, pw, am, 20.0, T0=30.0) + T = np.array(30.0) + Ts = np.array(float(supply_setpoint(p, 30.0))) + out = [] + for i in range(pw.size): + out.append(float(T)) + T, Ts, _ = rc_step( + p.heat_capacity_j_per_k, p.resistance_k_per_w, p.cooling_lag_s, + p.cooling_capacity_w, supply_setpoint(p, am[i]), T, Ts, pw[i], 20.0, + ) + assert np.allclose(out, scalar["temp_c"], atol=1e-9) + + +def test_capacity_limit_and_energy_balance(): + cap = 8_000.0 + p = make_params(cooling_capacity_w=cap, outdoor_coupling_k_per_k=0.0) + pw = np.full(600, 14_000.0) + am = np.full(600, 25.0) + out = simulate_full(p, pw, am, 10.0, T0=30.0) + assert np.all(out["heat_removed_w"] <= cap + 1e-6) + # Heat above capacity warms the node at (P - cap) / C once saturated. + late = out["temp_c"][-200:] + slope = (late[-1] - late[0]) / (199 * 10.0) + assert slope == pytest.approx((14_000.0 - cap) / p.heat_capacity_j_per_k, rel=1e-6) + # Energy balance: sum of heat removed + stored equals energy in. + stored = p.heat_capacity_j_per_k * (out["temp_c"][-1] - out["temp_c"][0]) + total_in = float(np.sum(pw)) * 10.0 + # temps has the state at the start of each step, so add the last step. + last_step_removed = out["heat_removed_w"][-1] * 10.0 + last_step_dt = 10.0 * (pw[-1] - out["heat_removed_w"][-1]) / p.heat_capacity_j_per_k + stored_total = stored + p.heat_capacity_j_per_k * last_step_dt + removed_total = float(np.sum(out["heat_removed_w"])) * 10.0 + assert removed_total + stored_total == pytest.approx(total_in, rel=1e-9) + assert last_step_removed > 0 + + +def test_capacity_series_override_cooling_failure_raises_temperature(): + p = make_params(outdoor_coupling_k_per_k=0.0) + n = 720 + pw = np.full(n, 10_000.0) + am = np.full(n, 25.0) + cap = np.full(n, 20_000.0) + cap[300:] = 2_000.0 + base = simulate(p, pw, am, 10.0, T0=22.0 + 15.0) + failed = simulate(p, pw, am, 10.0, T0=22.0 + 15.0, capacity_w=cap) + assert failed[-1] > base[-1] + 10.0 + assert np.allclose(failed[:300], base[:300]) + + +def test_saturation_crossing_is_close_to_fine_integration(): + p = make_params(cooling_capacity_w=9_000.0, outdoor_coupling_k_per_k=0.0) + n = 90 + pw = np.concatenate([np.full(30, 6_000.0), np.full(60, 12_000.0)]) + am = np.full(n, 25.0) + coarse = simulate(p, pw, am, 60.0, T0=22.0 + 9.0) + sub = 60 + fine = simulate(p, np.repeat(pw, sub), np.repeat(am, sub), 1.0, T0=22.0 + 9.0) + assert np.max(np.abs(coarse - fine[::sub])) < 0.05 + + +def test_fit_recovers_known_parameters(): + truth = make_params(cooling_lag_s=90.0) + rng = np.random.default_rng(11) + n = 3000 + dt = 20.0 + # Piecewise-constant power with random levels and a diurnal-like outdoor swing. + pw = np.repeat(rng.uniform(4_000, 16_000, size=n // 30), 30) + am = 28.0 + 8.0 * np.sin(2 * np.pi * np.arange(n) * dt / 86_400.0 * 6) + 4.0 * np.sin( + 2 * np.pi * np.arange(n) * dt / 5_000.0 + ) + temps = simulate(truth, pw, am, dt, T0=35.0) + temps_noisy = temps + rng.normal(0.0, 0.02, size=n) + fitted, report = fit( + pw, am, temps_noisy, dt, + free=("heat_capacity_j_per_k", "resistance_k_per_w", "cooling_lag_s", "supply_setpoint_c", "outdoor_coupling_k_per_k"), + fixed={"outdoor_reference_c": truth.outdoor_reference_c}, + ) + assert fitted.heat_capacity_j_per_k == pytest.approx(truth.heat_capacity_j_per_k, rel=0.05) + assert fitted.resistance_k_per_w == pytest.approx(truth.resistance_k_per_w, rel=0.05) + assert fitted.time_constant_s == pytest.approx(truth.time_constant_s, rel=0.05) + assert fitted.cooling_lag_s == pytest.approx(truth.cooling_lag_s, rel=0.05) + assert fitted.supply_setpoint_c == pytest.approx(truth.supply_setpoint_c, abs=0.5) + assert report["rmse_c"] < 0.1 + assert report["r2"] > 0.99 + assert report["time_constant_s"] == pytest.approx(fitted.time_constant_s) + + +def test_fit_recovers_rc_without_lag(): + truth = make_params(cooling_lag_s=0.0, outdoor_coupling_k_per_k=0.0) + rng = np.random.default_rng(5) + n = 1500 + pw = np.repeat(rng.uniform(3_000, 18_000, size=n // 25), 25) + am = np.full(n, 20.0) + temps = simulate(truth, pw, am, 15.0, T0=25.0) + fitted, report = fit(pw, am, temps, 15.0) + assert fitted.heat_capacity_j_per_k == pytest.approx(truth.heat_capacity_j_per_k, rel=0.05) + assert fitted.resistance_k_per_w == pytest.approx(truth.resistance_k_per_w, rel=0.05) + assert report["success"] + + +def test_fit_rejects_bad_inputs(): + with pytest.raises(ValueError): + fit(np.ones(4), np.ones(4), np.ones(4), 1.0) + with pytest.raises(ValueError): + fit(np.ones(20), np.ones(20), np.ones(20), 1.0, free=("supply_setpoint_c",)) + + +def throttle() -> GPUThrottleModel: + return GPUThrottleModel( + throttle_threshold_c=80.0, throttle_span_c=10.0, min_clock_fraction=0.5, power_exponent=2.0 + ) + + +def test_throttle_model_has_no_defaults(): + with pytest.raises(TypeError): + GPUThrottleModel() # type: ignore[call-arg] + + +def test_throttle_reduces_clock_and_power_monotonically(): + m = throttle() + temps = np.array([60.0, 80.0, 85.0, 90.0, 120.0]) + clk = m.clock_fraction(temps) + assert clk[0] == 1.0 and clk[1] == 1.0 + assert np.all(np.diff(clk) <= 0) + assert clk[-1] == pytest.approx(0.5) + pw = m.power_w(560.0, 100.0, clk) + assert np.all(np.diff(pw) <= 1e-12) + assert pw[-1] < pw[0] + # Idle power does not scale. + assert m.power_w(100.0, 100.0, 0.5) == pytest.approx(100.0) + + +def test_clock_for_power_inverts_power_w(): + m = throttle() + clk = m.clock_for_power(300.0, 560.0, 100.0) + assert float(m.power_w(560.0, 100.0, clk)) == pytest.approx(300.0) + assert float(m.clock_for_power(1_000.0, 560.0, 100.0)) == 1.0 + assert float(m.clock_for_power(50.0, 560.0, 100.0)) == pytest.approx(0.5) + + +def test_cooling_power_pue_mode(): + m = CoolingPowerModel(kind="pue", pue=1.2) + assert float(m.power_w(10_000.0, 30.0, 24.0)) == pytest.approx(2_000.0) + + +def test_carnot_calibration_reproduces_pue_and_rises_with_outdoor_temperature(): + m = CoolingPowerModel.carnot_from_pue( + pue=1.2, reference_outdoor_c=20.0, reference_supply_c=24.0, + evaporator_approach_k=10.0, condenser_approach_k=10.0, cop_min=1.5, cop_max=40.0, + ) + assert float(m.power_w(10_000.0, 20.0, 24.0)) == pytest.approx(2_000.0) + cool = float(m.power_w(10_000.0, 10.0, 24.0)) + hot = float(m.power_w(10_000.0, 40.0, 24.0)) + assert cool < 2_000.0 < hot + assert m.to_dict()["kind"] == "carnot" + with pytest.raises(ValueError): + CoolingPowerModel.carnot_from_pue( + pue=1.2, reference_outdoor_c=20.0, reference_supply_c=24.0, + evaporator_approach_k=10.0, condenser_approach_k=10.0, cop_min=6.0, cop_max=40.0, + ) From a850d3b76f092a7a5c76ce4cdcfe935c25949a94 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 13:15:16 -0700 Subject: [PATCH 02/52] Add e003 simulator smoke result (simulated data) Seven-day default scenario with and without the reactive site-cap baseline, hierarchy conservation and determinism checks, and an RC fit on measured laptop telemetry. Summary statistics only. Co-Authored-By: Claude Sonnet Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- .../e003-heatwave/results/sim-smoke-v1.json | 9796 +++++++++++++++++ 1 file changed, 9796 insertions(+) create mode 100644 experiments/e003-heatwave/results/sim-smoke-v1.json diff --git a/experiments/e003-heatwave/results/sim-smoke-v1.json b/experiments/e003-heatwave/results/sim-smoke-v1.json new file mode 100644 index 0000000..35d5162 --- /dev/null +++ b/experiments/e003-heatwave/results/sim-smoke-v1.json @@ -0,0 +1,9796 @@ +{ + "provenance": { + "script": "experiments/e003-heatwave/run_sim_smoke.py", + "script_sha256": "4776ae59c6d789f8d1b64ad1e6c55ec1e95910a35c35ddb6af17e67bece43c00", + "git": { + "commit": "5acc7f287023f349a96594d241cb6e40a6c7e6c3", + "branch": "hw-sim", + "dirty": "false" + }, + "inputs": { + "experiments/e003-heatwave/sim-scenario-v1.json": "abb8a55e6bfe30508a6459a143d8b46aa1d97bfc8d1a03e630a433806c0f5252", + "docs/data/e002-checkpoint-power-raw-v1.json": "d4095c17409c6cea9de0fa1fe15b85c0957b3619f20c80b6efb508f979a7912e" + }, + "data_kind": "simulated", + "created_utc": "2026-10-26T20:09:31+00:00", + "notes": "Seven-day default scenario; all outputs simulated. laptop_rc_fit uses measured telemetry." + }, + "result": { + "data_kind": "simulated", + "scenario_id": "e003-heatwave-sim-v1", + "no_controller": { + "wall_seconds": 121.87710332870483, + "summary": { + "data_kind": "simulated", + "site_energy_kwh": [ + 14197.39389806982, + 10630.925157529946 + ], + "site_mean_power_w": [ + 84508.29701232247, + 63279.31641386674 + ], + "site_peak_power_w": [ + 103145.2506536076, + 76267.90603748488 + ], + "site_cost_usd": [ + 1145.328564643894, + 745.7019388306751 + ], + "site_carbon_kg": [ + 4977.816393149402, + 4788.857249542838 + ], + "site_cap_seconds_above": [ + 1535.0, + 0.0 + ], + "site_cap_energy_above_kwh": [ + 0.4515373192773469, + 0.0 + ], + "site_import_seconds_above": [ + 7200.0, + 5400.0 + ], + "site_import_energy_above_kwh": [ + 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0.027881649995833868 + }, + "telemetry_update_period_s": 0.4946930000005523 + }, + "throttle_model": { + "throttle_threshold_c": 80.0, + "throttle_span_c": 8.0, + "min_clock_fraction": 0.5, + "power_exponent": 2.0 + }, + "cooling_power_model": { + "kind": "carnot", + "eta": 0.27860003482500445, + "evaporator_approach_k": 10.0, + "condenser_approach_k": 10.0, + "cop_min": 1.5, + "cop_max": 30.0 + }, + "breaker_model": { + "nontrip_ratio": 1.13, + "trip_ratio": 1.45, + "conventional_time_s": 3600.0, + "time_constant_s": 3851.2065092795115 + }, + "ground_truth_events": [ + { + "event_id": "hw-1-precursor", + "kind": "hardware_fault", + "site_id": "site-a", + "rack_id": "a2", + "onset_s": 103680, + "end_s": 125280, + "has_precursor": true, + "precursor_channels": [ + "gpu_temp_c" + ], + "precursor_lead_s": 7200, + "precursor_strength": { + "gpu_temp_c": 6.0 + }, + "announced_at_s": null, + "data_kind": "simulated" + }, + { + "event_id": "cf-1-precursor", + "kind": "cooling_failure", + "site_id": "site-a", + "rack_id": "a1", + "onset_s": 129600, + "end_s": 135000, + "has_precursor": true, + "precursor_channels": [ + "supply_temp_c" + ], + "precursor_lead_s": 3600, + "precursor_strength": { + "supply_temp_c": 3.0 + }, + "announced_at_s": null, + "data_kind": "simulated" + }, + { + "event_id": "bt-1-derate-precursor", + "kind": "breaker_trip", + "site_id": "site-a", + "rack_id": "a1", + "onset_s": 198720, + "end_s": 213120, + "has_precursor": true, + "precursor_channels": [ + "breaker_load_ratio" + ], + "precursor_lead_s": 3600, + "precursor_strength": { + "breaker_load_ratio": 0.4 + }, + "announced_at_s": null, + "data_kind": "simulated", + "mode": "derate" + }, + { + "event_id": "gc-1-announced", + "kind": "grid_curtailment", + "site_id": "site-a", + "rack_id": null, + "onset_s": 233280, + "end_s": 240480, + "has_precursor": true, + "precursor_channels": [ + "known_future_schedule" + ], + "precursor_lead_s": 1800.0, + "precursor_strength": {}, + "announced_at_s": 231480, + "data_kind": "simulated" + }, + { + "event_id": "cf-2-sudden", + "kind": "cooling_failure", + "site_id": "site-b", + "rack_id": "b2", + "onset_s": 276480, + "end_s": 280080, + "has_precursor": false, + "precursor_channels": [], + "precursor_lead_s": 0.0, + "precursor_strength": {}, + "announced_at_s": null, + "data_kind": "simulated" + }, + { + "event_id": "hw-2-sudden", + "kind": "hardware_fault", + "site_id": "site-b", + "rack_id": "b1", + "onset_s": 328320, + "end_s": 342720, + "has_precursor": false, + "precursor_channels": [], + "precursor_lead_s": 0.0, + "precursor_strength": {}, + "announced_at_s": null, + "data_kind": "simulated" + }, + { + "event_id": "bt-2-forced", + "kind": "breaker_trip", + "site_id": "site-b", + "rack_id": "b1", + "onset_s": 380160, + "end_s": 381360, + "has_precursor": false, + "precursor_channels": [], + "precursor_lead_s": 0.0, + "precursor_strength": {}, + "announced_at_s": null, + "data_kind": "simulated", + "mode": "forced" + }, + { + "event_id": "cf-3-fan-precursor", + "kind": "cooling_failure", + "site_id": "site-a", + "rack_id": null, + "onset_s": 440640, + "end_s": 447840, + "has_precursor": true, + "precursor_channels": [ + "thermal_resistance" + ], + "precursor_lead_s": 7200, + "precursor_strength": { + "thermal_resistance": 0.25 + }, + "announced_at_s": null, + "data_kind": "simulated" + }, + { + "event_id": "gc-2-unannounced", + "kind": "grid_curtailment", + "site_id": "site-b", + "rack_id": null, + "onset_s": 483840, + "end_s": 489240, + "has_precursor": false, + "precursor_channels": [], + "precursor_lead_s": 0.0, + "precursor_strength": {}, + "announced_at_s": null, + "data_kind": "simulated" + }, + { + "event_id": "hw-3-sudden", + "kind": "hardware_fault", + "site_id": "site-a", + "rack_id": "a1", + "onset_s": 535680, + "end_s": 550080, + "has_precursor": false, + "precursor_channels": [], + "precursor_lead_s": 0.0, + "precursor_strength": {}, + "announced_at_s": null, + "data_kind": "simulated" + } + ], + "plan_vs_realized": { + "planned_jobs": 72, + "jobs_started_at_planned_time": 47, + "note": "events (faults, throttling) perturb the realized schedule; the plan assumes none" + }, + "conservation_check_event_free_1h": { + "data_kind": "simulated", + "window_s": [ + 0, + 3600 + ], + "max_rel_error_gpu_to_rack": 0.0, + "max_rel_error_rack_components": 0.0, + "max_rel_error_rack_to_site": 0.0, + "max_rel_error_energy_hourly": 0.0 + }, + "determinism_check": { + "horizon_s": 21600, + "same_seed_identical": true, + "different_seed_differs": true + }, + "laptop_rc_fit": { + "data_kind": "measured", + "source": "docs/data/e002-checkpoint-power-raw-v1.json", + "runs_fitted": 32, + "resample_dt_s": 0.25, + "telemetry_update_period_s": 0.4946930000005523, + "median_rmse_c": 0.517776353899817, + "median_r2": 0.8165760801822547, + "median_time_constant_s": 0.40992846275426126, + "fraction_time_constant_below_update_period": 0.6875, + "all_converged": true, + "temperature_resolution_c": 1.0 + }, + "assumptions": [ + "Everything in this scenario is simulated. No number here is a measurement of a real cluster.", + "Site capacities (power cap, cooling capacity, import limit) are scenario choices; the per-GPU accelerator fields follow the e001 screening scenario H100 SXM sites (experiments/e001-beyond-one-datacenter/screening-scenario-v1.json).", + "Compute-phase GPU power is 0.8 x the cited H100 SXM TDP (700 W): the 0.8 is the gpu_stack calculator's gpu_power_fraction assumption. Host overhead is 0.25 x compute power: the calculator's server_power_fraction assumption.", + "Idle/compute and relative compute-phase noise are measured ratios from the e002 laptop RTX telemetry. The communication-phase and checkpoint levels are set equal to the measured idle ratio (structural assumption, no measurement). The measured checkpoint/compute ratio is reported alongside in the result.", + "Fraction of a training step spent in compute (0.7) is an assumption.", + "Rack heat capacity per GPU = DGX H100 system mass 130.45 kg / 8 GPUs x an assumed effective specific heat of 500 J/(kg K). Mass source: https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html", + "Supply setpoint 24 C is an assumption inside the DGX H100 cited operating range of 5-30 C (same source). Design air rise 12 K, cooling lag 120 s, outdoor coupling 0.2 K/K above 30 C, capacity derate 1 percent per K above 30 C, GPU-to-air offset 0.05 K/W, protective GPU shutdown at 95 C and its 1800 s hold are assumptions.", + "Throttle threshold 80 C, span 8 K, minimum clock 0.5 and power exponent 2 are assumptions; they are not H100 datasheet values.", + "Cooling power: Carnot-shaped COP with second-law efficiency chosen so the overhead equals the gpu_stack calculator PUE assumption (1.2) at 20 C outdoor and 24 C supply; approach temperatures 10 K and COP bounds 1.5 to 30 are assumptions.", + "Breaker curve: IEC 60898-1 conventional values (1.13 and 1.45 x rated, 1 hour), https://www.se.com/au/en/faqs/FA369327/. Rack breaker ratings are 1.1 x the full-load rack power; reset delay 1800 s is an assumption.", + "WAN, restore, restart and membership parameters come from the e001 scenarios; the per-GPU checkpoint size is the e001 screening checkpoint (1.12 TB) divided by its 256 GPUs.", + "The job mix, outdoor temperature (including the heat wave), electricity price and carbon intensity are synthetic and seeded. Job value per GPU-hour is a synthetic number with no source.", + "Event precursors (supply-temperature creep, resistance rise, GPU temperature creep, breaker derate ramp, curtailment announcement) are assumptions of the simulation, not measured facts." + ], + "simplifications": [ + "Job admission, GPU clocks, thermal integration, breaker state, event onsets and controller decisions are evaluated at control-step boundaries; event onsets take effect at the first boundary at or after the stated time.", + "Thermal integration uses the mean rack power over each control step.", + "A synchronous job runs at the clock of its slowest GPU and all its GPUs are modeled at that clock.", + "Per-GPU power noise is independent across GPUs and seconds, scaled to the measured relative standard deviation of laptop compute-phase power.", + "GPU temperature uses the rack air temperature at the start of the control step and the previous step's mean GPU power.", + "Multi-site jobs take no separate checkpoints; their durable state is the last exact average.", + "A faulted GPU draws 0 W until repaired; its host stays powered unless the rack breaker is open." + ] + } +} From b6c408138be9d1e5d7644059ba71d5bf77bc6d43 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 13:23:23 -0700 Subject: [PATCH 03/52] Add power_forecast scope: quantile power planning, RC thermal, early checkpoint, time shifting New scope loaded after economics with equations, references and unit checks for: - planning site power at a forecast quantile (headroom, GPUs packable, exceedance probability 1 - q, expected over-cap energy and penalty), stranded power, cap utilization and uncertainty reserve - lumped RC thermal model (time constant, steady state, temperature at time t, closed-form time to throttle with never/already guards, cooling margin) - early-checkpoint value (lost work with and without warning, expected loss weighted by recall, GPU-hours saved per fault) - time-shifting flexible work (energy-weighted price and carbon paid, cost and carbon per token, linked to the economics price, carbon and cost-per-token variables) Root inputs that a forecaster supplies carry no default and cite experiments/e003-heatwave. Tests cover each group with hand-derivable values. Co-Authored-By: Claude Sonnet --- gpu_stack/scopes/__init__.py | 4 + gpu_stack/scopes/power_forecast.py | 77 +++ gpu_stack/scopes/power_forecast_checkpoint.py | 193 ++++++ gpu_stack/scopes/power_forecast_planning.py | 359 ++++++++++ gpu_stack/scopes/power_forecast_rc.py | 298 ++++++++ gpu_stack/scopes/power_forecast_refs.py | 207 ++++++ gpu_stack/scopes/power_forecast_shift.py | 305 +++++++++ tests/test_power_forecast_scope.py | 641 ++++++++++++++++++ 8 files changed, 2084 insertions(+) create mode 100644 gpu_stack/scopes/power_forecast.py create mode 100644 gpu_stack/scopes/power_forecast_checkpoint.py create mode 100644 gpu_stack/scopes/power_forecast_planning.py create mode 100644 gpu_stack/scopes/power_forecast_rc.py create mode 100644 gpu_stack/scopes/power_forecast_refs.py create mode 100644 gpu_stack/scopes/power_forecast_shift.py create mode 100644 tests/test_power_forecast_scope.py diff --git a/gpu_stack/scopes/__init__.py b/gpu_stack/scopes/__init__.py index 302754e..4872cca 100644 --- a/gpu_stack/scopes/__init__.py +++ b/gpu_stack/scopes/__init__.py @@ -42,6 +42,9 @@ "cluster", # -> gpu, memory_subsystem, interconnect "thermal", # -> gpu, cluster "economics", # -> gpu, thermal, cluster, training, parallelism + + # Forecast-aware operation (Heatwave): sits on top of economics + "power_forecast", # -> thermal, cluster, training, economics ] @@ -62,6 +65,7 @@ "cluster": "node NIC topology -> rack -> cluster -> hyperscaler aggregation", "thermal": "junction-to-ambient resistance, coolant flow, PUE", "economics": "GPU amortization, $/kWh, $/token, run cost", + "power_forecast": "quantile power planning, stranded power, lumped RC thermal, early checkpoints, time-shifted $/token and CO2/token", } diff --git a/gpu_stack/scopes/power_forecast.py b/gpu_stack/scopes/power_forecast.py new file mode 100644 index 0000000..7248d2c --- /dev/null +++ b/gpu_stack/scopes/power_forecast.py @@ -0,0 +1,77 @@ +""" +scopes/power_forecast.py +======================== + +Aggregator for the forecast-aware power, thermal, and cost scope. + +The scopes below this one describe a cluster as it is: its power, its cooling, +its cost. A probabilistic forecast of power, temperature, price, and carbon +adds a way to act before the numbers arrive, and this scope holds the +equations behind those actions so the calculator can reason about them with +units. Four groups, in dependency order: planning (plan site power at a +forecast quantile, packing under a power cap, expected over-cap energy and +penalty, stranded power), a lumped RC thermal model (time constant, steady +state, temperature after a time, time to throttle, cooling margin), early +checkpoints (expected GPU-hours saved per fault by a warning), and time +shifting (energy-weighted price and carbon paid, cost and carbon per token). + +Quantities that a forecaster supplies are root inputs with no default. They +point at gpu_stack experiments/e003-heatwave, which produces them. This file +re-exports all of the helpers so public imports stay stable. +""" + +from ..core import System + +from .power_forecast_planning import * +from .power_forecast_planning import ( + POWER_FORECAST_PLANNING_EQUATIONS, + POWER_FORECAST_PLANNING_VARIABLES, +) +from .power_forecast_rc import * +from .power_forecast_rc import ( + POWER_FORECAST_RC_EQUATIONS, + POWER_FORECAST_RC_VARIABLES, +) +from .power_forecast_checkpoint import * +from .power_forecast_checkpoint import ( + POWER_FORECAST_CHECKPOINT_EQUATIONS, + POWER_FORECAST_CHECKPOINT_VARIABLES, +) +from .power_forecast_shift import * +from .power_forecast_shift import ( + POWER_FORECAST_SHIFT_EQUATIONS, + POWER_FORECAST_SHIFT_VARIABLES, +) + + +sys_power_forecast = System( + name="power_forecast", + scope="power_forecast", + description="Forecast-quantile power planning, lumped RC thermal, early checkpoints, and time-shifted cost and carbon per token.", +) + + +POWER_FORECAST_VARIABLES = ( + POWER_FORECAST_PLANNING_VARIABLES + + POWER_FORECAST_RC_VARIABLES + + POWER_FORECAST_CHECKPOINT_VARIABLES + + POWER_FORECAST_SHIFT_VARIABLES +) + +POWER_FORECAST_EQUATIONS = ( + POWER_FORECAST_PLANNING_EQUATIONS + + POWER_FORECAST_RC_EQUATIONS + + POWER_FORECAST_CHECKPOINT_EQUATIONS + + POWER_FORECAST_SHIFT_EQUATIONS +) + +for v in POWER_FORECAST_VARIABLES: + sys_power_forecast.add(v) + +for e in POWER_FORECAST_EQUATIONS: + sys_power_forecast.add(e) + + +__all__ = [ + *[name for name in globals() if not name.startswith("_")], +] diff --git a/gpu_stack/scopes/power_forecast_checkpoint.py b/gpu_stack/scopes/power_forecast_checkpoint.py new file mode 100644 index 0000000..087b2ca --- /dev/null +++ b/gpu_stack/scopes/power_forecast_checkpoint.py @@ -0,0 +1,193 @@ +""" +scopes/power_forecast_checkpoint.py +=================================== + +What an early warning of an outage is worth in GPU-hours. + +A training job that loses power, cooling, or a node falls back to its last +checkpoint. A fault with no warning arrives at a random point in the +checkpoint interval, so on average half an interval of work on every affected +GPU is lost (Young 1974). A forecast that warns of the fault with a lead time +at least as long as one checkpoint write lets the job checkpoint first, so +only about one write time of work is lost. The forecast does not catch every +fault: recall is the fraction it warns about in time. The expected loss per +fault weights the two cases by recall, and the GPU-hours saved per fault are +the difference from the no-warning loss. + +The checkpoint interval and checkpoint write time are the cluster +reliability scope's variables. The warning lead time, recall, and the number +of GPUs a fault takes down are root inputs; lead time and recall come from the +Heatwave experiment's replays and have no default here. +""" + +import sympy as sp + +from ..core import Inequality, eq +from ..core.units import HOUR, SECOND + +from .cluster_reliability import checkpoint_time, optimal_checkpoint_interval +from .power_forecast_refs import ( + forecast_var, + DALY_2006_REF, + E003_INPUT_REF, + EARLY_CHECKPOINT_MODEL_REF, + YOUNG_1974_REF, +) + + +DIMENSIONLESS = sp.Integer(1) + +CHECKPOINT_REFS = [EARLY_CHECKPOINT_MODEL_REF, YOUNG_1974_REF, DALY_2006_REF] +CHECKPOINT_INPUT_REFS = [EARLY_CHECKPOINT_MODEL_REF, E003_INPUT_REF] + + +# --------------------------------------------------------------------------- +# Root inputs: job size, warning lead time, recall +# --------------------------------------------------------------------------- + +gpus_affected = forecast_var( + "forecast.ckpt.gpus_affected", "N_aff_fc", "GPUs", + "GPUs whose job loses progress when one fault occurs (the size of the " + "affected job or domain). It can equal the cluster GPU count.", + CHECKPOINT_REFS, + positive=True, + sp_units=DIMENSIONLESS, +) +warning_lead_time = forecast_var( + "forecast.ckpt.lead_time", "L_warn_fc", "s", + "Lead time of the forecast warning: how long before the fault the " + "forecast raises the alarm. The model needs it to be at least the " + "checkpoint write time.", + CHECKPOINT_INPUT_REFS, + positive=True, + sp_units=SECOND, +) +warning_recall = forecast_var( + "forecast.ckpt.recall", "r_warn_fc", "dimensionless", + "Forecast recall: the fraction of faults warned about with at least the " + "stated lead time. Measure it from replays; the scope sets no default.", + CHECKPOINT_INPUT_REFS, + value_range=(0.0, 1.0), + sp_units=DIMENSIONLESS, +) + + +# --------------------------------------------------------------------------- +# Derived: lost work with and without warning, expected loss, savings +# --------------------------------------------------------------------------- + +lost_work_unwarned = forecast_var( + "forecast.ckpt.lost_work_unwarned", "W_lost0_fc", "GPU*h", + "Expected work lost per fault with no warning, in GPU-hours: half a " + "checkpoint interval on every affected GPU.", + CHECKPOINT_REFS, + nonnegative=True, + sp_units=HOUR, +) +lost_work_warned = forecast_var( + "forecast.ckpt.lost_work_warned", "W_lostw_fc", "GPU*h", + "Work lost per fault when the warning lead time covers a checkpoint " + "write, in GPU-hours: one checkpoint write time on every affected GPU.", + CHECKPOINT_REFS, + nonnegative=True, + sp_units=HOUR, +) +lost_work_expected = forecast_var( + "forecast.ckpt.lost_work_expected", "W_lostx_fc", "GPU*h", + "Expected work lost per fault with the forecast in place: the warned " + "loss with probability recall, the unwarned loss otherwise.", + CHECKPOINT_REFS, + nonnegative=True, + sp_units=HOUR, +) +gpu_hours_saved = forecast_var( + "forecast.ckpt.gpu_hours_saved", "S_gpuh_fc", "GPU*h", + "Expected GPU-hours saved per fault: unwarned loss minus expected loss " + "with the forecast.", + CHECKPOINT_REFS, + signed=True, + sp_units=HOUR, +) + + +# --------------------------------------------------------------------------- +# Equations +# --------------------------------------------------------------------------- + +eq_lost_work_unwarned = eq( + "forecast.eq.ckpt.lost_work_unwarned", + lost_work_unwarned.symbol, + sp.Rational(1, 2) * optimal_checkpoint_interval.symbol + * gpus_affected.symbol / sp.Integer(3600), + "Without warning, a fault loses half a checkpoint interval of work on " + "every affected GPU, converted from GPU-seconds to GPU-hours.", + references=CHECKPOINT_REFS, + check_units=True, +) +eq_lost_work_warned = eq( + "forecast.eq.ckpt.lost_work_warned", + lost_work_warned.symbol, + checkpoint_time.symbol * gpus_affected.symbol / sp.Integer(3600), + "With a warning at least one write long, a fault loses about one " + "checkpoint write time of work on every affected GPU, converted from " + "GPU-seconds to GPU-hours.", + references=CHECKPOINT_REFS, + check_units=True, +) +eq_lost_work_expected = eq( + "forecast.eq.ckpt.lost_work_expected", + lost_work_expected.symbol, + warning_recall.symbol * lost_work_warned.symbol + + (1 - warning_recall.symbol) * lost_work_unwarned.symbol, + "Expected loss weights the warned and unwarned losses by forecast recall.", + references=CHECKPOINT_REFS, + check_units=True, +) +eq_gpu_hours_saved = eq( + "forecast.eq.ckpt.gpu_hours_saved", + gpu_hours_saved.symbol, + lost_work_unwarned.symbol - lost_work_expected.symbol, + "GPU-hours saved per fault equal the unwarned loss minus the expected loss with the forecast.", + references=CHECKPOINT_REFS, + check_units=True, +) + +ineq_lead_time_covers_checkpoint = Inequality( + "forecast.ineq.ckpt.lead_time_covers_checkpoint", + warning_lead_time.symbol, + checkpoint_time.symbol, + ">=", + "The warning lead time must be at least the checkpoint write time, " + "otherwise the job cannot finish a checkpoint before the fault.", + references=CHECKPOINT_REFS, +) + + +POWER_FORECAST_CHECKPOINT_VARIABLES = [ + gpus_affected, + warning_lead_time, + warning_recall, + lost_work_unwarned, + lost_work_warned, + lost_work_expected, + gpu_hours_saved, +] + +POWER_FORECAST_CHECKPOINT_EQUATIONS = [ + eq_lost_work_unwarned, + eq_lost_work_warned, + eq_lost_work_expected, + eq_gpu_hours_saved, + ineq_lead_time_covers_checkpoint, +] + + +__all__ = [ + "gpus_affected", "warning_lead_time", "warning_recall", + "lost_work_unwarned", "lost_work_warned", "lost_work_expected", + "gpu_hours_saved", + "eq_lost_work_unwarned", "eq_lost_work_warned", "eq_lost_work_expected", + "eq_gpu_hours_saved", "ineq_lead_time_covers_checkpoint", + "POWER_FORECAST_CHECKPOINT_VARIABLES", + "POWER_FORECAST_CHECKPOINT_EQUATIONS", +] diff --git a/gpu_stack/scopes/power_forecast_planning.py b/gpu_stack/scopes/power_forecast_planning.py new file mode 100644 index 0000000..e5943be --- /dev/null +++ b/gpu_stack/scopes/power_forecast_planning.py @@ -0,0 +1,359 @@ +""" +scopes/power_forecast_planning.py +================================= + +Packing jobs under a site power cap by planning at a forecast quantile. + +A site has a power cap: a contract limit, a transformer rating, or a feeder +rating. A reactive operator keeps power under the cap by leaving room for the +worst case it has seen. A forecast-aware operator reads a probabilistic +forecast of site power and plans at a chosen quantile q: the planned power is +the forecast q-quantile, and for a calibrated forecast the chance that real +power lands above the plan is 1 - q. Headroom is the cap minus the plan, and +dividing headroom by the planned facility power of one more GPU gives how many +more GPUs fit. Power that does land above the cap costs energy at an over-cap +price. Stranded power is the other side of the same ledger: the cap minus the +expected power, which is the capacity a reactive operator leaves unused. + +The forecast outputs (planned power, expected power, mean overshoot) are root +inputs: the scope sets no default for them, because they come from the +Heatwave experiment's forecasts. The facility overhead that turns a GPU's IT +power into site power is the PUE already in the thermal scope. +""" + +import sympy as sp + +from ..core import Inequality, eq +from ..core.units import KWH, SECOND, WATT + +from .thermal_facility import pue +from .power_forecast_refs import ( + forecast_var, + E003_INPUT_REF, + FAN_WEBER_BARROSO_2007_REF, + GNEITING_RAFTERY_2007_REF, + KOENKER_2005_REF, + KOENKER_BASSETT_1978_REF, + OVERCAP_COST_MODEL_REF, + QUANTILE_PLANNING_MODEL_REF, + STRANDED_POWER_MODEL_REF, +) +from .economics_capex_refs import USD + + +DIMENSIONLESS = sp.Integer(1) +ENERGY_PRICE_KWH = USD / KWH + +PLANNING_REFS = [ + QUANTILE_PLANNING_MODEL_REF, + GNEITING_RAFTERY_2007_REF, + KOENKER_2005_REF, +] +FORECAST_INPUT_REFS = [ + QUANTILE_PLANNING_MODEL_REF, + E003_INPUT_REF, + KOENKER_BASSETT_1978_REF, +] +OVERCAP_REFS = [OVERCAP_COST_MODEL_REF, KOENKER_2005_REF] +STRANDED_REFS = [STRANDED_POWER_MODEL_REF, FAN_WEBER_BARROSO_2007_REF] + + +# --------------------------------------------------------------------------- +# Root inputs: the cap, the quantile, and what the forecaster supplies +# --------------------------------------------------------------------------- + +plan_quantile_level = forecast_var( + "forecast.plan.quantile_level", "q_plan_fc", "dimensionless", + "Quantile level q in (0, 1) at which the operator plans site power. A " + "higher q plans closer to the worst case and lowers the chance of " + "exceeding the cap.", + [QUANTILE_PLANNING_MODEL_REF, KOENKER_2005_REF], + value_range=(0.0, 1.0), + positive=True, + sp_units=DIMENSIONLESS, +) +site_power_cap = forecast_var( + "forecast.plan.site_power_cap", "P_cap_fc", "W", + "Site power cap that planned power must stay under (contract limit, " + "transformer or feeder rating). It can equal the facility electrical " + "design capacity in the thermal scope, or sit below it.", + [QUANTILE_PLANNING_MODEL_REF], + positive=True, + sp_units=WATT, +) +site_power_planned = forecast_var( + "forecast.plan.site_power_planned", "P_plan_fc", "W", + "Planned site power: the forecast q-quantile of site power over the " + "planning horizon, read from the forecaster at the quantile level " + "forecast.plan.quantile_level.", + FORECAST_INPUT_REFS, + nonnegative=True, + sp_units=WATT, +) +site_power_expected = forecast_var( + "forecast.plan.site_power_expected", "P_exp_fc", "W", + "Expected site power over the planning horizon: the forecast mean or the " + "forecast median (state which when supplying it).", + FORECAST_INPUT_REFS, + nonnegative=True, + sp_units=WATT, +) +gpu_power_planned = forecast_var( + "forecast.plan.gpu_power_planned", "p_gpu_plan_fc", "W", + "Planned IT power of one added GPU (a forecast quantile of its draw, " + "including its share of the node), before facility overhead.", + FORECAST_INPUT_REFS, + positive=True, + sp_units=WATT, +) +overshoot_power_mean = forecast_var( + "forecast.plan.overshoot_power_mean", "P_over_fc", "W", + "Mean amount by which site power exceeds the cap, averaged over the time " + "it is above the cap. Read from the forecast's upper tail.", + FORECAST_INPUT_REFS, + nonnegative=True, + sp_units=WATT, +) +planning_horizon = forecast_var( + "forecast.plan.horizon", "T_hor_fc", "s", + "Length of the planning horizon over which the plan applies.", + [QUANTILE_PLANNING_MODEL_REF], + positive=True, + sp_units=SECOND, +) +overcap_price = forecast_var( + "forecast.plan.overcap_price", "p_over_kWh_fc", "USD/(kW*h)", + "Price paid per kilowatt-hour drawn above the cap (contract overage " + "charge, curtailment cost, or shadow price). Supply from the site's " + "tariff or contract; the scope sets no default.", + [OVERCAP_COST_MODEL_REF], + nonnegative=True, + sp_units=ENERGY_PRICE_KWH, +) + + +# --------------------------------------------------------------------------- +# Derived: headroom, packing, exceedance, over-cap energy and penalty +# --------------------------------------------------------------------------- + +planning_headroom = forecast_var( + "forecast.plan.headroom", "P_head_fc", "W", + "Provisioning headroom: site power cap minus planned site power. " + "Negative when the plan already exceeds the cap.", + PLANNING_REFS, + signed=True, + sp_units=WATT, +) +gpu_site_power_planned = forecast_var( + "forecast.plan.gpu_site_power_planned", "p_gpu_site_fc", "W", + "Planned site power of one added GPU: its planned IT power times PUE.", + PLANNING_REFS, + positive=True, + sp_units=WATT, +) +n_gpus_packable = forecast_var( + "forecast.plan.n_gpus_packable", "N_pack_fc", "GPUs", + "Number of additional GPUs that fit in the headroom at their planned " + "site power, before rounding down to a whole GPU. Negative when headroom " + "is negative (GPUs to remove).", + PLANNING_REFS, + signed=True, + sp_units=DIMENSIONLESS, +) +exceedance_probability = forecast_var( + "forecast.plan.exceedance_probability", "p_exceed_fc", "dimensionless", + "Expected probability that site power exceeds the plan, for a calibrated " + "forecast: 1 - q. A miscalibrated forecast has a different realized rate; " + "measure it from replays.", + PLANNING_REFS, + value_range=(0.0, 1.0), + sp_units=DIMENSIONLESS, +) +overcap_energy = forecast_var( + "forecast.plan.overcap_energy", "E_over_fc", "kW*h", + "Expected energy drawn above the cap over the planning horizon: " + "exceedance probability times mean overshoot power times horizon length.", + OVERCAP_REFS, + nonnegative=True, + sp_units=KWH, +) +overcap_penalty_cost = forecast_var( + "forecast.plan.overcap_penalty_cost", "C_over_fc", "USD", + "Expected over-cap penalty over the planning horizon: over-cap energy " + "times the over-cap price.", + OVERCAP_REFS, + nonnegative=True, + sp_units=USD, +) + + +# --------------------------------------------------------------------------- +# Derived: stranded power and the reserve held for forecast uncertainty +# --------------------------------------------------------------------------- + +stranded_power = forecast_var( + "forecast.plan.stranded_power", "P_strand_fc", "W", + "Stranded power: site power cap minus expected site power. It is the " + "provisioned capacity that a reactive operator, who plans for the worst " + "case, leaves unused on average.", + STRANDED_REFS, + signed=True, + sp_units=WATT, +) +cap_utilization = forecast_var( + "forecast.plan.cap_utilization", "u_cap_fc", "dimensionless", + "Utilization of the cap: expected site power divided by the cap.", + STRANDED_REFS, + nonnegative=True, + sp_units=DIMENSIONLESS, +) +uncertainty_reserve = forecast_var( + "forecast.plan.uncertainty_reserve", "P_res_fc", "W", + "Power held back for forecast uncertainty: planned power minus expected " + "power. It is the part of the cap a quantile plan reserves beyond the " + "expected draw.", + PLANNING_REFS, + signed=True, + sp_units=WATT, +) + + +# --------------------------------------------------------------------------- +# Equations +# --------------------------------------------------------------------------- + +eq_planning_headroom = eq( + "forecast.eq.plan.headroom", + planning_headroom.symbol, + site_power_cap.symbol - site_power_planned.symbol, + "Headroom equals the site power cap minus planned site power.", + references=PLANNING_REFS, + check_units=True, +) +eq_gpu_site_power_planned = eq( + "forecast.eq.plan.gpu_site_power_planned", + gpu_site_power_planned.symbol, + gpu_power_planned.symbol * pue.symbol, + "Planned site power of one added GPU equals its planned IT power times PUE.", + references=PLANNING_REFS, + check_units=True, +) +eq_n_gpus_packable = eq( + "forecast.eq.plan.n_gpus_packable", + n_gpus_packable.symbol, + planning_headroom.symbol / gpu_site_power_planned.symbol, + "GPUs that fit under the cap equal headroom divided by the planned site power of one GPU.", + references=PLANNING_REFS, + check_units=True, +) +eq_exceedance_probability = eq( + "forecast.eq.plan.exceedance_probability", + exceedance_probability.symbol, + 1 - plan_quantile_level.symbol, + "For a calibrated quantile forecast, site power exceeds the planned " + "q-quantile with probability 1 - q.", + references=PLANNING_REFS, + check_units=True, +) +eq_overcap_energy = eq( + "forecast.eq.plan.overcap_energy", + overcap_energy.symbol, + exceedance_probability.symbol * overshoot_power_mean.symbol + * planning_horizon.symbol / sp.Integer(3_600_000), + "Expected over-cap energy equals exceedance probability times mean " + "overshoot power times horizon, converted from joules to kilowatt-hours " + "(3.6e6 J per kWh).", + references=OVERCAP_REFS, + check_units=True, +) +eq_overcap_penalty_cost = eq( + "forecast.eq.plan.overcap_penalty_cost", + overcap_penalty_cost.symbol, + overcap_energy.symbol * overcap_price.symbol, + "Over-cap penalty equals over-cap energy times the over-cap price.", + references=OVERCAP_REFS, + check_units=True, +) +eq_stranded_power = eq( + "forecast.eq.plan.stranded_power", + stranded_power.symbol, + site_power_cap.symbol - site_power_expected.symbol, + "Stranded power equals the site power cap minus expected site power.", + references=STRANDED_REFS, + check_units=True, +) +eq_cap_utilization = eq( + "forecast.eq.plan.cap_utilization", + cap_utilization.symbol, + site_power_expected.symbol / site_power_cap.symbol, + "Cap utilization equals expected site power divided by the cap.", + references=STRANDED_REFS, + check_units=True, +) +eq_uncertainty_reserve = eq( + "forecast.eq.plan.uncertainty_reserve", + uncertainty_reserve.symbol, + site_power_planned.symbol - site_power_expected.symbol, + "The uncertainty reserve equals planned site power minus expected site power.", + references=PLANNING_REFS, + check_units=True, +) + +ineq_quantile_level_below_one = Inequality( + "forecast.ineq.plan.quantile_level_below_one", + plan_quantile_level.symbol, + 1, + "<", + "The planning quantile level must be strictly below 1 (the domain rule on the variable already requires it to be positive).", + references=[QUANTILE_PLANNING_MODEL_REF], +) + + +POWER_FORECAST_PLANNING_VARIABLES = [ + plan_quantile_level, + site_power_cap, + site_power_planned, + site_power_expected, + gpu_power_planned, + overshoot_power_mean, + planning_horizon, + overcap_price, + planning_headroom, + gpu_site_power_planned, + n_gpus_packable, + exceedance_probability, + overcap_energy, + overcap_penalty_cost, + stranded_power, + cap_utilization, + uncertainty_reserve, +] + +POWER_FORECAST_PLANNING_EQUATIONS = [ + eq_planning_headroom, + eq_gpu_site_power_planned, + eq_n_gpus_packable, + eq_exceedance_probability, + eq_overcap_energy, + eq_overcap_penalty_cost, + eq_stranded_power, + eq_cap_utilization, + eq_uncertainty_reserve, + ineq_quantile_level_below_one, +] + + +__all__ = [ + "plan_quantile_level", "site_power_cap", "site_power_planned", + "site_power_expected", "gpu_power_planned", "overshoot_power_mean", + "planning_horizon", "overcap_price", "planning_headroom", + "gpu_site_power_planned", "n_gpus_packable", "exceedance_probability", + "overcap_energy", "overcap_penalty_cost", "stranded_power", + "cap_utilization", "uncertainty_reserve", + "eq_planning_headroom", "eq_gpu_site_power_planned", "eq_n_gpus_packable", + "eq_exceedance_probability", "eq_overcap_energy", + "eq_overcap_penalty_cost", "eq_stranded_power", "eq_cap_utilization", + "eq_uncertainty_reserve", + "ineq_quantile_level_below_one", + "POWER_FORECAST_PLANNING_VARIABLES", "POWER_FORECAST_PLANNING_EQUATIONS", +] diff --git a/gpu_stack/scopes/power_forecast_rc.py b/gpu_stack/scopes/power_forecast_rc.py new file mode 100644 index 0000000..f0bd6fb --- /dev/null +++ b/gpu_stack/scopes/power_forecast_rc.py @@ -0,0 +1,298 @@ +""" +scopes/power_forecast_rc.py +=========================== + +A lumped RC thermal model for forecasting temperature and time to throttle. + +Heat from a GPU, a node, or a rack loop flows through a thermal resistance R +(kelvin per watt) to the coolant supply, and the hardware stores heat in a +heat capacity C (joules per kelvin). With constant heat power Q the +temperature relaxes exponentially from its start value T0 toward a steady +state of supply temperature plus Q*R, with time constant tau = R*C. That is +the lumped-capacitance solution from any heat-transfer text, and it is +enough to turn a power forecast into a temperature forecast and a +time-until-throttle. + +The time until a throttle threshold is reached has a closed form, +t = tau * ln((T_ss - T0) / (T_ss - T_thr)), which only exists when the +steady state sits above the threshold. The piecewise definition returns 0 +when the node already sits at or above the threshold and infinity when the +threshold is never reached. The cooling margin is the power the node can +sustain below the threshold, (T_thr - T_supply) / R, minus the power it is +dissipating. + +The coolant supply temperature is the thermal scope's coolant inlet +temperature. For one package, R equals the thermal scope's junction-to- +coolant resistance and the steady state equals its junction temperature; +this scope keeps R as its own input so it can describe a node or a rack loop +whose resistance and capacity are fitted from telemetry. +""" + +import sympy as sp + +from ..core import Inequality, PiecewiseEquation, eq +from ..core.units import JOULE, KELVIN, SECOND, WATT + +from .thermal_package import T_coolant_inlet +from .power_forecast_refs import ( + forecast_var, + E003_INPUT_REF, + INCROPERA_LUMPED_REF, + LUMPED_RC_MODEL_REF, +) + + +DIMENSIONLESS = sp.Integer(1) +THERMAL_RESISTANCE = KELVIN / WATT +HEAT_CAPACITY = JOULE / KELVIN + +RC_REFS = [LUMPED_RC_MODEL_REF, INCROPERA_LUMPED_REF] +RC_INPUT_REFS = [LUMPED_RC_MODEL_REF, INCROPERA_LUMPED_REF, E003_INPUT_REF] + + +# --------------------------------------------------------------------------- +# Root inputs: R, C, heat power, start temperature, throttle threshold, time +# --------------------------------------------------------------------------- + +rc_resistance = forecast_var( + "forecast.rc.resistance", "R_rc_fc", "K/W", + "Thermal resistance from the lumped node to its coolant supply. For one " + "package it is the thermal scope's junction-to-coolant resistance; for a " + "node or rack loop, fit it from telemetry.", + RC_REFS, + positive=True, + sp_units=THERMAL_RESISTANCE, +) +rc_capacitance = forecast_var( + "forecast.rc.capacitance", "C_rc_fc", "J/K", + "Heat capacity of the lumped node: the energy needed to raise its " + "temperature by one kelvin (mass times specific heat of the hardware and " + "coolant it holds).", + RC_REFS, + positive=True, + sp_units=HEAT_CAPACITY, +) +rc_heat_power = forecast_var( + "forecast.rc.heat_power", "Q_rc_fc", "W", + "Constant heat power dissipated into the lumped node over the interval. " + "Take it from a power forecast quantile.", + RC_INPUT_REFS, + nonnegative=True, + sp_units=WATT, +) +rc_initial_temperature = forecast_var( + "forecast.rc.initial_temperature", "T0_rc_fc", "K", + "Node temperature at the start of the interval.", + RC_INPUT_REFS, + positive=True, + sp_units=KELVIN, +) +rc_elapsed_time = forecast_var( + "forecast.rc.elapsed_time", "t_rc_fc", "s", + "Time elapsed since the start of the interval.", + RC_REFS, + nonnegative=True, + sp_units=SECOND, +) +rc_throttle_temperature = forecast_var( + "forecast.rc.throttle_temperature", "T_thr_rc_fc", "K", + "Temperature at which the hardware begins to throttle. Take it from the " + "device datasheet; the scope sets no default.", + RC_REFS, + positive=True, + sp_units=KELVIN, +) + + +# --------------------------------------------------------------------------- +# Derived: tau, steady state, temperature at time t, time to throttle, margin +# --------------------------------------------------------------------------- + +rc_time_constant = forecast_var( + "forecast.rc.time_constant", "tau_rc_fc", "s", + "Thermal time constant: resistance times heat capacity.", + RC_REFS, + positive=True, + sp_units=SECOND, +) +rc_steady_temperature = forecast_var( + "forecast.rc.steady_temperature", "T_ss_rc_fc", "K", + "Steady-state temperature under constant heat power: coolant supply " + "temperature plus heat power times resistance.", + RC_REFS, + positive=True, + sp_units=KELVIN, +) +rc_temperature_at_time = forecast_var( + "forecast.rc.temperature_at_time", "T_t_rc_fc", "K", + "Node temperature after the elapsed time, starting from the initial " + "temperature under constant heat power.", + RC_REFS, + positive=True, + sp_units=KELVIN, +) +rc_time_to_throttle_reachable = forecast_var( + "forecast.rc.time_to_throttle_reachable", "t_thr_reach_rc_fc", "s", + "Closed-form time to reach the throttle temperature, valid only when " + "the initial temperature is below the threshold and the steady state is " + "above it. Outside that case this expression has no real value; use " + "forecast.rc.time_to_throttle, which handles those cases.", + RC_REFS, + sp_units=SECOND, +) +rc_time_to_throttle = forecast_var( + "forecast.rc.time_to_throttle", "t_thr_rc_fc", "s", + "Time until the node reaches the throttle temperature under constant " + "heat power: 0 if it already sits at or above the threshold, infinity " + "if the steady state never reaches it.", + RC_REFS, + nonnegative=True, + sp_units=SECOND, +) +rc_sustainable_power = forecast_var( + "forecast.rc.sustainable_power", "Q_sus_rc_fc", "W", + "Largest constant heat power the node can dissipate without ever " + "reaching the throttle temperature: (threshold - supply) / resistance.", + RC_REFS, + sp_units=WATT, +) +rc_cooling_margin = forecast_var( + "forecast.rc.cooling_margin", "Q_mar_rc_fc", "W", + "Cooling capacity margin: sustainable power minus the heat power being " + "dissipated. Positive means the threshold is never reached; zero or " + "negative means it is reached in finite time.", + RC_REFS, + signed=True, + sp_units=WATT, +) + + +# --------------------------------------------------------------------------- +# Equations +# --------------------------------------------------------------------------- + +eq_rc_time_constant = eq( + "forecast.eq.rc.time_constant", + rc_time_constant.symbol, + rc_resistance.symbol * rc_capacitance.symbol, + "The time constant is thermal resistance times heat capacity.", + references=RC_REFS, + check_units=True, +) +eq_rc_steady_temperature = eq( + "forecast.eq.rc.steady_temperature", + rc_steady_temperature.symbol, + T_coolant_inlet.symbol + rc_heat_power.symbol * rc_resistance.symbol, + "Steady-state temperature equals the coolant supply temperature plus heat power times resistance.", + references=RC_REFS, + check_units=True, +) +eq_rc_temperature_at_time = eq( + "forecast.eq.rc.temperature_at_time", + rc_temperature_at_time.symbol, + rc_steady_temperature.symbol + + (rc_initial_temperature.symbol - rc_steady_temperature.symbol) + * sp.exp(-rc_elapsed_time.symbol / rc_time_constant.symbol), + "Temperature relaxes exponentially from its initial value toward the " + "steady state with the thermal time constant.", + references=RC_REFS, + check_units=True, +) +eq_rc_time_to_throttle_reachable = eq( + "forecast.eq.rc.time_to_throttle_reachable", + rc_time_to_throttle_reachable.symbol, + rc_time_constant.symbol + * sp.log( + (rc_steady_temperature.symbol - rc_initial_temperature.symbol) + / (rc_steady_temperature.symbol - rc_throttle_temperature.symbol) + ), + "Solving the exponential approach for the threshold gives time constant " + "times the log of (steady - initial) over (steady - threshold).", + references=RC_REFS, + check_units=True, +) +eq_rc_time_to_throttle = PiecewiseEquation( + "forecast.eq.rc.time_to_throttle", + rc_time_to_throttle.symbol, + [ + (0, rc_initial_temperature.symbol >= rc_throttle_temperature.symbol), + ( + rc_time_to_throttle_reachable.symbol, + rc_steady_temperature.symbol > rc_throttle_temperature.symbol, + ), + (sp.oo, True), + ], + "Time to throttle is 0 when the node already sits at or above the " + "threshold, the closed-form time when the steady state lies above the " + "threshold, and infinity when the threshold is never reached.", + references=RC_REFS, +) +eq_rc_sustainable_power = eq( + "forecast.eq.rc.sustainable_power", + rc_sustainable_power.symbol, + (rc_throttle_temperature.symbol - T_coolant_inlet.symbol) + / rc_resistance.symbol, + "Sustainable power equals the temperature room between supply and threshold divided by resistance.", + references=RC_REFS, + check_units=True, +) +eq_rc_cooling_margin = eq( + "forecast.eq.rc.cooling_margin", + rc_cooling_margin.symbol, + rc_sustainable_power.symbol - rc_heat_power.symbol, + "Cooling margin equals sustainable power minus the heat power dissipated.", + references=RC_REFS, + check_units=True, +) + +ineq_rc_threshold_above_supply = Inequality( + "forecast.ineq.rc.threshold_above_supply", + rc_throttle_temperature.symbol, + T_coolant_inlet.symbol, + ">", + "The throttle threshold must lie above the coolant supply temperature, " + "otherwise no heat power is sustainable.", + references=RC_REFS, +) + + +POWER_FORECAST_RC_VARIABLES = [ + rc_resistance, + rc_capacitance, + rc_heat_power, + rc_initial_temperature, + rc_elapsed_time, + rc_throttle_temperature, + rc_time_constant, + rc_steady_temperature, + rc_temperature_at_time, + rc_time_to_throttle_reachable, + rc_time_to_throttle, + rc_sustainable_power, + rc_cooling_margin, +] + +POWER_FORECAST_RC_EQUATIONS = [ + eq_rc_time_constant, + eq_rc_steady_temperature, + eq_rc_temperature_at_time, + eq_rc_time_to_throttle_reachable, + eq_rc_time_to_throttle, + eq_rc_sustainable_power, + eq_rc_cooling_margin, + ineq_rc_threshold_above_supply, +] + + +__all__ = [ + "rc_resistance", "rc_capacitance", "rc_heat_power", + "rc_initial_temperature", "rc_elapsed_time", "rc_throttle_temperature", + "rc_time_constant", "rc_steady_temperature", "rc_temperature_at_time", + "rc_time_to_throttle_reachable", "rc_time_to_throttle", + "rc_sustainable_power", "rc_cooling_margin", + "eq_rc_time_constant", "eq_rc_steady_temperature", + "eq_rc_temperature_at_time", "eq_rc_time_to_throttle_reachable", + "eq_rc_time_to_throttle", "eq_rc_sustainable_power", + "eq_rc_cooling_margin", "ineq_rc_threshold_above_supply", + "POWER_FORECAST_RC_VARIABLES", "POWER_FORECAST_RC_EQUATIONS", +] diff --git a/gpu_stack/scopes/power_forecast_refs.py b/gpu_stack/scopes/power_forecast_refs.py new file mode 100644 index 0000000..17586e3 --- /dev/null +++ b/gpu_stack/scopes/power_forecast_refs.py @@ -0,0 +1,207 @@ +""" +The References shared by the forecast-aware power, thermal, and cost scope. + +The scope turns the decisions a quantile forecaster feeds (plan power at a +chosen quantile, checkpoint early, shift flexible work) into equations with +units. Each decision rests on a published result, and each root input that a +forecaster will later supply points at the experiment that produces it. The +References are defined once here so the planning, thermal, checkpoint, and +time-shifting files cite the same sources and stay free of circular imports. + +Two kinds of reference appear. Sources (papers, a textbook) justify the +equation. Model references say, in one sentence, what the equation assumes. +Root inputs that will come from a forecaster carry E003_INPUT_REF and no +default value anywhere in the scope. +""" + +from ..core import Reference, var + + +# --------------------------------------------------------------------------- +# Sources +# --------------------------------------------------------------------------- + +GNEITING_RAFTERY_2007_REF = Reference( + "Gneiting, T. and Raftery, A. E. (2007). Strictly Proper Scoring Rules, " + "Prediction, and Estimation. Journal of the American Statistical " + "Association 102(477), 359-378. A quantile forecast at level q is " + "calibrated when the observation falls below it with probability q.", + kind="paper", + url="https://doi.org/10.1198/016214506000001437", + year=2007, + doi="10.1198/016214506000001437", +) + +KOENKER_2005_REF = Reference( + "Koenker, R. (2005). Quantile Regression. Cambridge University Press. " + "The conditional q-quantile minimizes expected pinball (check) loss, so " + "P(Y > quantile_q) = 1 - q under a correctly specified model.", + kind="textbook", + url="https://doi.org/10.1017/CBO9780511754098", + year=2005, + doi="10.1017/CBO9780511754098", +) + +KOENKER_BASSETT_1978_REF = Reference( + "Koenker, R. and Bassett, G. (1978). Regression Quantiles. Econometrica " + "46(1), 33-50. Defines the quantile loss that quantile forecasters minimize.", + kind="paper", + url="https://doi.org/10.2307/1913643", + year=1978, + doi="10.2307/1913643", +) + +FAN_WEBER_BARROSO_2007_REF = Reference( + "Fan, X., Weber, W.-D., and Barroso, L. A. (2007). Power provisioning for " + "a warehouse-sized computer. Proceedings of the 34th Annual International " + "Symposium on Computer Architecture (ISCA), 13-23. Studies the gap " + "between provisioned power capacity and the power a facility draws.", + kind="paper", + url="https://doi.org/10.1145/1250662.1250665", + year=2007, + doi="10.1145/1250662.1250665", +) + +INCROPERA_LUMPED_REF = Reference( + "Incropera, F. P., DeWitt, D. P., Bergman, T. L., and Lavine, A. S. " + "Fundamentals of Heat and Mass Transfer (Wiley), chapter on transient " + "conduction, lumped capacitance method: for a body with thermal " + "resistance R and heat capacity C, theta(t) = theta_i * exp(-t / (R*C)), " + "where theta is the temperature difference to the surroundings.", + kind="textbook", +) + +YOUNG_1974_REF = Reference( + "Young, J. W. (1974). A first order approximation to the optimum " + "checkpoint interval. Communications of the ACM 17(9), 530-531. A fault " + "arriving at a uniformly random point of a checkpoint interval loses, on " + "average, half an interval of work.", + kind="paper", + url="https://doi.org/10.1145/361147.361115", + year=1974, + doi="10.1145/361147.361115", +) + +DALY_2006_REF = Reference( + "Daly, J. T. (2006). A higher order estimate of the optimum checkpoint " + "interval (title shortened). Future Generation Computer Systems 22(3), " + "303-312. Extends Young's estimate to include checkpoint write time and " + "restart time.", + kind="paper", + url="https://doi.org/10.1016/j.future.2004.11.016", + year=2006, + doi="10.1016/j.future.2004.11.016", +) + +WIESNER_2021_REF = Reference( + "Wiesner, P., Behnke, I., Scheinert, D., Gontarska, K., and Thamsen, L. " + "(2021). Let's wait awhile: how temporal workload shifting can reduce " + "carbon emissions in the cloud. Proceedings of the 22nd International " + "Middleware Conference, 260-272. Shifting delay-tolerant work into " + "lower-carbon hours changes the carbon intensity the work pays.", + kind="paper", + url="https://doi.org/10.1145/3464298.3493399", + year=2021, + doi="10.1145/3464298.3493399", +) + +RADOVANOVIC_2021_REF = Reference( + "Radovanovic, A. et al. (2021). Carbon-Aware Computing for Datacenters. " + "arXiv:2106.11750. Shapes flexible datacenter load against day-ahead " + "grid carbon-intensity forecasts.", + kind="paper", + url="https://arxiv.org/abs/2106.11750", + year=2021, +) + + +# --------------------------------------------------------------------------- +# Where forecaster-supplied root inputs come from +# --------------------------------------------------------------------------- + +E003_INPUT_REF = Reference( + "gpu_stack experiments/e003-heatwave: the value of this input is produced " + "by that experiment's quantile forecasts and replays. The scope sets no " + "default; supply the value from the experiment's result file.", + kind="memo", +) + + +# --------------------------------------------------------------------------- +# Model references: one sentence on what each group of equations assumes +# --------------------------------------------------------------------------- + +QUANTILE_PLANNING_MODEL_REF = Reference( + "Planning at quantile q sets planned site power to the forecast " + "q-quantile; for a calibrated forecast the chance that power exceeds that " + "plan is 1 - q, and headroom under the cap is the cap minus the plan.", + kind="model", +) + +OVERCAP_COST_MODEL_REF = Reference( + "Expected over-cap energy is exceedance probability times mean overshoot " + "power times the planning horizon; its penalty cost is that energy times " + "an over-cap price.", + kind="model", +) + +STRANDED_POWER_MODEL_REF = Reference( + "Stranded power is the provisioned cap minus the expected site power; " + "cap utilization is expected power over the cap.", + kind="model", +) + +LUMPED_RC_MODEL_REF = Reference( + "A single thermal node with resistance R to its coolant supply and heat " + "capacity C, driven by constant heat power Q: time constant R*C, steady " + "state at supply + Q*R, exponential approach between them.", + kind="model", +) + +EARLY_CHECKPOINT_MODEL_REF = Reference( + "A fault with no warning loses about half a checkpoint interval of work " + "on every affected GPU; a warning at least one checkpoint-write long lets " + "the job checkpoint first, so about one write time of work is lost. The " + "expected loss weights the two cases by forecast recall.", + kind="model", +) + +TIME_SHIFT_MODEL_REF = Reference( + "A flexible fraction of the job's energy moves to hours with a different " + "price and carbon intensity; the energy-weighted average paid is the " + "convex combination of the unshifted and shifted values. Shifted work is " + "assumed to use cluster hours that would otherwise sit idle, so " + "non-energy cost per token is unchanged.", + kind="model", +) + + +def forecast_var(name, symbol, units, description, references, **kwargs): + """Declare a Variable in the power_forecast scope with its references.""" + return var( + name, symbol, units, description, + scope="power_forecast", + references=references, + **kwargs, + ) + + +__all__ = [ + "forecast_var", + "GNEITING_RAFTERY_2007_REF", + "KOENKER_2005_REF", + "KOENKER_BASSETT_1978_REF", + "FAN_WEBER_BARROSO_2007_REF", + "INCROPERA_LUMPED_REF", + "YOUNG_1974_REF", + "DALY_2006_REF", + "WIESNER_2021_REF", + "RADOVANOVIC_2021_REF", + "E003_INPUT_REF", + "QUANTILE_PLANNING_MODEL_REF", + "OVERCAP_COST_MODEL_REF", + "STRANDED_POWER_MODEL_REF", + "LUMPED_RC_MODEL_REF", + "EARLY_CHECKPOINT_MODEL_REF", + "TIME_SHIFT_MODEL_REF", +] diff --git a/gpu_stack/scopes/power_forecast_shift.py b/gpu_stack/scopes/power_forecast_shift.py new file mode 100644 index 0000000..a15f0e8 --- /dev/null +++ b/gpu_stack/scopes/power_forecast_shift.py @@ -0,0 +1,305 @@ +""" +scopes/power_forecast_shift.py +============================== + +Trading dollars per token against carbon per token by moving flexible work. + +Some of a job's energy is flexible: it can run in a different hour without +changing what the job produces. Price and grid carbon intensity both vary by +hour, and a forecast of each says which hours are cheaper and cleaner. If a +fraction phi of the job's energy moves to hours with price p_sh and carbon +intensity I_sh while the rest stays at the economics scope's blended price +and carbon intensity, the energy-weighted average the job pays is the convex +combination (1 - phi) * unshifted + phi * shifted. + +Dividing by throughput turns that into per-token numbers. Facility energy per +token is the job's allocated site power over tokens per second (the same +power the economics scope bills), so electricity cost per token is that energy +times the average price paid, and carbon per token is that energy times the +average intensity paid. Total cost per token starts from the economics +scope's cost per token and swaps in the shifted electricity and carbon cost. +Shifted work is assumed to use cluster hours that would otherwise sit idle, +so every non-energy cost per token is unchanged. + +The shifted hours' price and carbon intensity come from the Heatwave +experiment's price and carbon forecasts and have no default here. +""" + +import sympy as sp + +from ..core import eq +from ..core.units import JOULE, KILOGRAM, KWH + +from .economics_capex_refs import USD +from .economics_opex import ( + carbon_intensity_kg_per_kwh, + carbon_price_per_tonne, + job_dc_power, + price_kwh, +) +from .economics_recovery import cost_per_token +from .training_scaling import tokens_per_sec +from .power_forecast_refs import ( + forecast_var, + E003_INPUT_REF, + RADOVANOVIC_2021_REF, + TIME_SHIFT_MODEL_REF, + WIESNER_2021_REF, +) + + +DIMENSIONLESS = sp.Integer(1) +ENERGY_PRICE_KWH = USD / KWH +CARBON_INTENSITY_KWH = KILOGRAM / KWH + +SHIFT_REFS = [TIME_SHIFT_MODEL_REF, WIESNER_2021_REF, RADOVANOVIC_2021_REF] +SHIFT_INPUT_REFS = SHIFT_REFS + [E003_INPUT_REF] + +JOULES_PER_KWH = sp.Integer(3_600_000) +KG_PER_TONNE = sp.Integer(1000) + + +# --------------------------------------------------------------------------- +# Root inputs: the flexible fraction and the shifted hours' price and carbon +# --------------------------------------------------------------------------- + +flexible_fraction = forecast_var( + "forecast.shift.flexible_fraction", "phi_flex_fc", "dimensionless", + "Fraction of the job's energy that is moved to the shifted hours " + "(0 keeps the job where it is, 1 moves all of it).", + SHIFT_REFS, + value_range=(0.0, 1.0), + sp_units=DIMENSIONLESS, +) +price_shifted = forecast_var( + "forecast.shift.price_shifted", "p_sh_fc", "USD/(kW*h)", + "Energy-weighted average electricity price of the hours the flexible " + "work moves to, read from the price forecast.", + SHIFT_INPUT_REFS, + nonnegative=True, + sp_units=ENERGY_PRICE_KWH, +) +carbon_intensity_shifted = forecast_var( + "forecast.shift.carbon_intensity_shifted", "I_sh_fc", "kg/(kW*h)", + "Energy-weighted average grid carbon intensity of the hours the flexible " + "work moves to, read from the carbon-intensity forecast.", + SHIFT_INPUT_REFS, + nonnegative=True, + sp_units=CARBON_INTENSITY_KWH, +) + + +# --------------------------------------------------------------------------- +# Derived: energy-weighted averages paid, then per-token energy, cost, carbon +# --------------------------------------------------------------------------- + +price_paid = forecast_var( + "forecast.shift.price_paid", "p_paid_fc", "USD/(kW*h)", + "Energy-weighted average electricity price paid after shifting: the " + "economics scope's blended price for the unshifted share and the shifted " + "hours' price for the shifted share.", + SHIFT_REFS, + nonnegative=True, + sp_units=ENERGY_PRICE_KWH, +) +carbon_intensity_paid = forecast_var( + "forecast.shift.carbon_intensity_paid", "I_paid_fc", "kg/(kW*h)", + "Energy-weighted average carbon intensity paid after shifting: the " + "economics scope's grid intensity for the unshifted share and the " + "shifted hours' intensity for the shifted share.", + SHIFT_REFS, + nonnegative=True, + sp_units=CARBON_INTENSITY_KWH, +) +facility_energy_per_token = forecast_var( + "forecast.shift.facility_energy_per_token", "e_tok_fac_fc", "J/token", + "Facility energy per training token: the job's allocated site power " + "divided by tokens per second.", + SHIFT_REFS, + nonnegative=True, + sp_units=JOULE, +) +energy_cost_per_token = forecast_var( + "forecast.shift.energy_cost_per_token", "c_e_tok_fc", "USD/token", + "Electricity cost per token after shifting: facility energy per token " + "times the average price paid.", + SHIFT_REFS, + nonnegative=True, + sp_units=USD, +) +carbon_per_token_unshifted = forecast_var( + "forecast.shift.carbon_per_token_unshifted", "m_tok0_fc", "kg/token", + "Carbon emitted per token with no shifting: facility energy per token " + "times the economics scope's grid carbon intensity.", + SHIFT_REFS, + nonnegative=True, + sp_units=KILOGRAM, +) +carbon_per_token = forecast_var( + "forecast.shift.carbon_per_token", "m_tok_fc", "kg/token", + "Carbon emitted per token after shifting: facility energy per token " + "times the average carbon intensity paid.", + SHIFT_REFS, + nonnegative=True, + sp_units=KILOGRAM, +) +cost_per_token_shifted = forecast_var( + "forecast.shift.cost_per_token", "C_tok_sh_fc", "USD/token", + "Fully allocated cost per token after shifting: the economics scope's " + "cost per token, with its electricity cost replaced by the shifted " + "electricity cost and its carbon cost replaced by the shifted carbon " + "cost. Non-energy costs are unchanged.", + SHIFT_REFS, + signed=True, + sp_units=USD, +) +cost_saving_per_token = forecast_var( + "forecast.shift.cost_saving_per_token", "dC_tok_fc", "USD/token", + "Dollars saved per token by shifting: economics cost per token minus " + "shifted cost per token. Negative when shifting costs more.", + SHIFT_REFS, + signed=True, + sp_units=USD, +) +carbon_saving_per_token = forecast_var( + "forecast.shift.carbon_saving_per_token", "dm_tok_fc", "kg/token", + "Carbon avoided per token by shifting: unshifted carbon per token minus " + "shifted carbon per token. Negative when shifting emits more.", + SHIFT_REFS, + signed=True, + sp_units=KILOGRAM, +) + + +# --------------------------------------------------------------------------- +# Equations +# --------------------------------------------------------------------------- + +eq_price_paid = eq( + "forecast.eq.shift.price_paid", + price_paid.symbol, + (1 - flexible_fraction.symbol) * price_kwh.symbol + + flexible_fraction.symbol * price_shifted.symbol, + "Average price paid is the energy-weighted mix of the unshifted blended price and the shifted hours' price.", + references=SHIFT_REFS, + check_units=True, +) +eq_carbon_intensity_paid = eq( + "forecast.eq.shift.carbon_intensity_paid", + carbon_intensity_paid.symbol, + (1 - flexible_fraction.symbol) * carbon_intensity_kg_per_kwh.symbol + + flexible_fraction.symbol * carbon_intensity_shifted.symbol, + "Average carbon intensity paid is the energy-weighted mix of the unshifted grid intensity and the shifted hours' intensity.", + references=SHIFT_REFS, + check_units=True, +) +eq_facility_energy_per_token = eq( + "forecast.eq.shift.facility_energy_per_token", + facility_energy_per_token.symbol, + job_dc_power.symbol / tokens_per_sec.symbol, + "Facility energy per token equals the job's allocated site power divided by tokens per second.", + references=SHIFT_REFS, + check_units=True, +) +eq_energy_cost_per_token = eq( + "forecast.eq.shift.energy_cost_per_token", + energy_cost_per_token.symbol, + facility_energy_per_token.symbol * price_paid.symbol / JOULES_PER_KWH, + "Electricity cost per token equals energy per token times the average " + "price paid, with joules converted to kilowatt-hours (3.6e6 J per kWh).", + references=SHIFT_REFS, + check_units=True, +) +eq_carbon_per_token_unshifted = eq( + "forecast.eq.shift.carbon_per_token_unshifted", + carbon_per_token_unshifted.symbol, + facility_energy_per_token.symbol * carbon_intensity_kg_per_kwh.symbol + / JOULES_PER_KWH, + "Unshifted carbon per token equals energy per token times grid carbon " + "intensity, with joules converted to kilowatt-hours.", + references=SHIFT_REFS, + check_units=True, +) +eq_carbon_per_token = eq( + "forecast.eq.shift.carbon_per_token", + carbon_per_token.symbol, + facility_energy_per_token.symbol * carbon_intensity_paid.symbol + / JOULES_PER_KWH, + "Shifted carbon per token equals energy per token times the average " + "carbon intensity paid, with joules converted to kilowatt-hours.", + references=SHIFT_REFS, + check_units=True, +) +eq_cost_per_token_shifted = eq( + "forecast.eq.shift.cost_per_token", + cost_per_token_shifted.symbol, + cost_per_token.symbol + + facility_energy_per_token.symbol + * (price_paid.symbol - price_kwh.symbol) / JOULES_PER_KWH + + carbon_price_per_tonne.symbol + * (carbon_per_token.symbol - carbon_per_token_unshifted.symbol) + / KG_PER_TONNE, + "Shifted cost per token equals the economics cost per token plus the " + "change in electricity cost and the change in carbon cost per token.", + references=SHIFT_REFS, + check_units=True, +) +eq_cost_saving_per_token = eq( + "forecast.eq.shift.cost_saving_per_token", + cost_saving_per_token.symbol, + cost_per_token.symbol - cost_per_token_shifted.symbol, + "Cost saving per token equals economics cost per token minus shifted cost per token.", + references=SHIFT_REFS, + check_units=True, +) +eq_carbon_saving_per_token = eq( + "forecast.eq.shift.carbon_saving_per_token", + carbon_saving_per_token.symbol, + carbon_per_token_unshifted.symbol - carbon_per_token.symbol, + "Carbon saving per token equals unshifted carbon per token minus shifted carbon per token.", + references=SHIFT_REFS, + check_units=True, +) + + +POWER_FORECAST_SHIFT_VARIABLES = [ + flexible_fraction, + price_shifted, + carbon_intensity_shifted, + price_paid, + carbon_intensity_paid, + facility_energy_per_token, + energy_cost_per_token, + carbon_per_token_unshifted, + carbon_per_token, + cost_per_token_shifted, + cost_saving_per_token, + carbon_saving_per_token, +] + +POWER_FORECAST_SHIFT_EQUATIONS = [ + eq_price_paid, + eq_carbon_intensity_paid, + eq_facility_energy_per_token, + eq_energy_cost_per_token, + eq_carbon_per_token_unshifted, + eq_carbon_per_token, + eq_cost_per_token_shifted, + eq_cost_saving_per_token, + eq_carbon_saving_per_token, +] + + +__all__ = [ + "flexible_fraction", "price_shifted", "carbon_intensity_shifted", + "price_paid", "carbon_intensity_paid", "facility_energy_per_token", + "energy_cost_per_token", "carbon_per_token_unshifted", "carbon_per_token", + "cost_per_token_shifted", "cost_saving_per_token", + "carbon_saving_per_token", + "eq_price_paid", "eq_carbon_intensity_paid", + "eq_facility_energy_per_token", "eq_energy_cost_per_token", + "eq_carbon_per_token_unshifted", "eq_carbon_per_token", + "eq_cost_per_token_shifted", "eq_cost_saving_per_token", + "eq_carbon_saving_per_token", + "POWER_FORECAST_SHIFT_VARIABLES", "POWER_FORECAST_SHIFT_EQUATIONS", +] diff --git a/tests/test_power_forecast_scope.py b/tests/test_power_forecast_scope.py new file mode 100644 index 0000000..cfd5ad1 --- /dev/null +++ b/tests/test_power_forecast_scope.py @@ -0,0 +1,641 @@ +"""Tests for the forecast-aware power, thermal, and cost scope. + +The scope puts the equations behind forecast-driven operation into the map: +planning site power at a forecast quantile, stranded power, a lumped RC +thermal model, the value of an early checkpoint, and time-shifted cost and +carbon per token. These tests cover each group three ways. Metadata: every +variable has a unit and a reference, every equation has a reference, and the +unit-checked set is pinned. Numbers: each equation is evaluated through the +resolver at inputs chosen so the answer can be derived by hand, and the +expected value is computed in the test from those same inputs. Behavior: the +limiting cases (never reaches the throttle threshold, no recall, no shifting) +come out where the algebra says they must. A unit check that has been +deliberately broken must raise, so the checks are known to bite. + +Inputs in these tests are arbitrary numbers picked for arithmetic, not +measurements of any system. The one sampling test uses simulated (synthetic) +draws from a normal distribution to check the exceedance rule 1 - q. +""" + +import math + +import numpy as np +import pytest +import sympy as sp + +import gpu_stack +from gpu_stack import Registry, resolve +from gpu_stack.core.units import ( + HOUR, JOULE, KELVIN, KILOGRAM, KWH, SECOND, WATT, UnitError, + check_equation_units, infer_expr_units_with_coefficient, +) +from gpu_stack.scopes import ( + SCOPE_DESCRIPTIONS, SCOPE_MODULES, power_forecast as pf, +) +from gpu_stack.scopes.economics_capex_refs import USD + + +# Equations that carry a unit check. Pinned on purpose: the one piecewise +# relation is not unit-checked (the checker cannot infer a Piecewise), and +# its closed-form branch is checked on its own; inequalities state bounds. +UNCHECKED_POWER_FORECAST_RELATIONS = { + "forecast.eq.rc.time_to_throttle", + "forecast.ineq.plan.quantile_level_below_one", + "forecast.ineq.rc.threshold_above_supply", + "forecast.ineq.ckpt.lead_time_covers_checkpoint", +} + + +def value(variable_name, **assignments): + """Resolve a variable with assignments keyed by registered variable name.""" + result = resolve(variable_name, assignments=assignments) + assert result.value is not None, result + return result.value + + +def constraint(variable_name, equation, **assignments): + result = resolve(variable_name, assignments=assignments) + return next(c for c in result.constraints if c.equation == equation) + + +# --------------------------------------------------------------------------- +# Registration and metadata +# --------------------------------------------------------------------------- + +def test_scope_is_registered_after_economics(): + assert "power_forecast" in SCOPE_MODULES + assert SCOPE_MODULES.index("power_forecast") > SCOPE_MODULES.index("economics") + assert SCOPE_DESCRIPTIONS["power_forecast"] + assert "power_forecast" in Registry.systems + + +def test_every_variable_and_equation_has_units_and_references(): + assert pf.POWER_FORECAST_VARIABLES + assert [v.name for v in pf.POWER_FORECAST_VARIABLES if v.sp_units is None] == [] + assert [v.name for v in pf.POWER_FORECAST_VARIABLES if not v.references] == [] + assert [e.name for e in pf.POWER_FORECAST_EQUATIONS if not e.references] == [] + assert all(v.scope == "power_forecast" for v in pf.POWER_FORECAST_VARIABLES) + + +def test_unit_checked_equations_are_explicitly_curated(): + unchecked = { + e.name + for e in pf.POWER_FORECAST_EQUATIONS + if not getattr(e, "_check_units_flag", False) + } + assert unchecked == UNCHECKED_POWER_FORECAST_RELATIONS + + +def test_names_follow_the_forecast_prefix_and_are_unique(): + names = [v.name for v in pf.POWER_FORECAST_VARIABLES] + assert len(names) == len(set(names)) + assert all(n.startswith("forecast.") for n in names) + eq_names = [e.name for e in pf.POWER_FORECAST_EQUATIONS] + assert len(eq_names) == len(set(eq_names)) + assert all(n.startswith("forecast.eq.") or n.startswith("forecast.ineq.") for n in eq_names) + + +def test_sources_are_cited_with_links(): + sources = { + r + for v in pf.POWER_FORECAST_VARIABLES + for r in v.references + if r.kind in {"paper", "textbook"} + } + # Gneiting & Raftery, Koenker, Koenker & Bassett, Fan et al., Incropera, + # Young, Daly, Wiesner et al., Radovanovic et al. + assert len(sources) == 9 + for ref in sources: + if ref.kind == "paper": + assert ref.url and ref.year + + +def test_forecaster_inputs_point_at_the_experiment_and_carry_no_default(): + for name in ( + "forecast.plan.site_power_planned", + "forecast.plan.site_power_expected", + "forecast.plan.overshoot_power_mean", + "forecast.rc.heat_power", + "forecast.ckpt.lead_time", + "forecast.ckpt.recall", + "forecast.shift.price_shifted", + "forecast.shift.carbon_intensity_shifted", + ): + v = Registry.variables[name] + assert v.is_root_input, name + assert any("e003-heatwave" in r.citation for r in v.references), name + + +def test_the_scope_adds_no_cycles_and_no_duplicate_of_existing_variables(): + assert gpu_stack.find_cycles() == [] + reused = { + "thermal.dc.pue", + "thermal.coolant.t_inlet", + "cluster.rel.checkpoint_time", + "cluster.rel.optimal_checkpoint_interval", + "econ.power.price_kwh", + "econ.carbon.intensity_kg_per_kwh", + "econ.carbon.price_per_tonne", + "econ.job.dc_power", + "econ.cost.per_token", + "training.tokens_per_sec", + } + used = { + v.name + for e in pf.POWER_FORECAST_EQUATIONS + for v in e.variables_in_relation() + } + assert reused <= used + + +def test_representative_units(): + assert pf.site_power_cap.sp_units == WATT + assert pf.overcap_energy.sp_units == KWH + assert pf.overcap_penalty_cost.sp_units == USD + assert pf.rc_resistance.sp_units == KELVIN / WATT + assert pf.rc_capacitance.sp_units == JOULE / KELVIN + assert pf.rc_time_constant.sp_units == SECOND + assert pf.rc_steady_temperature.sp_units == KELVIN + assert pf.lost_work_unwarned.sp_units == HOUR + assert pf.facility_energy_per_token.sp_units == JOULE + assert pf.carbon_per_token.sp_units == KILOGRAM + assert pf.price_paid.sp_units == USD / KWH + + +def _units_agree(equation, rhs): + symbol_units = { + v.symbol: v.sp_units for v in Registry.variables.values() if v.sp_units is not None + } + lhs_u = infer_expr_units_with_coefficient(equation.lhs, symbol_units, equation.name) + rhs_u = infer_expr_units_with_coefficient(rhs, symbol_units, equation.name) + check_equation_units(lhs_u, rhs_u, equation.name) + + +def test_unit_checks_pass_as_written_and_raise_when_broken(): + for equation in pf.POWER_FORECAST_EQUATIONS: + if getattr(equation, "_check_units_flag", False): + _units_agree(equation, equation.rhs) + + # Dropping or doubling a conversion factor must be caught. + broken = { + pf.eq_overcap_energy: pf.eq_overcap_energy.rhs * sp.Integer(3_600_000), + pf.eq_lost_work_unwarned: pf.eq_lost_work_unwarned.rhs * sp.Integer(3600), + pf.eq_energy_cost_per_token: pf.eq_energy_cost_per_token.rhs * sp.Integer(3_600_000), + } + # A time constant swapped for a resistance changes the exponent's dimension. + broken[pf.eq_rc_temperature_at_time] = pf.eq_rc_temperature_at_time.rhs.subs( + pf.rc_time_constant.symbol, pf.rc_resistance.symbol + ) + for equation, bad_rhs in broken.items(): + with pytest.raises(UnitError): + _units_agree(equation, bad_rhs) + + +# --------------------------------------------------------------------------- +# Group 1: planning at a quantile, packing, exceedance, over-cap energy +# --------------------------------------------------------------------------- + +def test_headroom_and_packable_gpus(): + cap, planned = 10.0e6, 8.0e6 + gpu_it_power, pue = 700.0, 1.25 + headroom = cap - planned + assert value( + "forecast.plan.headroom", + **{"forecast.plan.site_power_cap": cap, "forecast.plan.site_power_planned": planned}, + ) == pytest.approx(headroom) + per_gpu_site = gpu_it_power * pue + assert value( + "forecast.plan.gpu_site_power_planned", + **{"forecast.plan.gpu_power_planned": gpu_it_power, "thermal.dc.pue": pue}, + ) == pytest.approx(per_gpu_site) + assert value( + "forecast.plan.n_gpus_packable", + **{ + "forecast.plan.site_power_cap": cap, + "forecast.plan.site_power_planned": planned, + "forecast.plan.gpu_power_planned": gpu_it_power, + "thermal.dc.pue": pue, + }, + ) == pytest.approx(headroom / per_gpu_site) + + +def test_plan_over_the_cap_gives_negative_headroom_and_gpus_to_remove(): + inputs = { + "forecast.plan.site_power_cap": 5.0e6, + "forecast.plan.site_power_planned": 6.0e6, + "forecast.plan.gpu_power_planned": 800.0, + "thermal.dc.pue": 1.0, + } + assert value("forecast.plan.headroom", **inputs) == pytest.approx(-1.0e6) + assert value("forecast.plan.n_gpus_packable", **inputs) == pytest.approx(-1.0e6 / 800.0) + + +@pytest.mark.parametrize("q", [0.5, 0.9, 0.95, 0.99]) +def test_exceedance_probability_is_one_minus_q(q): + assert value( + "forecast.plan.exceedance_probability", **{"forecast.plan.quantile_level": q} + ) == pytest.approx(1.0 - q) + + +def test_calibrated_quantile_exceedance_matches_one_minus_q_on_synthetic_draws(): + # Simulated (synthetic) site power draws, not a measurement. + rng = np.random.default_rng(0) + draws = rng.normal(loc=8.0e6, scale=0.5e6, size=200_000) + for q in (0.5, 0.9, 0.99): + planned = float(np.quantile(draws, q)) + empirical = float(np.mean(draws > planned)) + predicted = value( + "forecast.plan.exceedance_probability", **{"forecast.plan.quantile_level": q} + ) + assert empirical == pytest.approx(predicted, abs=0.004) + + +def test_overcap_energy_and_penalty(): + q, overshoot, horizon, price = 0.9, 5.0e5, 3600.0, 0.2 + p_exceed = 1.0 - q + energy_kwh = p_exceed * overshoot * horizon / 3.6e6 + inputs = { + "forecast.plan.quantile_level": q, + "forecast.plan.overshoot_power_mean": overshoot, + "forecast.plan.horizon": horizon, + "forecast.plan.overcap_price": price, + } + assert value("forecast.plan.overcap_energy", **inputs) == pytest.approx(energy_kwh) + assert value("forecast.plan.overcap_penalty_cost", **inputs) == pytest.approx( + energy_kwh * price + ) + + +def test_raising_the_quantile_lowers_expected_overcap_penalty(): + base = { + "forecast.plan.overshoot_power_mean": 5.0e5, + "forecast.plan.horizon": 7200.0, + "forecast.plan.overcap_price": 0.1, + } + costs = [ + value("forecast.plan.overcap_penalty_cost", **base, **{"forecast.plan.quantile_level": q}) + for q in (0.5, 0.9, 0.99) + ] + assert costs[0] > costs[1] > costs[2] > 0 + + +def test_quantile_level_must_lie_strictly_between_zero_and_one(): + for q, ok in ((0.5, True), (0.0, False), (1.0, False)): + above = constraint( + "forecast.plan.exceedance_probability", + "domain.forecast.plan.quantile_level.positive", + **{"forecast.plan.quantile_level": q}, + ) + below = constraint( + "forecast.plan.exceedance_probability", + "forecast.ineq.plan.quantile_level_below_one", + **{"forecast.plan.quantile_level": q}, + ) + assert (above.satisfied and below.satisfied) is ok + + +# --------------------------------------------------------------------------- +# Group 2: stranded power +# --------------------------------------------------------------------------- + +def test_stranded_power_utilization_and_reserve(): + cap, expected, planned = 10.0e6, 6.0e6, 8.0e6 + inputs = { + "forecast.plan.site_power_cap": cap, + "forecast.plan.site_power_expected": expected, + "forecast.plan.site_power_planned": planned, + } + assert value("forecast.plan.stranded_power", **inputs) == pytest.approx(cap - expected) + assert value("forecast.plan.cap_utilization", **inputs) == pytest.approx(expected / cap) + assert value("forecast.plan.uncertainty_reserve", **inputs) == pytest.approx(planned - expected) + + +def test_stranded_power_plus_utilization_account_for_the_whole_cap(): + inputs = { + "forecast.plan.site_power_cap": 12.0e6, + "forecast.plan.site_power_expected": 9.0e6, + } + stranded = value("forecast.plan.stranded_power", **inputs) + utilization = value("forecast.plan.cap_utilization", **inputs) + assert stranded / inputs["forecast.plan.site_power_cap"] == pytest.approx(1.0 - utilization) + + +# --------------------------------------------------------------------------- +# Group 3: lumped RC thermal model +# --------------------------------------------------------------------------- + +RC = { + "forecast.rc.resistance": 0.05, # K/W + "forecast.rc.capacitance": 2000.0, # J/K + "forecast.rc.heat_power": 700.0, # W + "thermal.coolant.t_inlet": 300.0, # K + "forecast.rc.initial_temperature": 310.0, + "forecast.rc.throttle_temperature": 330.0, + "forecast.rc.elapsed_time": 60.0, +} + + +def rc(**overrides): + return {**RC, **overrides} + + +def test_rc_time_constant_and_steady_state(): + tau = RC["forecast.rc.resistance"] * RC["forecast.rc.capacitance"] + steady = RC["thermal.coolant.t_inlet"] + RC["forecast.rc.heat_power"] * RC["forecast.rc.resistance"] + assert value("forecast.rc.time_constant", **RC) == pytest.approx(tau) + assert value("forecast.rc.steady_temperature", **RC) == pytest.approx(steady) + + +def test_rc_temperature_after_time_matches_the_closed_form(): + tau = RC["forecast.rc.resistance"] * RC["forecast.rc.capacitance"] + steady = RC["thermal.coolant.t_inlet"] + RC["forecast.rc.heat_power"] * RC["forecast.rc.resistance"] + t0 = RC["forecast.rc.initial_temperature"] + for t in (0.0, 30.0, tau, 5 * tau, 50 * tau): + expected = steady + (t0 - steady) * math.exp(-t / tau) + assert value( + "forecast.rc.temperature_at_time", **rc(**{"forecast.rc.elapsed_time": t}) + ) == pytest.approx(expected) + + +def test_rc_temperature_limits_and_one_time_constant(): + tau = RC["forecast.rc.resistance"] * RC["forecast.rc.capacitance"] + steady = RC["thermal.coolant.t_inlet"] + RC["forecast.rc.heat_power"] * RC["forecast.rc.resistance"] + t0 = RC["forecast.rc.initial_temperature"] + + def at(t): + return value("forecast.rc.temperature_at_time", **rc(**{"forecast.rc.elapsed_time": t})) + + assert at(0.0) == pytest.approx(t0) + assert at(100 * tau) == pytest.approx(steady) + # After one time constant the gap to steady state has shrunk to 1/e. + assert at(tau) - steady == pytest.approx((t0 - steady) / math.e) + + +def test_rc_time_to_throttle_is_the_time_the_closed_form_reaches_the_threshold(): + tau = RC["forecast.rc.resistance"] * RC["forecast.rc.capacitance"] + steady = RC["thermal.coolant.t_inlet"] + RC["forecast.rc.heat_power"] * RC["forecast.rc.resistance"] + t0, thr = RC["forecast.rc.initial_temperature"], RC["forecast.rc.throttle_temperature"] + expected = tau * math.log((steady - t0) / (steady - thr)) + t_hit = value("forecast.rc.time_to_throttle", **RC) + assert t_hit == pytest.approx(expected) + # Round trip: evaluated at that time, the temperature equals the threshold. + assert value( + "forecast.rc.temperature_at_time", **rc(**{"forecast.rc.elapsed_time": t_hit}) + ) == pytest.approx(thr) + + +def test_rc_time_to_throttle_is_infinite_when_the_threshold_is_never_reached(): + # Steady state 325 K sits below the 330 K threshold. + low_power = rc(**{"forecast.rc.heat_power": 500.0}) + assert value("forecast.rc.time_to_throttle", **low_power) == sp.oo + # Steady state exactly at the threshold is approached but not reached. + at_threshold = rc(**{"forecast.rc.heat_power": 600.0}) + assert value("forecast.rc.time_to_throttle", **at_threshold) == sp.oo + + +def test_rc_time_to_throttle_is_zero_when_already_at_or_above_the_threshold(): + for t0 in (330.0, 335.0): + assert value( + "forecast.rc.time_to_throttle", **rc(**{"forecast.rc.initial_temperature": t0}) + ) == 0 + + +def test_rc_cooling_margin_sign_matches_whether_the_threshold_is_reached(): + sustainable = (RC["forecast.rc.throttle_temperature"] - RC["thermal.coolant.t_inlet"]) / RC["forecast.rc.resistance"] + assert value("forecast.rc.sustainable_power", **RC) == pytest.approx(sustainable) + for power in (500.0, 599.0, 601.0, 700.0): + margin = value("forecast.rc.cooling_margin", **rc(**{"forecast.rc.heat_power": power})) + assert margin == pytest.approx(sustainable - power) + t_hit = value("forecast.rc.time_to_throttle", **rc(**{"forecast.rc.heat_power": power})) + assert (margin > 0) == (t_hit == sp.oo) + + +def test_rc_matches_the_package_junction_temperature_in_the_thermal_scope(): + # One package: R = theta_ja and Q = package power, so the RC steady state + # equals the thermal scope's junction temperature. + theta_jc, theta_ca, power, supply = 0.015, 0.025, 1000.0, 305.0 + theta_ja = theta_jc + theta_ca + junction = value( + "thermal.t_junction", + **{ + "thermal.coolant.t_inlet": supply, + "thermal.theta_ca": theta_ca, + "thermal.theta_jc": theta_jc, + "gpu.power.total": power, + }, + ) + steady = value( + "forecast.rc.steady_temperature", + **{ + "forecast.rc.resistance": theta_ja, + "forecast.rc.heat_power": power, + "thermal.coolant.t_inlet": supply, + }, + ) + assert steady == pytest.approx(junction) + + +def test_rc_threshold_must_lie_above_supply_temperature(): + ok = constraint( + "forecast.rc.sustainable_power", "forecast.ineq.rc.threshold_above_supply", **RC + ) + bad = constraint( + "forecast.rc.sustainable_power", + "forecast.ineq.rc.threshold_above_supply", + **rc(**{"forecast.rc.throttle_temperature": 295.0}), + ) + assert ok.satisfied is True + assert bad.satisfied is False + + +# --------------------------------------------------------------------------- +# Group 4: early checkpoint value +# --------------------------------------------------------------------------- + +CKPT = { + "cluster.rel.optimal_checkpoint_interval": 3600.0, # s + "cluster.rel.checkpoint_time": 120.0, # s + "forecast.ckpt.gpus_affected": 1024, + "forecast.ckpt.recall": 0.8, + "forecast.ckpt.lead_time": 300.0, +} + + +def test_lost_work_without_and_with_warning(): + unwarned = 0.5 * CKPT["cluster.rel.optimal_checkpoint_interval"] * CKPT["forecast.ckpt.gpus_affected"] / 3600.0 + warned = CKPT["cluster.rel.checkpoint_time"] * CKPT["forecast.ckpt.gpus_affected"] / 3600.0 + assert value("forecast.ckpt.lost_work_unwarned", **CKPT) == pytest.approx(unwarned) + assert value("forecast.ckpt.lost_work_warned", **CKPT) == pytest.approx(warned) + # 0.5 h on 1024 GPUs is 512 GPU-hours; one 120 s write is 1024/30 GPU-hours. + assert unwarned == pytest.approx(512.0) + assert warned == pytest.approx(1024.0 / 30.0) + + +def test_expected_loss_and_gpu_hours_saved_weight_the_cases_by_recall(): + unwarned = 0.5 * 3600.0 * 1024 / 3600.0 + warned = 120.0 * 1024 / 3600.0 + r = CKPT["forecast.ckpt.recall"] + expected = r * warned + (1 - r) * unwarned + assert value("forecast.ckpt.lost_work_expected", **CKPT) == pytest.approx(expected) + assert value("forecast.ckpt.gpu_hours_saved", **CKPT) == pytest.approx(unwarned - expected) + assert value("forecast.ckpt.gpu_hours_saved", **CKPT) == pytest.approx(r * (unwarned - warned)) + + +def test_recall_zero_saves_nothing_and_recall_one_saves_the_full_difference(): + unwarned = 0.5 * 3600.0 * 1024 / 3600.0 + warned = 120.0 * 1024 / 3600.0 + none = {**CKPT, "forecast.ckpt.recall": 0.0} + full = {**CKPT, "forecast.ckpt.recall": 1.0} + assert value("forecast.ckpt.gpu_hours_saved", **none) == pytest.approx(0.0) + assert value("forecast.ckpt.gpu_hours_saved", **full) == pytest.approx(unwarned - warned) + + +def test_gpu_hours_saved_scale_with_job_size_and_interval(): + base = value("forecast.ckpt.gpu_hours_saved", **CKPT) + double_gpus = value("forecast.ckpt.gpu_hours_saved", **{**CKPT, "forecast.ckpt.gpus_affected": 2048}) + assert double_gpus == pytest.approx(2 * base) + longer = value( + "forecast.ckpt.gpu_hours_saved", + **{**CKPT, "cluster.rel.optimal_checkpoint_interval": 7200.0}, + ) + assert longer > base + + +def test_lead_time_must_cover_the_checkpoint_write(): + ok = constraint( + "forecast.ckpt.gpu_hours_saved", "forecast.ineq.ckpt.lead_time_covers_checkpoint", **CKPT + ) + short = constraint( + "forecast.ckpt.gpu_hours_saved", + "forecast.ineq.ckpt.lead_time_covers_checkpoint", + **{**CKPT, "forecast.ckpt.lead_time": 60.0}, + ) + exact = constraint( + "forecast.ckpt.gpu_hours_saved", + "forecast.ineq.ckpt.lead_time_covers_checkpoint", + **{**CKPT, "forecast.ckpt.lead_time": 120.0}, + ) + assert ok.satisfied is True + assert short.satisfied is False + assert exact.satisfied is True + + +# --------------------------------------------------------------------------- +# Group 5: time-shifting flexible work +# --------------------------------------------------------------------------- + +SHIFT = { + "forecast.shift.flexible_fraction": 0.25, + "econ.power.price_kwh": 0.10, # USD/kWh, unshifted blended + "forecast.shift.price_shifted": 0.04, # USD/kWh + "econ.carbon.intensity_kg_per_kwh": 0.40, # kg/kWh, unshifted + "forecast.shift.carbon_intensity_shifted": 0.10, + "econ.job.dc_power": 1.0e6, # W + "training.tokens_per_sec": 2.0e5, # tokens/s + "econ.cost.per_token": 1.0e-6, # USD/token + "econ.carbon.price_per_tonne": 50.0, # USD/t +} + + +def test_energy_weighted_price_and_carbon_paid(): + phi = SHIFT["forecast.shift.flexible_fraction"] + price = (1 - phi) * SHIFT["econ.power.price_kwh"] + phi * SHIFT["forecast.shift.price_shifted"] + carbon = ( + (1 - phi) * SHIFT["econ.carbon.intensity_kg_per_kwh"] + + phi * SHIFT["forecast.shift.carbon_intensity_shifted"] + ) + assert value("forecast.shift.price_paid", **SHIFT) == pytest.approx(price) + assert value("forecast.shift.carbon_intensity_paid", **SHIFT) == pytest.approx(carbon) + + +def test_facility_energy_cost_and_carbon_per_token(): + energy = SHIFT["econ.job.dc_power"] / SHIFT["training.tokens_per_sec"] # J/token + phi = SHIFT["forecast.shift.flexible_fraction"] + price = (1 - phi) * 0.10 + phi * 0.04 + carbon = (1 - phi) * 0.40 + phi * 0.10 + assert energy == pytest.approx(5.0) + assert value("forecast.shift.facility_energy_per_token", **SHIFT) == pytest.approx(energy) + assert value("forecast.shift.energy_cost_per_token", **SHIFT) == pytest.approx( + energy * price / 3.6e6 + ) + assert value("forecast.shift.carbon_per_token", **SHIFT) == pytest.approx( + energy * carbon / 3.6e6 + ) + assert value("forecast.shift.carbon_per_token_unshifted", **SHIFT) == pytest.approx( + energy * 0.40 / 3.6e6 + ) + + +def test_shifted_cost_per_token_swaps_in_shifted_electricity_and_carbon_cost(): + phi = 0.25 + energy = 5.0 + price_paid = (1 - phi) * 0.10 + phi * 0.04 + m0 = energy * 0.40 / 3.6e6 + m1 = energy * ((1 - phi) * 0.40 + phi * 0.10) / 3.6e6 + expected = ( + SHIFT["econ.cost.per_token"] + + energy * (price_paid - 0.10) / 3.6e6 + + 50.0 * (m1 - m0) / 1000.0 + ) + assert value("forecast.shift.cost_per_token", **SHIFT) == pytest.approx(expected) + assert value("forecast.shift.cost_saving_per_token", **SHIFT) == pytest.approx( + SHIFT["econ.cost.per_token"] - expected + ) + assert value("forecast.shift.carbon_saving_per_token", **SHIFT) == pytest.approx(m0 - m1) + + +def test_no_shifting_leaves_cost_and_carbon_per_token_unchanged(): + none = {**SHIFT, "forecast.shift.flexible_fraction": 0.0} + assert value("forecast.shift.price_paid", **none) == pytest.approx(0.10) + assert value("forecast.shift.cost_per_token", **none) == pytest.approx(SHIFT["econ.cost.per_token"]) + assert value("forecast.shift.cost_saving_per_token", **none) == pytest.approx(0.0, abs=1e-18) + assert value("forecast.shift.carbon_saving_per_token", **none) == pytest.approx(0.0, abs=1e-18) + + +def test_shifting_everything_pays_the_shifted_hours_price_and_intensity(): + all_in = {**SHIFT, "forecast.shift.flexible_fraction": 1.0} + assert value("forecast.shift.price_paid", **all_in) == pytest.approx(0.04) + assert value("forecast.shift.carbon_intensity_paid", **all_in) == pytest.approx(0.10) + + +def test_savings_grow_with_the_flexible_fraction_when_shifted_hours_are_cheaper_and_cleaner(): + savings = [ + value( + "forecast.shift.cost_saving_per_token", + **{**SHIFT, "forecast.shift.flexible_fraction": phi}, + ) + for phi in (0.0, 0.25, 0.5, 1.0) + ] + carbon = [ + value( + "forecast.shift.carbon_saving_per_token", + **{**SHIFT, "forecast.shift.flexible_fraction": phi}, + ) + for phi in (0.0, 0.25, 0.5, 1.0) + ] + assert savings == sorted(savings) and savings[-1] > savings[0] + assert carbon == sorted(carbon) and carbon[-1] > carbon[0] + + +def test_shifting_to_dearer_hours_gives_negative_cost_saving(): + dearer = {**SHIFT, "forecast.shift.price_shifted": 0.20, "econ.carbon.price_per_tonne": 0.0} + assert value("forecast.shift.cost_saving_per_token", **dearer) < 0 + + +def test_economics_run_power_cost_agrees_with_facility_energy_per_token(): + # Run electricity cost per token in the economics scope is + # price_ws * job power * wall-clock / tokens, and wall-clock = tokens / TPS, + # so it equals the facility energy per token times the blended price. + tokens = 1.0e12 + tps = SHIFT["training.tokens_per_sec"] + wallclock = tokens / tps + run_cost = value( + "econ.run.power_cost", + **{ + "econ.power.price_kwh": SHIFT["econ.power.price_kwh"], + "econ.job.dc_power": SHIFT["econ.job.dc_power"], + "training.wallclock": wallclock, + }, + ) + energy = value("forecast.shift.facility_energy_per_token", **SHIFT) + assert run_cost / tokens == pytest.approx(energy * SHIFT["econ.power.price_kwh"] / 3.6e6) From 8494237c65c0d51baee8b5d61960cd97c14b0d25 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 13:23:24 -0700 Subject: [PATCH 04/52] Refresh registry stats snapshot for the power_forecast scope README stats block and snapshot table and the pinned snapshot in tests/test_import_registry.py rewritten from live registry values (gpu_stack.docs_stats_check live stats), not by hand. Co-Authored-By: Claude Sonnet --- README.md | 44 +++++++++++++++++------------------ tests/test_import_registry.py | 28 +++++++++++----------- 2 files changed, 36 insertions(+), 36 deletions(-) diff --git a/README.md b/README.md index b885716..6f8089f 100644 --- a/README.md +++ b/README.md @@ -78,40 +78,40 @@ I built this with AI coding agents. They write quickly, and they grade their own ```text Registry stats: - systems 16 - variables 1267 + systems 17 + variables 1316 constants 18 - equations 701 - root_inputs 583 - leaves 241 + equations 733 + root_inputs 603 + leaves 251 Coverage: - non_constant_variables 1249 - with_sp_units 1249 - with_references 1249 - equations 701 - equations_with_references 701 - equations_with_unit_check 642 + non_constant_variables 1298 + with_sp_units 1298 + with_references 1298 + equations 733 + equations_with_references 733 + equations_with_unit_check 670 ``` ## Current Snapshot | Signal | Value | |---|---:| -| Systems | 16 | -| Variables | 1267 | +| Systems | 17 | +| Variables | 1316 | | Constants | 18 | -| Equations | 701 | -| Root inputs | 583 | -| Leaves | 241 | +| Equations | 733 | +| Root inputs | 603 | +| Leaves | 251 | | Cycles | 0 | -| Topological order length | 1267 | +| Topological order length | 1316 | | Hard audit failures | 0 | -| Non-constant variables with `sp_units` | 1249 | -| Non-constant variables with references | 1249 | -| Equations with references | 701 | -| Equations with unit checks | 642 | -| Root-debt families | 142 | +| Non-constant variables with `sp_units` | 1298 | +| Non-constant variables with references | 1298 | +| Equations with references | 733 | +| Equations with unit checks | 670 | +| Root-debt families | 146 | | Package version | 0.27.0 | diff --git a/tests/test_import_registry.py b/tests/test_import_registry.py index 5e46e93..d83c414 100644 --- a/tests/test_import_registry.py +++ b/tests/test_import_registry.py @@ -1,7 +1,7 @@ """Pins the registry's published size and proves it can rebuild from empty. -PUBLISHED_SNAPSHOT records the exact counts the project advertises — 1267 -variables, 701 equations, 583 root inputs, and the rest. Any scope change +PUBLISHED_SNAPSHOT records the exact counts the project advertises — 1316 +variables, 733 equations, 603 root inputs, and the rest. Any scope change that adds or removes a variable moves these numbers, and that is the point: the change must be seen and the snapshot updated deliberately. If a change legitimately moves the numbers, update the expectations here — never @@ -16,19 +16,19 @@ PUBLISHED_SNAPSHOT = { - "systems": 16, - "variables": 1267, + "systems": 17, + "variables": 1316, "constants": 18, - "equations": 701, - "root_inputs": 583, - "leaves": 241, - "topological_order_length": 1267, - "with_sp_units": 1249, - "with_references": 1249, - "equations_with_references": 701, - "equations_with_unit_check": 642, - "root_kind": 583, - "derived_kind": 666, + "equations": 733, + "root_inputs": 603, + "leaves": 251, + "topological_order_length": 1316, + "with_sp_units": 1298, + "with_references": 1298, + "equations_with_references": 733, + "equations_with_unit_check": 670, + "root_kind": 603, + "derived_kind": 695, "measured_kind": 0, "definitional_kind": 18, } From 734481337f943614a9e3a0f977ed230526571e0b Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:03:00 -0700 Subject: [PATCH 05/52] Add Heatwave cost-timing module, scenario, runner stages and tests Daily rolling planner for flexible training work against real public price and carbon series (CAISO and DE-LU pairs), policies, accounting in the calculator's terms, bootstrap, and the feedback path into the calculator. Co-Authored-By: Claude Sonnet --- .../e003-heatwave/cost-scenario-v1.json | 45 ++ .../ecb_usd_per_eur_2026-04-27_2026-10-25.csv | 130 ++++ experiments/e003-heatwave/run_cost.py | 444 ++++++++++++ gpu_stack/research/heatwave_cost.py | 655 ++++++++++++++++++ tests/test_research_heatwave_cost.py | 401 +++++++++++ 5 files changed, 1675 insertions(+) create mode 100644 experiments/e003-heatwave/cost-scenario-v1.json create mode 100644 experiments/e003-heatwave/inputs/ecb_usd_per_eur_2026-04-27_2026-10-25.csv create mode 100644 experiments/e003-heatwave/run_cost.py create mode 100644 gpu_stack/research/heatwave_cost.py create mode 100644 tests/test_research_heatwave_cost.py diff --git a/experiments/e003-heatwave/cost-scenario-v1.json b/experiments/e003-heatwave/cost-scenario-v1.json new file mode 100644 index 0000000..6eaadec --- /dev/null +++ b/experiments/e003-heatwave/cost-scenario-v1.json @@ -0,0 +1,45 @@ +{ + "schema": "gpu-stack.e003-heatwave-cost-scenario.v1", + "scenario_id": "e003-heatwave-cost-v1", + "data_kind": "public", + "description": "Cost timing: a cluster running a reference training job against real public hourly price and carbon-intensity series (CAISO NP15 day-ahead price with CAISO in-state intensity, and DE-LU day-ahead price with a DE lifecycle-intensity proxy; each pair is from one region). A share f of the work may be delayed by up to a window W; each day the scheduler plans the flexible work from a 48-hour quantile forecast. The series are public data; the workload (utilization, flexible share, windows) is a scenario assumption and nothing here is measured on a real cluster.", + "reference_run": { + "params": 7e9, + "tokens": 2e12, + "gpu": "H100-SXM", + "n_gpus": 1024, + "overrides": {}, + "note": "The README example of the calculator; every other calculator input is its default." + }, + "test": { + "first_day_utc": "2026-04-27", + "weeks": 26, + "normalization_days": 90, + "note": "26 whole weeks ending 2026-10-25. The 90 days before the first test day fix the z-score constants only. Nothing is tuned on the test period." + }, + "decision": {"hour_utc": 0, "horizon_hours": 48, "run_hours": 24}, + "workload": { + "utilization": 0.7, + "power_cap": 1.0, + "flexible_fractions": [0.1, 0.3, 0.5], + "windows_hours": [24, 72], + "headline": {"flexible_fraction": 0.3, "window_hours": 24}, + "utilization_sensitivity": [0.5, 0.9], + "note": "utilization is the mean offered load as a fraction of the cluster's full-load power (an assumption, not a measurement); power_cap is the most total load allowed in any hour, as a fraction of full-load power." + }, + "lambdas": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], + "planning_quantiles": {"default": 0.5, "risk_sweep": [0.1, 0.5, 0.75, 0.9], "risk_sweep_flexible_fraction": 0.3}, + "pairs": ["caiso", "de"], + "context_hours": {"timestral": 2048, "classical": 672}, + "forecasters": { + "seasonal_naive_24": {"kind": "seasonal_naive", "season_length": 24, "max_context": 672, "kind_label": "classical"}, + "ets_24": {"kind": "ets", "season_length": 24, "max_context": 672, "kind_label": "classical"}, + "arima_seasonal": {"kind": "arima", "order": [2, 0, 1], "season_length": 24, "seasonal_order": [1, 1, 0], "max_context": 672, "kind_label": "classical"}, + "timestral_5M": {"kind": "timestral", "size": "5M", "threads": 1, "kind_label": "timestral zero-shot"}, + "timestral_28M": {"kind": "timestral", "size": "28M", "threads": 1, "kind_label": "timestral zero-shot"}, + "timestral_209M": {"kind": "timestral", "size": "209M", "threads": 1, "kind_label": "timestral zero-shot", "subsample_only": true} + }, + "subsample_weeks": [0, 4, 8, 12, 16, 20, 24], + "reactive": {"lookback_hours": 168}, + "bootstrap": {"n": 2000, "block_days": 7, "seed": 0, "level": 0.95} +} diff --git a/experiments/e003-heatwave/inputs/ecb_usd_per_eur_2026-04-27_2026-10-25.csv b/experiments/e003-heatwave/inputs/ecb_usd_per_eur_2026-04-27_2026-10-25.csv new file mode 100644 index 0000000..0c48546 --- /dev/null +++ b/experiments/e003-heatwave/inputs/ecb_usd_per_eur_2026-04-27_2026-10-25.csv @@ -0,0 +1,130 @@ +date,usd_per_eur +2026-04-27,1.1749 +2026-04-28,1.168 +2026-04-29,1.1706 +2026-04-30,1.1702 +2026-05-04,1.17 +2026-05-05,1.1686 +2026-05-06,1.1762 +2026-05-07,1.177 +2026-05-08,1.1761 +2026-05-11,1.1765 +2026-05-12,1.1738 +2026-05-13,1.1715 +2026-05-14,1.1702 +2026-05-15,1.1628 +2026-05-18,1.1648 +2026-05-19,1.162 +2026-05-20,1.16 +2026-05-21,1.1599 +2026-05-22,1.1595 +2026-05-25,1.1643 +2026-05-26,1.1634 +2026-05-27,1.1637 +2026-05-28,1.1617 +2026-05-29,1.1644 +2026-06-01,1.1646 +2026-06-02,1.1649 +2026-06-03,1.1614 +2026-06-04,1.164 +2026-06-05,1.164 +2026-06-08,1.154 +2026-06-09,1.1573 +2026-06-10,1.1539 +2026-06-11,1.1537 +2026-06-12,1.1567 +2026-06-15,1.1607 +2026-06-16,1.1594 +2026-06-17,1.1591 +2026-06-18,1.1461 +2026-06-19,1.1467 +2026-06-22,1.1456 +2026-06-23,1.1392 +2026-06-24,1.134 +2026-06-25,1.1342 +2026-06-26,1.1401 +2026-06-29,1.1406 +2026-06-30,1.1394 +2026-07-01,1.1383 +2026-07-02,1.1399 +2026-07-03,1.1448 +2026-07-06,1.1415 +2026-07-07,1.1433 +2026-07-08,1.1404 +2026-07-09,1.1435 +2026-07-10,1.143 +2026-07-13,1.1424 +2026-07-14,1.1405 +2026-07-15,1.1406 +2026-07-16,1.1467 +2026-07-17,1.1435 +2026-07-20,1.1426 +2026-07-21,1.1418 +2026-07-22,1.1408 +2026-07-23,1.1392 +2026-07-24,1.1377 +2026-07-27,1.1389 +2026-07-28,1.1367 +2026-07-29,1.138 +2026-07-30,1.1476 +2026-07-31,1.1485 +2026-08-03,1.1535 +2026-08-04,1.1515 +2026-08-05,1.1554 +2026-08-06,1.1542 +2026-08-07,1.1535 +2026-08-10,1.1555 +2026-08-11,1.154 +2026-08-12,1.1545 +2026-08-13,1.1534 +2026-08-14,1.1567 +2026-08-17,1.1593 +2026-08-18,1.1576 +2026-08-19,1.1605 +2026-08-20,1.1681 +2026-08-21,1.1699 +2026-08-24,1.1664 +2026-08-25,1.1662 +2026-08-26,1.1669 +2026-08-27,1.1645 +2026-08-28,1.1643 +2026-08-31,1.1596 +2026-09-01,1.159 +2026-09-02,1.1578 +2026-09-03,1.1615 +2026-09-04,1.1622 +2026-09-07,1.1622 +2026-09-08,1.1614 +2026-09-09,1.1652 +2026-09-10,1.1616 +2026-09-11,1.1592 +2026-09-14,1.1551 +2026-09-15,1.1539 +2026-09-16,1.1537 +2026-09-17,1.1481 +2026-09-18,1.146 +2026-09-21,1.149 +2026-09-22,1.1463 +2026-09-23,1.1411 +2026-09-24,1.1367 +2026-09-25,1.1403 +2026-09-28,1.1378 +2026-09-29,1.1355 +2026-09-30,1.1355 +2026-10-01,1.1298 +2026-10-02,1.1225 +2026-10-05,1.1204 +2026-10-06,1.1269 +2026-10-07,1.1177 +2026-10-08,1.1186 +2026-10-09,1.1206 +2026-10-12,1.1181 +2026-10-13,1.1188 +2026-10-14,1.1195 +2026-10-15,1.1208 +2026-10-16,1.1253 +2026-10-19,1.1225 +2026-10-20,1.1215 +2026-10-21,1.1234 +2026-10-22,1.1205 +2026-10-23,1.1193 diff --git a/experiments/e003-heatwave/run_cost.py b/experiments/e003-heatwave/run_cost.py new file mode 100644 index 0000000..b80cb20 --- /dev/null +++ b/experiments/e003-heatwave/run_cost.py @@ -0,0 +1,444 @@ +"""Cost timing runner (e003): dollars per token against carbon per token, real public series. + +Stages (each run is short; forecasts are cached week by week so a stage can be repeated): + + python experiments/e003-heatwave/run_cost.py forecast --worker 0 --of 2 # fill the forecast cache + python experiments/e003-heatwave/run_cost.py schedule --worker 0 --of 2 # plan and account every configuration (cached) + python experiments/e003-heatwave/run_cost.py evaluate # bootstrap, write results/cost-v1.json + +The price and carbon series are public data (``heatwave.data.public``); the workload is a scenario +assumption (``cost-scenario-v1.json``). Needs numpy, scipy, pandas, statsforecast, torch, +timestral and the ``heatwave`` package (``PYTHONPATH`` may point at its repository). +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import time +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[1] +sys.path.insert(0, str(ROOT)) + +from gpu_stack.research import heatwave_cost as hc # noqa: E402 + +SCENARIO = HERE / "cost-scenario-v1.json" +CACHE = HERE / "cache" +SCHED = HERE / "cache" / "schedule" +OUT = HERE / "results" / "cost-v1.json" +INPUTS = HERE / "inputs" + + +def gpu_stack_git() -> dict: + def git(*args: str) -> str: + return subprocess.run(["git", *args], cwd=ROOT, capture_output=True, text=True, check=True).stdout.strip() + + dirty = bool(git("status", "--porcelain", "--", ".", ":(exclude)experiments/e003-heatwave/results")) + return {"repo": "gpu_stack", "commit": git("rev-parse", "HEAD"), "branch": git("rev-parse", "--abbrev-ref", "HEAD"), "dirty": str(dirty).lower()} + + +def cache_digest(directory: Path, pattern: str) -> dict: + """sha256 over the sorted (file name, file sha256) list of a cache directory, and the file count.""" + import hashlib + from heatwave.provenance import sha256_file + + rows = sorted((p.name, sha256_file(p)) for p in directory.glob(pattern)) + return {"n_files": len(rows), "sha256": hashlib.sha256(repr(rows).encode()).hexdigest()} + + +def series_for(pair: str, scenario: dict) -> dict: + """Hourly series of a pair plus the index positions of the test period and of the normalization window.""" + import pandas as pd + + d = hc.load_hourly_pair(pair) + t = scenario["test"] + start = pd.Timestamp(t["first_day_utc"], tz="UTC") + i0 = int(d["index"].get_loc(start)) + n_hours = t["weeks"] * 7 * hc.HOURS_PER_DAY + if i0 + n_hours > len(d["index"]): + raise ValueError("the test period runs past the end of the series") + d["test_start"] = i0 + d["n_test_hours"] = n_hours + d["norm_slice"] = (i0 - t["normalization_days"] * hc.HOURS_PER_DAY, i0) + return d + + +def forecast_tasks(scenario: dict) -> list: + order = ["seasonal_naive_24", "timestral_5M", "timestral_28M", "ets_24", "arima_seasonal", "timestral_209M"] + tasks = [] + for fk in order: + spec = scenario["forecasters"][fk] + weeks = scenario["subsample_weeks"] if spec.get("subsample_only") else range(scenario["test"]["weeks"]) + for pair in scenario["pairs"]: + for what in ("price", "carbon"): + for w in weeks: + tasks.append((f"{pair}_{what}", fk, int(w))) + return tasks + + +def forecast_stage(scenario: dict, worker: int, of: int) -> None: + CACHE.mkdir(exist_ok=True) + (CACHE / ".gitignore").write_text("*\n") + data: dict = {} + fc_cache: dict = {} + todo = forecast_tasks(scenario)[worker::of] + ctx_cfg = scenario["context_hours"] + horizon = scenario["decision"]["horizon_hours"] + for series_key, fk, week in todo: + path = hc.week_cache_path(CACHE, series_key, fk, week) + if path.exists(): + continue + pair, what = series_key.split("_") + if pair not in data: + data[pair] = series_for(pair, scenario) + spec = scenario["forecasters"][fk] + if fk not in fc_cache: + fc_cache = {fk: hc.make_forecaster(spec)} + ctx = ctx_cfg["timestral"] if spec["kind"] == "timestral" else ctx_cfg["classical"] + t0 = time.time() + vals = hc.compute_week(data[pair][what], data[pair]["test_start"], week, fc_cache[fk], ctx, horizon) + tmp = path.with_suffix(".tmp.npz") + np.savez_compressed(tmp, values=vals, levels=np.array(hc.QUANTILE_LEVELS)) + tmp.rename(path) + print(f"{series_key} {fk} week {week}: {time.time() - t0:.1f}s", flush=True) + + +# --------------------------------------------------------------------------------------------- +# Schedule stage: one cached task = one policy configuration swept over all lambdas +# --------------------------------------------------------------------------------------------- + +LP_POLICIES = ["seasonal_naive_24", "ets_24", "arima_seasonal", "timestral_5M", "timestral_28M", "perfect"] + + +def day_blocks_for(scenario: dict, which: str) -> list: + if which == "full": + return [(0, scenario["test"]["weeks"] * 7)] + return [(7 * w, 7) for w in scenario["subsample_weeks"]] + + +def schedule_task_list(scenario: dict) -> list: + wl = scenario["workload"] + q_default = scenario["planning_quantiles"]["default"] + rho0 = wl["utilization"] + f_risk = scenario["planning_quantiles"]["risk_sweep_flexible_fraction"] + tasks = [] + for pair in scenario["pairs"]: + for which in ("full", "sub"): + pols = LP_POLICIES + (["timestral_209M"] if which == "sub" else []) + for pol in pols: + for f in wl["flexible_fractions"]: + for w in wl["windows_hours"]: + tasks.append(dict(pair=pair, which=which, policy=pol, f=f, W=w, rho=rho0, q=q_default)) + for q in scenario["planning_quantiles"]["risk_sweep"]: + if q == q_default: + continue + for pol in LP_POLICIES[:-1]: + for w in wl["windows_hours"]: + tasks.append(dict(pair=pair, which="full", policy=pol, f=f_risk, W=w, rho=rho0, q=q)) + for rho in wl["utilization_sensitivity"]: + for pol in ("perfect", "timestral_28M", "seasonal_naive_24"): + hf = wl["headline"] + tasks.append(dict(pair=pair, which="full", policy=pol, f=hf["flexible_fraction"], W=hf["window_hours"], rho=rho, q=q_default)) + return tasks + + +def task_key(t: dict) -> str: + return f"{t['pair']}__{t['which']}__{t['policy']}__f{t['f']:g}_W{t['W']}_rho{t['rho']:g}_q{t['q']:g}" + + +class PairContext: + """Series, normalization constants and forecast tables of one pair.""" + + def __init__(self, pair: str, scenario: dict): + self.pair = pair + self.scenario = scenario + self.d = series_for(pair, scenario) + lo, hi = self.d["norm_slice"] + self.norm = hc.zscore_constants(self.d["price"][lo:hi], self.d["carbon"][lo:hi]) + self.i0 = self.d["test_start"] + self._tables: dict = {} + + def realised(self, blocks: list) -> tuple: + idx = hc.block_hour_indices(blocks, self.i0) + return self.d["price"][idx], self.d["carbon"][idx] + + def table(self, what: str, fk: str, weeks: list) -> np.ndarray: + key = (what, fk, tuple(weeks)) + if key not in self._tables: + parts = [] + for w in weeks: + path = hc.week_cache_path(CACHE, f"{self.pair}_{what}", fk, w) + parts.append(np.load(path)["values"].astype(np.float64)) + self._tables[key] = np.concatenate(parts, axis=0) + return self._tables[key] + + def perfect(self, what: str, days: list) -> np.ndarray: + t0 = [self.i0 + d * hc.HOURS_PER_DAY for d in days] + H = self.scenario["decision"]["horizon_hours"] + return hc.padded_future(self.d[what], t0, H) + + +def run_schedule_task(ctx: PairContext, scenario: dict, t: dict) -> dict: + which = t["which"] + real_blocks = day_blocks_for(scenario, which) + weeks = list(range(scenario["test"]["weeks"])) if which == "full" else list(scenario["subsample_weeks"]) + days = [7 * w + k for w in weeks for k in range(7)] + table_blocks = [(0, len(days))] if which == "full" else [(7 * j, 7) for j in range(len(weeks))] + price_real, carbon_real = ctx.realised(real_blocks) + model = hc.FlexModel(t["rho"], t["f"], t["W"], scenario["workload"]["power_cap"]) + lambdas = scenario["lambdas"] + if t["policy"] == "perfect": + pq, cq = ctx.perfect("price", days), ctx.perfect("carbon", days) + else: + j = hc.quantile_index(t["q"]) + pq = ctx.table("price", t["policy"], weeks)[:, :, j] + cq = ctx.table("carbon", t["policy"], weeks)[:, :, j] + sums, flex, fb = hc.sweep_forecast_policy(pq, cq, lambdas, model, ctx.norm, table_blocks, price_real, carbon_real) + return {"sums": sums, "flex": flex, "fallbacks": fb} + + +def schedule_stage(scenario: dict, worker: int, of: int) -> None: + SCHED.mkdir(exist_ok=True) + (SCHED / ".gitignore").write_text("*\n") + ctxs: dict = {} + for t in schedule_task_list(scenario)[worker::of]: + path = SCHED / f"{task_key(t)}.npz" + if path.exists(): + continue + if t["pair"] not in ctxs: + ctxs[t["pair"]] = PairContext(t["pair"], scenario) + t0 = time.time() + try: + res = run_schedule_task(ctxs[t["pair"]], scenario, t) + except FileNotFoundError: + print(f"{task_key(t)}: forecasts not cached yet, skipped", flush=True) + continue + tmp = path.with_suffix(".tmp.npz") + np.savez_compressed(tmp, **res) + tmp.rename(path) + print(f"{task_key(t)}: {time.time() - t0:.1f}s fallbacks={res['fallbacks']}", flush=True) + + +# --------------------------------------------------------------------------------------------- +# Evaluate stage: bootstrap, per-token numbers, result JSON +# --------------------------------------------------------------------------------------------- + +POLICY_ORDER = ["no_shift", "reactive", "seasonal_naive_24", "ets_24", "arima_seasonal", "timestral_5M", "timestral_28M", "timestral_209M", "perfect"] + + +def load_fx() -> tuple: + import csv + path = INPUTS / "ecb_usd_per_eur_2026-04-27_2026-10-25.csv" + rows = list(csv.DictReader(path.open())) + rates = np.array([float(r["usd_per_eur"]) for r in rows]) + return float(rates.mean()), len(rates), path + + +def ratio_safe(num, den, tiny=1e-12): + return np.where(np.abs(den) > tiny, num / np.where(np.abs(den) > tiny, den, 1.0), np.nan) + + +def curve_from_sums(sums, flex, none_sums, pf_sums, idx, terms, fx, level, f, W, rho) -> dict: + """Per-lambda numbers (with bootstrap intervals) for one policy configuration.""" + L = sums.shape[0] + fields: dict = {k: [] for k in [ + "price_paid", "carbon_paid", "electricity_usd_per_mtoken", "total_usd_per_mtoken", "co2_g_per_mtoken", + "saving_pct_electricity", "saving_pct_total", "saving_pct_co2", "pf_share_cost", "pf_share_co2"]} + cis: dict = {k: [] for k in fields} + shifted_p, shifted_c, phi = [], [], [] + for l in range(L): + p, c = hc.effective_prices(sums[l]) + p0, c0 = hc.effective_prices(none_sums[l]) + pp, cp = hc.effective_prices(pf_sums[l]) + bp, bc = hc.effective_prices(sums[l], idx) + bp0, bc0 = hc.effective_prices(none_sums[l], idx) + bpp, bcp = hc.effective_prices(pf_sums[l], idx) + hw = terms["hardware_usd_per_mtoken"] + e = terms["energy_mwh_per_mtoken"] + + def usd(price): + return e * price * fx + + def point_and_boot(name, point, boot): + fields[name].append(float(point)) + lo, hi = hc.percentile_interval(boot, level) + cis[name].append([lo, hi]) + + point_and_boot("price_paid", p, bp) + point_and_boot("carbon_paid", c, bc) + point_and_boot("electricity_usd_per_mtoken", usd(p), usd(bp)) + point_and_boot("total_usd_per_mtoken", usd(p) + hw, usd(bp) + hw) + point_and_boot("co2_g_per_mtoken", e * c * 1000.0, e * bc * 1000.0) + point_and_boot("saving_pct_electricity", 100 * (p0 - p) / p0, 100 * (bp0 - bp) / bp0) + point_and_boot("saving_pct_total", 100 * (usd(p0) - usd(p)) / (usd(p0) + hw), 100 * (usd(bp0) - usd(bp)) / (usd(bp0) + hw)) + point_and_boot("saving_pct_co2", 100 * (c0 - c) / c0, 100 * (bc0 - bc) / bc0) + point_and_boot("pf_share_cost", ratio_safe(p0 - p, p0 - pp), ratio_safe(bp0 - bp, bp0 - bpp)) + point_and_boot("pf_share_co2", ratio_safe(c0 - c, c0 - cp), ratio_safe(bc0 - bc, bc0 - bcp)) + fl = flex[l].sum(axis=0) + shifted_p.append(fl[1] / fl[0] if fl[0] > 0 else float("nan")) + shifted_c.append(fl[2] / fl[0] if fl[0] > 0 else float("nan")) + phi.append(fl[0] / sums[l][:, 0].sum()) + p0, c0 = hc.effective_prices(none_sums[0]) + out = {"flexible_fraction": f, "window_hours": W, "utilization": rho, "lambda": None, + "price_unshifted": float(p0), "carbon_unshifted": float(c0), + "price_shifted": shifted_p, "carbon_shifted": shifted_c, "flexible_energy_share": phi} + out.update(fields) + out.update({k + "_ci": v for k, v in cis.items()}) + return out + + +def forecast_scores(ctx: PairContext, scenario: dict) -> dict: + """Pinball-based scores of every forecaster on the test period (full weeks, and the sub-sampled weeks).""" + from heatwave.forecast import QuantileForecast + from heatwave.forecast import metrics as M + + H = scenario["decision"]["horizon_hours"] + out: dict = {} + for what in ("price", "carbon"): + for fk, spec in scenario["forecasters"].items(): + sets = {"subsample": scenario["subsample_weeks"]} + if not spec.get("subsample_only"): + sets["full"] = list(range(scenario["test"]["weeks"])) + for name, weeks in sets.items(): + days = [7 * w + k for w in weeks for k in range(7)] + t0 = [ctx.i0 + d * hc.HOURS_PER_DAY for d in days] + _, fut = hc.forecast_windows(ctx.d[what], t0, 1, H) + vals = ctx.table(what, fk, weeks) + rec = {} + for tag, sl in (("h1_24", slice(0, 24)), ("h25_48", slice(24, 48))): + fc = QuantileForecast(values=vals[:, None, sl, :]) + y = fut[:, None, sl] + rec[tag] = {"wql": M.wql(fc, y), "mae_median": M.mae_median(fc, y), "coverage_80": M.coverage(fc, y, 0.8)} + out.setdefault(what, {}).setdefault(fk, {})[name] = rec + return out + + +def evaluate_stage(scenario: dict) -> None: + from heatwave.provenance import write_result + + tasks = schedule_task_list(scenario) + missing = [task_key(t) for t in tasks if not (SCHED / f"{task_key(t)}.npz").exists()] + if missing: + raise SystemExit(f"{len(missing)} schedule tasks are not cached yet, e.g. {missing[:3]}; run the schedule stage first") + terms = hc.reference_terms(scenario["reference_run"]) + fx_eur, fx_n, fx_path = load_fx() + wl = scenario["workload"] + bs = scenario["bootstrap"] + lambdas = scenario["lambdas"] + n_full = scenario["test"]["weeks"] * 7 + n_sub = 7 * len(scenario["subsample_weeks"]) + idx = {"full": hc.block_bootstrap_indices(n_full, bs["n"], bs["block_days"], bs["seed"]), + "sub": hc.block_bootstrap_indices(n_sub, bs["n"], bs["block_days"], bs["seed"])} + idx_iid = {"full": hc.block_bootstrap_indices(n_full, bs["n"], 1, bs["seed"] + 1)} + out_pairs: dict = {} + from heatwave.data.schema import PROCESSED + from heatwave.provenance import sha256_file + + data_files = ["public_caiso_np15_lmp_dam", "public_caiso_carbon", "public_de_price_dam_1h", "public_de_generation"] + inputs: list = [SCENARIO, fx_path] + [PROCESSED / f"{n}{ext}" for n in data_files for ext in (".parquet", ".meta.json")] + for pair in scenario["pairs"]: + ctx = PairContext(pair, scenario) + fx = 1.0 if ctx.d["meta"]["currency"] == "USD" else fx_eur + curves = {"full": {}, "sub": {}} + fallbacks = 0 + pair_tasks = [t for t in tasks if t["pair"] == pair] + loaded = {} + for t in pair_tasks: + z = np.load(SCHED / f"{task_key(t)}.npz") + loaded[task_key(t)] = (z["sums"], z["flex"]) + fallbacks += int(z["fallbacks"]) + configs = sorted({(t["which"], t["f"], t["W"], t["rho"]) for t in pair_tasks}) + for which, f, W, rho in configs: + blocks = day_blocks_for(scenario, which) + price_real, carbon_real = ctx.realised(blocks) + model = hc.FlexModel(rho, f, W, wl["power_cap"]) + none_s = hc.sweep_none(model, len(lambdas), price_real, carbon_real) + react = hc.sweep_reactive(ctx.d["price"], ctx.d["carbon"], lambdas, model, ctx.norm, blocks, ctx.i0, price_real, carbon_real, + scenario["reactive"]["lookback_hours"]) + pf_key = task_key(dict(pair=pair, which=which, policy="perfect", f=f, W=W, rho=rho, q=scenario["planning_quantiles"]["default"])) + pf_s = loaded[pf_key][0] + ix = idx[which] + block = curves[which] + block[hc.curve_key("no_shift", f, W, rho, None)] = curve_from_sums(none_s[0], none_s[1], none_s[0], pf_s, ix, terms, fx, bs["level"], f, W, rho) + block[hc.curve_key("reactive", f, W, rho, None)] = curve_from_sums(react[0], react[1], none_s[0], pf_s, ix, terms, fx, bs["level"], f, W, rho) + for t in pair_tasks: + if (t["which"], t["f"], t["W"], t["rho"]) != (which, f, W, rho): + continue + sums, flex = loaded[task_key(t)] + block[hc.curve_key(t["policy"], f, W, rho, t["q"])] = curve_from_sums(sums, flex, none_s[0], pf_s, ix, terms, fx, bs["level"], f, W, rho) + for which in curves: + for c in curves[which].values(): + c["lambda"] = list(lambdas) + out_pairs[pair] = { + "series": ctx.d["meta"], "same_region": True, "usd_per_price_unit": fx, + "normalization_constants": ctx.norm, + "plan_fallbacks_total": fallbacks, + "forecast_scores": forecast_scores(ctx, scenario), + "curves": curves["full"], "curves_subsample": curves["sub"], + "mean_price_test": float(ctx.realised(day_blocks_for(scenario, "full"))[0].mean()), + "mean_carbon_test": float(ctx.realised(day_blocks_for(scenario, "full"))[1].mean()), + } + # round trip through the calculator for the headline curves + hf = wl["headline"] + roundtrip = {} + doc = {"scenario": scenario, "pairs": out_pairs} + for pair in scenario["pairs"]: + curve = hc.lookup_curve(doc, pair, "timestral_28M", hf["flexible_fraction"], hf["window_hours"]) + fxp = out_pairs[pair]["usd_per_price_unit"] + recs = [] + for lam in (0.0, 0.5, 1.0): + est, carbon_kg, ti = hc.estimate_with_timing(scenario["reference_run"], curve, lam, fxp) + i = scenario["lambdas"].index(lam) + recs.append({ + "lambda": lam, "calculator_electricity_cost_usd": est.electricity_cost, + "calculator_electricity_usd_per_mtoken": est.electricity_cost / (est.tokens / 1e6), + "curve_electricity_usd_per_mtoken": curve["electricity_usd_per_mtoken"][i], + "calculator_total_usd_per_mtoken": est.cost_per_million_tokens, + "curve_total_usd_per_mtoken": curve["total_usd_per_mtoken"][i], + "carbon_kg_reference_run": carbon_kg, + "curve_co2_g_per_mtoken_times_run_mtokens_kg": curve["co2_g_per_mtoken"][i] * est.tokens / 1e6 / 1000.0, + "timing_inputs": ti, + }) + roundtrip[pair] = {"policy": "timestral_28M", "flexible_fraction": hf["flexible_fraction"], "window_hours": hf["window_hours"], "points": recs} + result = { + "scenario": scenario, "reference_run_terms": terms, + "fx": {"usd_per_eur_mean_over_ecb_business_days": fx_eur, "n_days": fx_n, + "source": "ECB euro foreign exchange reference rates, USD per EUR, 2026-04-27 to 2026-10-25 (https://data-api.ecb.europa.eu)"}, + "pairs": out_pairs, "calculator_roundtrip": roundtrip, + "gpu_stack_git": gpu_stack_git(), + "cache_digests": {"forecast": cache_digest(CACHE, "*.npz"), "schedule": cache_digest(SCHED, "*.npz")}, + "notes": [ + "Electricity price is the wholesale day-ahead price (CAISO NP15 LMP, DE-LU day-ahead); no retail adders, network fees or taxes.", + "Hardware cost per token is the calculator's and does not depend on timing; total = electricity + hardware. DE electricity is converted at the mean ECB rate.", + "CAISO carbon is the in-state intensity computed from CAISO's published CO2 and fuel mix (imports excluded). DE carbon is a production-based lifecycle proxy from IPCC AR5 medians, not an official figure.", + "Workload (utilization, flexible share, windows, no idle power, constant energy per token) is a scenario assumption; nothing is measured on a real cluster.", + "Forecasts are made at 00:00 UTC from history only; no day-ahead publications are used as inputs.", + ], + } + write_result(OUT, result, script=__file__, data_kind="public", inputs=inputs, + notes="Public price and carbon series; modeled workload. The git field is the heatwave repository; the gpu_stack repository state is in result.gpu_stack_git.") + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("stage", choices=["forecast", "schedule", "evaluate"]) + ap.add_argument("--worker", type=int, default=0) + ap.add_argument("--of", type=int, default=1) + a = ap.parse_args() + scenario = hc.load_scenario(SCENARIO) + if a.stage == "forecast": + forecast_stage(scenario, a.worker, a.of) + elif a.stage == "schedule": + schedule_stage(scenario, a.worker, a.of) + else: + evaluate_stage(scenario) + + +if __name__ == "__main__": + main() diff --git a/gpu_stack/research/heatwave_cost.py b/gpu_stack/research/heatwave_cost.py new file mode 100644 index 0000000..5e1b0c6 --- /dev/null +++ b/gpu_stack/research/heatwave_cost.py @@ -0,0 +1,655 @@ +"""Cost timing for the Heatwave extension (e003): dollars per token against carbon per token. + +A share ``f`` of a training cluster's work is flexible. It can be delayed by up to a window +``W`` hours; the rest runs when it arrives. Every day the scheduler reads a 48-hour, 21-quantile +forecast of the electricity price and of the grid carbon intensity, turns the two into one cost per +hour, ``lam * price + (1 - lam) * carbon`` (both z-scored with constants fixed before the test +period), and places the flexible work into the cheapest hours that the power cap and the deadlines +allow. Sweeping ``lam`` from 0 to 1 traces the Pareto curve of electricity cost per token against +carbon per token. + +Everything here is a model of a cluster running against REAL PUBLIC price and carbon series (the +series come from ``heatwave.data.public``; ``data_kind`` is "public" for the series and the +workload is a scenario assumption). Nothing is measured on a real cluster. + +Model (all scenario parameters are in ``experiments/e003-heatwave/cost-scenario-v1.json``) +----------------------------------------------------------------------------------------- +* Load is in units of the cluster's full-load power. The mean offered load is ``rho`` (a scenario + assumption), so work arrives at ``rho`` full-load hours per hour. A share ``f`` of it is + flexible. The inflexible part, ``rho * (1 - f)``, runs in its arrival hour. +* Total load in any hour is at most ``power_cap`` (a fraction of the full-load power). Shifted + work therefore uses capacity that would otherwise sit idle, and every non-energy cost per token + is unchanged by timing. +* Power is proportional to load and tokens per hour are proportional to load (no throttling, no + idle power). Energy per token is a constant taken from ``gpu_stack.calculator.estimate`` for a + declared reference run, so dollars per token and grams of CO2 per token come out in the + calculator's own terms: electricity cost per token = calculator energy per token * the + energy-weighted price paid, carbon per token = the same energy * the energy-weighted carbon + intensity paid. +* Hardware amortization per token is the calculator's, and it does not depend on timing. Total + cost per token = electricity (this module) + hardware (calculator). +* Work arriving in hour ``a`` must finish by the end of hour ``a + W - 1``. With one window length + for all work, first-in first-out is earliest-deadline first, and a schedule ``y`` is feasible + exactly when ``due(t) <= cumulative_run(t) <= arrived(t)`` in every hour ``t`` (``cumulative_run`` + includes work already run). The daily planning problem is therefore a small linear program. +* Work that the plan leaves beyond the 48-hour horizon is valued at the mean planned cost over the + horizon (a terminal value), so it is carried to the next day's plan; deadlines inside the horizon + are hard constraints. + +Policies are one scheduler with different cost forecasts: perfect foresight (realised values), +classical forecasts, Timestral zero-shot, plus two policies with no planning problem: no shifting +and a reactive rule (run flexible work when the last observed cost is below its trailing median). + +Currency: CAISO prices are USD/MWh, DE-LU prices EUR/MWh. Electricity cost per token for DE is in +EUR; a reported exchange rate (``usd_per_eur``) converts it when it is added to the calculator's +USD hardware cost. +""" + +from __future__ import annotations + +import json +import math +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple + +import numpy as np + +HOURS_PER_DAY = 24 +QUANTILE_LEVELS: Tuple[float, ...] = ( + 0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, + 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.99, +) # same grid as heatwave.forecast.QUANTILE_LEVELS; checked in the tests +DEFAULT_LAMBDAS: Tuple[float, ...] = tuple(round(0.1 * i, 1) for i in range(11)) +_TOL = 1e-9 + + +def load_scenario(path: "str | Path") -> Dict[str, Any]: + return json.loads(Path(path).read_text()) + + +# --------------------------------------------------------------------------- +# Reference run, in the calculator's terms +# --------------------------------------------------------------------------- + + +def reference_terms(ref: Mapping[str, Any]) -> Dict[str, float]: + """Energy, tokens and hardware cost of the declared reference run, from ``calculator.estimate``. + + ``ref`` has ``params``, ``tokens``, ``gpu``, ``n_gpus`` and an optional ``overrides`` dict of + calculator inputs. Returns the quantities this module needs; each comes from the calculator, + none is typed in here. + """ + from gpu_stack.calculator import estimate + + est = estimate(ref["params"], ref["tokens"], gpu=ref["gpu"], n_gpus=ref["n_gpus"], **dict(ref.get("overrides", {}))) + run_hours = est.training_days * 24.0 + mtok = est.tokens / 1e6 + return { + "params": est.params, + "tokens": est.tokens, + "gpu": est.gpu, + "n_gpus": est.n_gpus, + "training_days": est.training_days, + "gpu_hours": est.gpu_hours, + "energy_mwh": est.energy_mwh, + "facility_power_mw": est.energy_mwh / run_hours, + "facility_power_w_per_gpu": est.energy_mwh * 1e6 / est.gpu_hours, + "tokens_per_hour": est.tokens / run_hours, + "tokens_per_gpu_hour": est.tokens / est.gpu_hours, + "energy_mwh_per_mtoken": est.energy_mwh / mtok, + "hardware_cost_usd": est.hardware_cost, + "hardware_usd_per_mtoken": est.hardware_cost / mtok, + "calculator_default_electricity_cost_usd": est.electricity_cost, + "calculator_default_electricity_price_usd_per_kwh": est.inputs["electricity_price"], + "calculator_default_cost_per_mtoken_usd": est.cost_per_million_tokens, + } + + +# --------------------------------------------------------------------------- +# Workload model and the daily planning problem +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class FlexModel: + """Offered load ``rho``, flexible share ``f``, window ``window_hours`` and total-load cap ``power_cap``.""" + + utilization: float + flexible_fraction: float + window_hours: int + power_cap: float = 1.0 + + def __post_init__(self) -> None: + if not 0.0 < self.utilization <= self.power_cap: + raise ValueError("need 0 < utilization <= power_cap") + if not 0.0 <= self.flexible_fraction <= 1.0: + raise ValueError("flexible_fraction must be in [0, 1]") + if self.window_hours < 1: + raise ValueError("window_hours must be at least 1") + + @property + def inflexible_load(self) -> float: + return self.utilization * (1.0 - self.flexible_fraction) + + @property + def flexible_rate(self) -> float: + """Flexible work arriving per hour, in full-load hours.""" + return self.utilization * self.flexible_fraction + + @property + def flexible_cap(self) -> float: + """Most flexible work that can run in one hour (total-load cap minus the inflexible load).""" + return self.power_cap - self.inflexible_load + + def arrived(self, t: np.ndarray) -> np.ndarray: + """Flexible work that has arrived by the end of hour ``t`` (hour 0 is the start of the run).""" + return self.flexible_rate * (np.asarray(t, dtype=np.float64) + 1.0) + + def due(self, t: np.ndarray) -> np.ndarray: + """Flexible work whose deadline is the end of hour ``t`` or earlier.""" + return self.flexible_rate * np.maximum(0.0, np.asarray(t, dtype=np.float64) - self.window_hours + 2.0) + + +_TRI: Dict[int, np.ndarray] = {} + + +def _tri(h: int) -> np.ndarray: + m = _TRI.get(h) + if m is None: + m = np.tril(np.ones((h, h))) + _TRI[h] = m + return m + + +def plan_flexible(cost: np.ndarray, model: FlexModel, t0: int, run_before: float, terminal_cost: Optional[float] = None) -> Tuple[np.ndarray, bool]: + """Plan the flexible work of the next ``len(cost)`` hours. + + ``cost[h]`` is the planned cost of one full-load hour of flexible work in hour ``t0 + h``; + ``run_before`` is the flexible work already run before hour ``t0``. Minimises + ``sum_h (cost[h] - terminal_cost) * y[h]`` subject to ``0 <= y[h] <= flexible_cap`` and + ``due(t) <= run_before + cumsum(y)(t) <= arrived(t)`` (``terminal_cost`` defaults to the mean + planned cost). Returns ``(y, fell_back)``; ``fell_back`` is True if the linear program was not + solved, in which case the plan runs the work as soon as it arrives. + """ + from scipy.optimize import linprog + + cost = np.asarray(cost, dtype=np.float64) + h = cost.size + t = np.arange(t0, t0 + h) + hi = model.arrived(t) - run_before + lo = model.due(t) - run_before + ref = float(np.mean(cost)) if terminal_cost is None else float(terminal_cost) + tri = _tri(h) + res = linprog( + cost - ref, + A_ub=np.vstack([tri, -tri]), + b_ub=np.concatenate([hi + _TOL, -(lo - _TOL)]), + bounds=(0.0, model.flexible_cap), + method="highs", + ) + if res.status != 0: + return _asap_plan(model, t0, h, run_before), True + return np.clip(res.x, 0.0, model.flexible_cap), False + + +def _asap_plan(model: FlexModel, t0: int, h: int, run_before: float) -> np.ndarray: + y = np.zeros(h) + done = run_before + for k in range(h): + pending = model.arrived(t0 + k) - done + y[k] = min(model.flexible_cap, max(0.0, pending)) + done += y[k] + return y + + +def schedule_forecast( + cost_by_day: np.ndarray, + model: FlexModel, + day_blocks: Sequence[Tuple[int, int]], +) -> Tuple[np.ndarray, Dict[str, float]]: + """Run the daily rolling plan over blocks of days. + + ``cost_by_day`` is (n_days_total, H): row ``d`` is the planned cost of the H hours that start + at the daily decision time of day ``d``. ``day_blocks`` is a list of ``(first_day, n_days)``; + the backlog is empty at the start of every block. Only the first 24 hours of each plan are + run. Returns the flexible work run in every hour of the blocks (concatenated in block order) + and diagnostics. + """ + out: List[np.ndarray] = [] + fallbacks = 0 + for first, n in day_blocks: + run_before = 0.0 + t_rel = 0 + for k in range(n): + y, fb = plan_flexible(cost_by_day[first + k], model, t_rel, run_before) + fallbacks += int(fb) + y24 = y[:HOURS_PER_DAY] + out.append(y24) + run_before += float(y24.sum()) + t_rel += HOURS_PER_DAY + y_all = np.concatenate(out) if out else np.zeros(0) + return y_all, {"plan_fallbacks": float(fallbacks)} + + +def schedule_none(model: FlexModel, n_hours: int) -> np.ndarray: + """No shifting: flexible work runs in its arrival hour.""" + return np.full(n_hours, model.flexible_rate) + + +def trailing_median(x: np.ndarray, window: int) -> np.ndarray: + """``out[i]`` is the median of ``x[i - window + 1 : i + 1]`` (NaN for ``i < window - 1``).""" + x = np.asarray(x, dtype=np.float64) + out = np.full(x.size, np.nan) + if x.size >= window: + out[window - 1:] = np.median(np.lib.stride_tricks.sliding_window_view(x, window), axis=1) + return out + + +def schedule_reactive( + cost_hist: np.ndarray, + model: FlexModel, + day_blocks: Sequence[Tuple[int, int]], + lookback: int = 168, +) -> np.ndarray: + """Reactive rule without a forecast. + + ``cost_hist`` is the observed cost of every hour of the full series (history plus test) and + ``day_blocks`` holds ``(first_hour_index, n_days)`` pairs in that array's hour numbering; the + backlog starts empty at each block. In hour ``i`` the rule compares the last observed cost + (hour ``i - 1``) with the median of the ``lookback`` observed hours that end with it. If the + last cost is at or below that median it runs all pending flexible work up to the cap, otherwise + only the work that is due this hour. + """ + med = trailing_median(cost_hist, lookback) + out: List[np.ndarray] = [] + cap = model.flexible_cap + rate = model.flexible_rate + win = model.window_hours + for first_hour, n_days in day_blocks: + n = n_days * HOURS_PER_DAY + y = np.zeros(n) + run = 0.0 + for k in range(n): + i = first_hour + k + arrived = rate * (k + 1.0) + pending = arrived - run + if cost_hist[i - 1] <= med[i - 1]: + want = pending + else: + want = rate * max(0.0, k - win + 2.0) - run + v = min(cap, max(0.0, want)) + y[k] = v + run += v + out.append(y) + return np.concatenate(out) if out else np.zeros(0) + + +# --------------------------------------------------------------------------- +# Cost, accounting and uncertainty +# --------------------------------------------------------------------------- + + +def zscore_constants(price: np.ndarray, carbon: np.ndarray) -> Dict[str, float]: + """Mean and standard deviation of the training-window price and carbon (fixed before the test period).""" + return { + "price_mean": float(np.mean(price)), "price_std": float(np.std(price)), + "carbon_mean": float(np.mean(carbon)), "carbon_std": float(np.std(carbon)), + } + + +def mixed_cost(price: np.ndarray, carbon: np.ndarray, lam: float, norm: Mapping[str, float]) -> np.ndarray: + """``lam * z(price) + (1 - lam) * z(carbon)`` with the fixed training-window constants.""" + return lam * (np.asarray(price) - norm["price_mean"]) / norm["price_std"] + (1.0 - lam) * (np.asarray(carbon) - norm["carbon_mean"]) / norm["carbon_std"] + + +def daily_sums(y: np.ndarray, model: FlexModel, price: np.ndarray, carbon: np.ndarray) -> np.ndarray: + """Per-day sums of load, load * price and load * carbon. Returns an array of shape (n_days, 3). + + ``y`` is the flexible work run in each hour (blocks concatenated), ``price`` and ``carbon`` are + the realised series over the same hours. Load in an hour is the inflexible load plus ``y``. + """ + load = model.inflexible_load + np.asarray(y, dtype=np.float64) + n_days = load.size // HOURS_PER_DAY + l2 = load.reshape(n_days, HOURS_PER_DAY) + p2 = np.asarray(price, dtype=np.float64).reshape(n_days, HOURS_PER_DAY) + c2 = np.asarray(carbon, dtype=np.float64).reshape(n_days, HOURS_PER_DAY) + return np.stack([l2.sum(axis=1), (l2 * p2).sum(axis=1), (l2 * c2).sum(axis=1)], axis=1) + + +def flexible_daily_sums(y: np.ndarray, price: np.ndarray, carbon: np.ndarray) -> np.ndarray: + """Per-day sums of flexible work, flexible * price and flexible * carbon, shape (n_days, 3).""" + y = np.asarray(y, dtype=np.float64) + n_days = y.size // HOURS_PER_DAY + y2 = y.reshape(n_days, HOURS_PER_DAY) + p2 = np.asarray(price, dtype=np.float64).reshape(n_days, HOURS_PER_DAY) + c2 = np.asarray(carbon, dtype=np.float64).reshape(n_days, HOURS_PER_DAY) + return np.stack([y2.sum(axis=1), (y2 * p2).sum(axis=1), (y2 * c2).sum(axis=1)], axis=1) + + +def effective_prices(sums: np.ndarray, idx: Optional[np.ndarray] = None) -> Tuple[Any, Any]: + """Energy-weighted price and carbon intensity from daily sums (optionally under bootstrap indices). + + ``sums`` is (n_days, 3). ``idx`` is None, an index vector, or a (n_boot, n) index matrix; the + return values then have shape (), or (n_boot,). + """ + if idx is None: + s = sums.sum(axis=0) + return s[1] / s[0], s[2] / s[0] + g = sums[idx] + s = g.sum(axis=-2) + return s[..., 1] / s[..., 0], s[..., 2] / s[..., 0] + + +def block_bootstrap_indices(n_days: int, n_boot: int, block_days: int, seed: int) -> np.ndarray: + """Circular moving-block bootstrap indices, shape (n_boot, n_days). ``block_days=1`` is the iid day bootstrap.""" + rng = np.random.default_rng(seed) + n_blocks = int(math.ceil(n_days / block_days)) + starts = rng.integers(0, n_days, size=(n_boot, n_blocks)) + idx = (starts[:, :, None] + np.arange(block_days)[None, None, :]) % n_days + return idx.reshape(n_boot, -1)[:, :n_days] + + +def percentile_interval(x: np.ndarray, level: float = 0.95) -> Tuple[float, float]: + a = (1.0 - level) / 2.0 + lo, hi = np.nanquantile(x, [a, 1.0 - a]) + return float(lo), float(hi) + + +# --------------------------------------------------------------------------- +# Per-token numbers in the calculator's terms +# --------------------------------------------------------------------------- + + +def per_mtoken(price_paid: Any, carbon_paid: Any, terms: Mapping[str, float], usd_per_price_unit: float = 1.0) -> Dict[str, Any]: + """Electricity cost, total cost and carbon per million tokens. + + ``price_paid`` in price units per MWh, ``carbon_paid`` in g/kWh. ``usd_per_price_unit`` is 1.0 + for USD prices and the exchange rate for EUR prices. Carbon: energy per Mtoken in MWh times + 1000 kWh/MWh times g/kWh gives grams per Mtoken. + """ + e = terms["energy_mwh_per_mtoken"] + elec = e * price_paid * usd_per_price_unit + return { + "electricity_per_mtoken": e * price_paid, + "electricity_usd_per_mtoken": elec, + "total_usd_per_mtoken": elec + terms["hardware_usd_per_mtoken"], + "co2_g_per_mtoken": e * carbon_paid * 1000.0, + } + + +# --------------------------------------------------------------------------- +# Public series, rolling forecasts and their cache (needs the heatwave package) +# --------------------------------------------------------------------------- + +PAIR_SOURCES: Dict[str, Dict[str, Any]] = { + # price: (region, market, column, currency); carbon: (region, kwargs) + "caiso": {"price": ("caiso", "dam", "lmp_usd_per_mwh", "USD"), "carbon": ("caiso", {"basis": "in_state"})}, + "de": {"price": ("de", "dam", "price_eur_per_mwh", "EUR"), "carbon": ("de", {})}, +} + + +def fill_hourly(x: "Any", period: int = 24, max_weeks: int = 3) -> Tuple[np.ndarray, int]: + """Fill NaN hours with the mean of the same hour of day on neighbouring days, else by interpolation. + + Returns ``(filled array, number of hours filled)``. Used for the few hours that a source did not publish. + """ + v = np.asarray(x, dtype=np.float64).copy() + miss = np.flatnonzero(np.isnan(v)) + if miss.size == 0: + return v, 0 + src = v.copy() + for i in miss: + vals = [] + for k in range(1, max_weeks * 7 + 1): + for j in (i - k * period, i + k * period): + if 0 <= j < v.size and not np.isnan(src[j]): + vals.append(src[j]) + if len(vals) >= 4: + break + if vals: + v[i] = float(np.mean(vals)) + still = np.isnan(v) + if still.any(): + idx = np.arange(v.size) + v[still] = np.interp(idx[still], idx[~still], v[~still]) + return v, int(miss.size) + + +def load_hourly_pair(pair: str) -> Dict[str, Any]: + """Hourly UTC price and carbon intensity of one same-region pair, from ``heatwave.data.public``. + + Returns ``{"index": DatetimeIndex, "price": ndarray, "carbon": ndarray, "meta": {...}}`` on the + common hourly index. Hours a source did not publish are filled with :func:`fill_hourly` and + counted in ``meta``. + """ + import pandas as pd + from heatwave.data import public as P + + src = PAIR_SOURCES[pair] + region, market, column, currency = src["price"] + price = P.load_price(region, market)[column].resample("1h").mean() + cregion, ckw = src["carbon"] + carbon = P.load_carbon(cregion, **ckw)["intensity_g_per_kwh"].resample("1h").mean() + start = max(price.index.min(), carbon.index.min()) + end = min(price.index.max(), carbon.index.max()) + idx = pd.date_range(start, end, freq="1h", tz="UTC") + p, n_p = fill_hourly(price.reindex(idx).to_numpy()) + c, n_c = fill_hourly(carbon.reindex(idx).to_numpy()) + return { + "index": idx, "price": p, "carbon": c, + "meta": { + "pair": pair, "price_source": f"{region}/{market}/{column}", "price_unit": f"{currency}/MWh", "currency": currency, + "carbon_source": f"{cregion} {ckw}", "carbon_unit": "gCO2/kWh", + "hours_total": int(len(idx)), "price_hours_filled": n_p, "carbon_hours_filled": n_c, + "first_hour_utc": str(idx[0]), "last_hour_utc": str(idx[-1]), + }, + } + + +def make_forecaster(spec: Mapping[str, Any]) -> Any: + """Build a ``heatwave.forecast`` forecaster from a scenario spec ``{"kind": ..., ...}``.""" + kind = spec["kind"] + if kind == "seasonal_naive": + from heatwave.forecast import SeasonalNaiveForecaster + return SeasonalNaiveForecaster(spec["season_length"], max_context=spec.get("max_context")) + if kind == "ets": + from heatwave.forecast import ETSForecaster + return ETSForecaster(spec["season_length"], max_context=spec.get("max_context", 2048)) + if kind == "arima": + from heatwave.forecast import ARIMAForecaster + return ARIMAForecaster(tuple(spec["order"]), season_length=spec.get("season_length", 1), + seasonal_order=tuple(spec.get("seasonal_order", (0, 0, 0))), max_context=spec.get("max_context", 1024)) + if kind == "timestral": + from heatwave.forecast.timestral_fc import TimestralForecaster + return TimestralForecaster(size=spec["size"], threads=spec.get("threads", 1)) + raise ValueError(f"unknown forecaster kind {kind!r}") + + +def forecast_windows(values: np.ndarray, t0: Sequence[int], context_len: int, horizon: int) -> Tuple[np.ndarray, np.ndarray]: + """Context windows (n, 1, context_len) before each ``t0`` and the realised future (n, horizon). + + Future hours past the end of the series are NaN. + """ + ctx = np.stack([values[t - context_len:t] for t in t0])[:, None, :] + fut = np.full((len(t0), horizon), np.nan) + for k, t in enumerate(t0): + seg = values[t:t + horizon] + fut[k, :seg.size] = seg + return ctx, fut + + +def rolling_forecast(values: np.ndarray, t0: Sequence[int], forecaster: Any, context_len: int, horizon: int, batch: int = 4) -> np.ndarray: + """Forecast ``horizon`` hours at every ``t0`` from the ``context_len`` hours before it. Returns (n, horizon, 21). + + Only values before ``t0`` enter a forecast. + """ + out = [] + for k in range(0, len(t0), batch): + ctx, _ = forecast_windows(values, t0[k:k + batch], context_len, horizon) + q = forecaster.forecast(ctx, horizon) + out.append(q.values[:, 0]) + return np.concatenate(out, axis=0) + + +def week_cache_path(cache_dir: "str | Path", series_key: str, forecaster_key: str, week: int) -> Path: + return Path(cache_dir) / f"{series_key}__{forecaster_key}__w{week:02d}.npz" + + +def compute_week(values: np.ndarray, test_start: int, week: int, forecaster: Any, context_len: int, horizon: int, batch: int = 4) -> np.ndarray: + """Forecasts for the 7 daily decision times (00:00 UTC) of one test week. Returns (7, horizon, 21), float32.""" + t0 = [test_start + (7 * week + d) * HOURS_PER_DAY for d in range(7)] + return rolling_forecast(values, t0, forecaster, context_len, horizon, batch).astype(np.float32) + + +def quantile_index(q: float) -> int: + for j, lv in enumerate(QUANTILE_LEVELS): + if abs(lv - q) < 1e-9: + return j + raise ValueError(f"planning quantile {q} is not on the 21-level grid") + + +def padded_future(values: np.ndarray, t0: Sequence[int], horizon: int) -> np.ndarray: + """Realised values for ``horizon`` hours after each ``t0``. + + Hours past the end of the series copy the same hour one day earlier (only the last decision + days need this, and only for the perfect-foresight look-ahead beyond 24 hours). + """ + out = np.empty((len(t0), horizon)) + n = len(values) + for k, t in enumerate(t0): + for h in range(horizon): + j = t + h + while j >= n: + j -= HOURS_PER_DAY + out[k, h] = values[j] + return out + + +def block_hour_indices(blocks_days: Sequence[Tuple[int, int]], test_start: int) -> np.ndarray: + """Hour indices (into the full series) of the test hours that belong to the blocks, in order.""" + return np.concatenate([test_start + np.arange(first * HOURS_PER_DAY, (first + n) * HOURS_PER_DAY) for first, n in blocks_days]) + + +def sweep_forecast_policy( + price_q: np.ndarray, + carbon_q: np.ndarray, + lambdas: Sequence[float], + model: FlexModel, + norm: Mapping[str, float], + blocks_days: Sequence[Tuple[int, int]], + price_real: np.ndarray, + carbon_real: np.ndarray, +) -> Tuple[np.ndarray, np.ndarray, int]: + """Run one forecast policy for every ``lam``. + + ``price_q`` and ``carbon_q`` are (n_days_total, H) planned values. ``price_real`` and + ``carbon_real`` are the realised series over the blocks' hours. Returns daily sums of load, + load*price and load*carbon, shape (n_lam, n_block_days, 3), the flexible-work daily sums with + the same layout, and the number of plans that fell back to run-on-arrival. + """ + sums, flex, fb = [], [], 0 + for lam in lambdas: + y, diag = schedule_forecast(mixed_cost(price_q, carbon_q, lam, norm), model, blocks_days) + sums.append(daily_sums(y, model, price_real, carbon_real)) + flex.append(flexible_daily_sums(y, price_real, carbon_real)) + fb += int(diag["plan_fallbacks"]) + return np.stack(sums), np.stack(flex), fb + + +def sweep_reactive( + price_full: np.ndarray, + carbon_full: np.ndarray, + lambdas: Sequence[float], + model: FlexModel, + norm: Mapping[str, float], + blocks_days: Sequence[Tuple[int, int]], + test_start: int, + price_real: np.ndarray, + carbon_real: np.ndarray, + lookback: int = 168, +) -> Tuple[np.ndarray, np.ndarray]: + sums, flex = [], [] + blocks_hours = [(test_start + first * HOURS_PER_DAY, n) for first, n in blocks_days] + for lam in lambdas: + cost = mixed_cost(price_full, carbon_full, lam, norm) + y = schedule_reactive(cost, model, blocks_hours, lookback) + sums.append(daily_sums(y, model, price_real, carbon_real)) + flex.append(flexible_daily_sums(y, price_real, carbon_real)) + return np.stack(sums), np.stack(flex) + + +def sweep_none(model: FlexModel, n_lambdas: int, price_real: np.ndarray, carbon_real: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + y = schedule_none(model, price_real.size) + s = daily_sums(y, model, price_real, carbon_real) + f = flexible_daily_sums(y, price_real, carbon_real) + return np.repeat(s[None], n_lambdas, axis=0), np.repeat(f[None], n_lambdas, axis=0) + + +# --------------------------------------------------------------------------- +# Feeding the result back to the calculator +# --------------------------------------------------------------------------- + + +def curve_key(policy: str, flexible_fraction: float, window_hours: int, utilization: float, plan_quantile: Optional[float]) -> str: + q = "na" if plan_quantile is None else f"{plan_quantile:g}" + return f"{policy}|f={flexible_fraction:g}|W={window_hours}|rho={utilization:g}|q={q}" + + +def lookup_curve(result_doc: Mapping[str, Any], pair: str, policy: str, flexible_fraction: float, window_hours: int, + utilization: Optional[float] = None, plan_quantile: Optional[float] = 0.5, subsample: bool = False) -> Mapping[str, Any]: + """Pick one Pareto curve out of the result JSON written by ``run_cost.py`` (the whole document or its ``result`` part).""" + res = result_doc.get("result", result_doc) + if utilization is None: + utilization = res["scenario"]["workload"]["utilization"] + q = None if policy in ("no_shift", "reactive") else plan_quantile + block = res["pairs"][pair]["curves_subsample" if subsample else "curves"] + return block[curve_key(policy, flexible_fraction, window_hours, utilization, q)] + + +def interpolate_in_lambda(curve: Mapping[str, Any], field: str, lam: float) -> float: + """Linear interpolation of a per-lambda field of a curve at ``lam`` in [0, 1].""" + return float(np.interp(lam, curve["lambda"], curve[field])) + + +def timing_inputs(curve: Mapping[str, Any], lam: float, usd_per_price_unit: float = 1.0) -> Dict[str, float]: + """Effective electricity price and carbon intensity for one flexible fraction, window, policy and ``lam``. + + The names match the calculator and the ``power_forecast`` shift scope: + + * ``electricity_price``: USD per kWh, the energy-weighted price paid after shifting. Pass it to + ``gpu_stack.calculator.estimate(..., electricity_price=...)``. + * ``price_shifted`` (USD/kWh) and ``carbon_intensity_shifted`` (kg/kWh): averages over the hours + the flexible work ran in (scope inputs ``forecast.shift.price_shifted`` and + ``forecast.shift.carbon_intensity_shifted``), with ``flexible_fraction`` as the scope's + ``forecast.shift.flexible_fraction``. + * ``price_unshifted`` and ``carbon_intensity_unshifted``: the no-shift averages. + * ``carbon_intensity_paid`` (kg/kWh): the energy-weighted intensity paid after shifting. + """ + f = float(curve["flexible_fraction"]) + out = { + "electricity_price": interpolate_in_lambda(curve, "price_paid", lam) / 1000.0 * usd_per_price_unit, + "price_shifted": interpolate_in_lambda(curve, "price_shifted", lam) / 1000.0 * usd_per_price_unit, + "price_unshifted": float(curve["price_unshifted"]) / 1000.0 * usd_per_price_unit, + "carbon_intensity_paid": interpolate_in_lambda(curve, "carbon_paid", lam) / 1000.0, + "carbon_intensity_shifted": interpolate_in_lambda(curve, "carbon_shifted", lam) / 1000.0, + "carbon_intensity_unshifted": float(curve["carbon_unshifted"]) / 1000.0, + "flexible_fraction": f, + "flexible_energy_share": interpolate_in_lambda(curve, "flexible_energy_share", lam), + } + return out + + +def estimate_with_timing(ref: Mapping[str, Any], curve: Mapping[str, Any], lam: float, usd_per_price_unit: float = 1.0) -> Tuple[Any, float, Dict[str, float]]: + """Run the calculator on the reference run with the timed electricity price. + + Returns ``(Estimate, carbon_kg, timing_inputs)``. ``carbon_kg`` is the calculator's energy in + MWh times the carbon intensity paid in g/kWh (1 MWh = 1000 kWh, 1 kg = 1000 g). + """ + from gpu_stack.calculator import estimate + + ti = timing_inputs(curve, lam, usd_per_price_unit) + est = estimate(ref["params"], ref["tokens"], gpu=ref["gpu"], n_gpus=ref["n_gpus"], + electricity_price=ti["electricity_price"], **dict(ref.get("overrides", {}))) + carbon_kg = est.energy_mwh * ti["carbon_intensity_paid"] * 1000.0 + return est, carbon_kg, ti diff --git a/tests/test_research_heatwave_cost.py b/tests/test_research_heatwave_cost.py new file mode 100644 index 0000000..cfc94ce --- /dev/null +++ b/tests/test_research_heatwave_cost.py @@ -0,0 +1,401 @@ +"""Tests for the Heatwave cost-timing module (dollars per token against carbon per token). + +The scheduler tests use small synthetic price and carbon series (labelled synthetic here, used only +to check the code); the result-file test reads the committed result when it exists. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np +import pytest +from scipy.optimize import linprog + +from gpu_stack.research import heatwave_cost as hc + +HERE = Path(__file__).resolve().parent +RESULT = HERE.parent / "experiments" / "e003-heatwave" / "results" / "cost-v1.json" +SCENARIO = HERE.parent / "experiments" / "e003-heatwave" / "cost-scenario-v1.json" + + +def synthetic_series(n_hours: int, seed: int = 0): + """Synthetic daily cycle plus noise; price and carbon are correlated through a shared term.""" + rng = np.random.default_rng(seed) + t = np.arange(n_hours) + shared = np.sin(2 * np.pi * t / 24.0) + price = 40.0 + 15.0 * shared + 6.0 * rng.normal(size=n_hours) + carbon = 200.0 + 60.0 * shared + 25.0 * np.sin(2 * np.pi * t / 24.0 + 1.0) + 15.0 * rng.normal(size=n_hours) + return price, carbon + + +NORM = {"price_mean": 40.0, "price_std": 15.0, "carbon_mean": 200.0, "carbon_std": 60.0} + + +# --------------------------------------------------------------------------------------------- +# Model and planning problem +# --------------------------------------------------------------------------------------------- + + +def test_quantile_grid_matches_heatwave(): + forecast = pytest.importorskip("heatwave.forecast") + assert tuple(forecast.QUANTILE_LEVELS) == hc.QUANTILE_LEVELS + + +def test_flex_model_validation_and_rates(): + m = hc.FlexModel(0.7, 0.3, 24) + assert m.flexible_rate == pytest.approx(0.21) + assert m.inflexible_load == pytest.approx(0.49) + assert m.flexible_cap == pytest.approx(0.51) + with pytest.raises(ValueError): + hc.FlexModel(1.2, 0.3, 24, power_cap=1.0) + with pytest.raises(ValueError): + hc.FlexModel(0.7, 1.5, 24) + with pytest.raises(ValueError): + hc.FlexModel(0.7, 0.3, 0) + + +def test_arrived_and_due_window_one_means_immediate(): + m = hc.FlexModel(0.7, 0.3, 1) + t = np.arange(10) + assert np.allclose(m.arrived(t), m.due(t)) + + +def _transport_optimum(cost, model, t0, run_before, terminal): + """Same problem as a transportation LP over (arrival hour, run hour) pairs, to check the cumulative form. + + Work that arrived before ``t0`` and has not run yet is split into arrival cohorts oldest first + (first-in first-out is earliest-deadline first for one window length). + """ + h = len(cost) + a = model.flexible_rate + arrivals = np.arange(0, t0 + h) + # cohort remaining mass: cohorts before t0 are served oldest first up to run_before + mass = np.full(arrivals.size, a) + left = run_before + for i in range(t0): + take = min(mass[i], left) + mass[i] -= take + left -= take + cohorts = [(i, mass[i]) for i in range(arrivals.size) if mass[i] > 1e-12 or i >= t0] + n_c = len(cohorts) + var = n_c * (h + 1) # last column = carried past the horizon + c = np.zeros(var) + bounds = [] + for ci, (i, m) in enumerate(cohorts): + for k in range(h + 1): + idx = ci * (h + 1) + k + if k < h: + c[idx] = cost[k] - terminal + ok = (t0 + k >= i) and (t0 + k <= i + model.window_hours - 1) + else: + ok = i + model.window_hours - 1 >= t0 + h # deadline beyond the horizon + bounds.append((0.0, None) if ok else (0.0, 0.0)) + a_eq = np.zeros((n_c, var)) + b_eq = np.zeros(n_c) + for ci, (i, m) in enumerate(cohorts): + a_eq[ci, ci * (h + 1):(ci + 1) * (h + 1)] = 1.0 + b_eq[ci] = m + # arrivals after t0 are known inside the horizon, but a cohort arriving at hour i >= t0 is only available from hour i + a_ub = np.zeros((h, var)) + for k in range(h): + for ci in range(n_c): + a_ub[k, ci * (h + 1) + k] = 1.0 + res = linprog(c, A_ub=a_ub, b_ub=np.full(h, model.flexible_cap), A_eq=a_eq, b_eq=b_eq, bounds=bounds, method="highs") + assert res.status == 0 + return res.fun + + +@pytest.mark.parametrize("window", [3, 6]) +def test_cumulative_form_matches_transportation_form(window): + # Only cohorts whose deadline lies inside the horizon are forced; others may be carried (cost = terminal value). + rng = np.random.default_rng(1) + m = hc.FlexModel(0.6, 0.5, window) + h, t0 = 12, 20 + for _ in range(5): + cost = rng.normal(size=h) + run_before = float(m.due(t0 - 1)) + rng.uniform(0.0, 0.2) + y, fell_back = hc.plan_flexible(cost, m, t0, run_before) + assert not fell_back + terminal = float(cost.mean()) + obj = float(np.dot(cost - terminal, y)) + # the transportation form drops cohorts' carried mass cost (terminal - terminal = 0), so objectives are comparable + assert obj == pytest.approx(_transport_optimum(cost, m, t0, run_before, terminal), abs=1e-7) + + +def test_plan_respects_bounds(): + m = hc.FlexModel(0.7, 0.3, 24) + rng = np.random.default_rng(2) + t0, run_before = 48, float(m.due(47)) + 0.3 + y, fb = hc.plan_flexible(rng.normal(size=48), m, t0, run_before) + assert not fb + t = np.arange(t0, t0 + 48) + cum = run_before + np.cumsum(y) + assert np.all(y >= -1e-9) and np.all(y <= m.flexible_cap + 1e-9) + assert np.all(cum >= m.due(t) - 1e-7) + assert np.all(cum <= m.arrived(t) + 1e-7) + + +def test_rolling_schedule_meets_deadlines_and_conserves_work(): + n_days = 12 + price, carbon = synthetic_series(n_days * 24 + 48, seed=3) + for window in (24, 72): + m = hc.FlexModel(0.7, 0.5, window) + cost_by_day = np.stack([hc.mixed_cost(price[24 * d:24 * d + 48], carbon[24 * d:24 * d + 48], 0.5, NORM) for d in range(n_days)]) + y, diag = hc.schedule_forecast(cost_by_day, m, [(0, n_days)]) + assert diag["plan_fallbacks"] == 0 + t = np.arange(y.size) + cum = np.cumsum(y) + assert np.all(cum >= m.due(t) - 1e-6), "work finished after its deadline" + assert np.all(cum <= m.arrived(t) + 1e-6), "work run before it arrived" + backlog = m.arrived(t[-1]) - cum[-1] + assert 0.0 <= backlog <= m.flexible_rate * window + 1e-6 + assert np.all(y <= m.flexible_cap + 1e-9) + + +def test_window_one_and_zero_flexible_fraction_equal_no_shift(): + n_days = 5 + price, carbon = synthetic_series(n_days * 24 + 48, seed=4) + for m in (hc.FlexModel(0.7, 0.4, 1), hc.FlexModel(0.7, 0.0, 24)): + cost_by_day = np.stack([hc.mixed_cost(price[24 * d:24 * d + 48], carbon[24 * d:24 * d + 48], 0.3, NORM) for d in range(n_days)]) + y, _ = hc.schedule_forecast(cost_by_day, m, [(0, n_days)]) + assert np.allclose(y, hc.schedule_none(m, n_days * 24), atol=1e-7) + + +def test_perfect_foresight_lowers_cost_and_carbon_at_the_ends_of_lambda(): + n_days = 20 + n = n_days * 24 + price, carbon = synthetic_series(n + 48, seed=5) + m = hc.FlexModel(0.7, 0.5, 24) + pf_p = hc.padded_future(price, [24 * d for d in range(n_days)], 48) + pf_c = hc.padded_future(carbon, [24 * d for d in range(n_days)], 48) + sums, flex, fb = hc.sweep_forecast_policy(pf_p, pf_c, [0.0, 1.0], m, NORM, [(0, n_days)], price[:n], carbon[:n]) + none_s, _ = hc.sweep_none(m, 2, price[:n], carbon[:n]) + p0, c0 = hc.effective_prices(none_s[0]) + p_cost, c_cost = hc.effective_prices(sums[1]) # lam = 1: price only + p_carb, c_carb = hc.effective_prices(sums[0]) # lam = 0: carbon only + assert fb == 0 + assert p_cost < p0 and c_carb < c0 + assert p_cost <= p_carb + 1e-9 # planning on price gives the lower price + assert c_carb <= c_cost + 1e-9 # planning on carbon gives the lower carbon + + +def test_risk_quantile_changes_plan_when_spread_differs_by_hour(): + # Two hours with equal medians; the second has a wide upper tail. A median planner is indifferent, a q=0.9 planner avoids it. + m = hc.FlexModel(0.5, 1.0, 4) + cost_mid = np.array([1.0, 1.0, 5.0, 5.0]) + cost_high = np.array([1.0, 3.0, 5.0, 5.0]) + y_mid, _ = hc.plan_flexible(cost_mid, m, 0, 0.0) + y_high, _ = hc.plan_flexible(cost_high, m, 0, 0.0) + assert y_high[0] >= y_mid[0] - 1e-9 + assert y_high[1] <= y_mid[1] + 1e-9 + + +def test_reactive_obeys_deadline_and_cap_and_constant_cost_runs_on_arrival(): + n_days = 10 + price, carbon = synthetic_series(n_days * 24 + 200, seed=6) + m = hc.FlexModel(0.7, 0.5, 24) + cost = hc.mixed_cost(price, carbon, 0.5, NORM) + y = hc.schedule_reactive(cost, m, [(200, n_days)], lookback=168) + t = np.arange(y.size) + cum = np.cumsum(y) + assert np.all(cum >= m.due(t) - 1e-9) and np.all(cum <= m.arrived(t) + 1e-9) + assert np.all(y <= m.flexible_cap + 1e-9) + flat = np.zeros(n_days * 24 + 200) + y_flat = hc.schedule_reactive(flat, m, [(200, n_days)], lookback=168) + assert np.allclose(y_flat, hc.schedule_none(m, n_days * 24)) + + +def test_trailing_median_matches_numpy(): + x = np.array([5.0, 1.0, 3.0, 2.0, 9.0, 4.0]) + out = hc.trailing_median(x, 3) + assert np.isnan(out[:2]).all() + assert out[2:] == pytest.approx([3.0, 2.0, 3.0, 4.0]) + + +# --------------------------------------------------------------------------------------------- +# Accounting +# --------------------------------------------------------------------------------------------- + + +def test_no_shift_effective_values_are_plain_means(): + n = 10 * 24 + price, carbon = synthetic_series(n, seed=7) + m = hc.FlexModel(0.7, 0.3, 24) + s = hc.daily_sums(hc.schedule_none(m, n), m, price, carbon) + p, c = hc.effective_prices(s) + assert p == pytest.approx(price.mean()) and c == pytest.approx(carbon.mean()) + + +def test_shift_identity_matches_the_power_forecast_scope(): + # price paid = (1 - phi) * unshifted price + phi * shifted price, with phi the flexible energy share + n_days = 15 + n = n_days * 24 + price, carbon = synthetic_series(n + 48, seed=8) + m = hc.FlexModel(0.7, 0.4, 24) + pf_p = hc.padded_future(price, [24 * d for d in range(n_days)], 48) + pf_c = hc.padded_future(carbon, [24 * d for d in range(n_days)], 48) + sums, flex, _ = hc.sweep_forecast_policy(pf_p, pf_c, [0.5], m, NORM, [(0, n_days)], price[:n], carbon[:n]) + p, c = hc.effective_prices(sums[0]) + fl = flex[0].sum(axis=0) + phi = fl[0] / sums[0][:, 0].sum() + p_sh, c_sh = fl[1] / fl[0], fl[2] / fl[0] + assert p == pytest.approx((1 - phi) * price[:n].mean() + phi * p_sh) + assert c == pytest.approx((1 - phi) * carbon[:n].mean() + phi * c_sh) + + +def test_bootstrap_indices_shape_range_and_blocks(): + idx = hc.block_bootstrap_indices(50, 200, 7, seed=0) + assert idx.shape == (200, 50) and idx.min() >= 0 and idx.max() < 50 + # inside a block the days are consecutive (circular) + assert np.all((idx[:, 1:7] - idx[:, :6]) % 50 == 1) + iid = hc.block_bootstrap_indices(50, 100, 1, seed=0) + assert iid.shape == (100, 50) + assert np.array_equal(idx, hc.block_bootstrap_indices(50, 200, 7, seed=0)) + + +def test_effective_prices_under_bootstrap_has_boot_shape(): + sums = np.abs(np.random.default_rng(0).normal(size=(30, 3))) + 1.0 + idx = hc.block_bootstrap_indices(30, 40, 3, seed=1) + p, c = hc.effective_prices(sums, idx) + assert p.shape == (40,) and c.shape == (40,) + lo, hi = hc.percentile_interval(p) + assert lo <= np.median(p) <= hi + + +def test_fill_hourly_uses_same_hour_of_neighbouring_days(): + x = np.tile(np.arange(24, dtype=float), 6) + y = x.copy() + y[50] = np.nan + filled, n = hc.fill_hourly(y) + assert n == 1 and filled[50] == pytest.approx(x[50]) + + +def test_padded_future_copies_previous_day_past_the_end(): + v = np.arange(48, dtype=float) + out = hc.padded_future(v, [40], 16) + assert out[0, 0] == 40 and out[0, 7] == 47 + assert out[0, 8] == 24 + 0 # hour 48 -> hour 24 + assert out[0, 15] == 31 + + +def test_quantile_index_requires_grid_level(): + assert hc.quantile_index(0.5) == 10 + with pytest.raises(ValueError): + hc.quantile_index(0.123) + + +# --------------------------------------------------------------------------------------------- +# Calculator terms and the feedback path +# --------------------------------------------------------------------------------------------- + +REF = {"params": 7e9, "tokens": 2e12, "gpu": "H100-SXM", "n_gpus": 1024, "overrides": {}} + + +def test_reference_terms_agree_with_calculator(): + from gpu_stack.calculator import estimate + + terms = hc.reference_terms(REF) + est = estimate(7e9, 2e12, gpu="H100-SXM", n_gpus=1024) + assert terms["energy_mwh_per_mtoken"] * est.tokens / 1e6 == pytest.approx(est.energy_mwh) + assert terms["facility_power_mw"] * est.training_days * 24 == pytest.approx(est.energy_mwh) + assert terms["tokens_per_gpu_hour"] * est.gpu_hours == pytest.approx(est.tokens) + default_elec = terms["energy_mwh_per_mtoken"] * 1000 * terms["calculator_default_electricity_price_usd_per_kwh"] * est.tokens / 1e6 + assert default_elec == pytest.approx(est.electricity_cost) + assert terms["hardware_usd_per_mtoken"] * est.tokens / 1e6 == pytest.approx(est.hardware_cost) + + +def test_per_mtoken_units(): + terms = hc.reference_terms(REF) + r = hc.per_mtoken(50.0, 200.0, terms) # 50 USD/MWh, 200 g/kWh + assert r["electricity_usd_per_mtoken"] == pytest.approx(terms["energy_mwh_per_mtoken"] * 50.0) + assert r["co2_g_per_mtoken"] == pytest.approx(terms["energy_mwh_per_mtoken"] * 1000.0 * 200.0) + assert r["total_usd_per_mtoken"] == pytest.approx(r["electricity_usd_per_mtoken"] + terms["hardware_usd_per_mtoken"]) + + +def _toy_curve(): + return { + "flexible_fraction": 0.3, "window_hours": 24, "utilization": 0.7, "lambda": [0.0, 1.0], + "price_paid": [36.0, 30.0], "carbon_paid": [100.0, 110.0], "price_shifted": [20.0, 10.0], + "carbon_shifted": [60.0, 90.0], "flexible_energy_share": [0.3, 0.3], + "price_unshifted": 40.0, "carbon_unshifted": 120.0, + "electricity_usd_per_mtoken": [0.0, 0.0], "total_usd_per_mtoken": [0.0, 0.0], "co2_g_per_mtoken": [0.0, 0.0], + } + + +def test_timing_inputs_units_and_interpolation(): + ti = hc.timing_inputs(_toy_curve(), 0.5) + assert ti["electricity_price"] == pytest.approx(33.0 / 1000.0) + assert ti["carbon_intensity_paid"] == pytest.approx(105.0 / 1000.0) + assert ti["price_shifted"] == pytest.approx(15.0 / 1000.0) + ti_eur = hc.timing_inputs(_toy_curve(), 0.0, usd_per_price_unit=1.1) + assert ti_eur["electricity_price"] == pytest.approx(36.0 / 1000.0 * 1.1) + + +def test_estimate_with_timing_changes_only_electricity(): + from gpu_stack.calculator import estimate + + base = estimate(7e9, 2e12, gpu="H100-SXM", n_gpus=1024, electricity_price=0.040) + est, carbon_kg, ti = hc.estimate_with_timing(REF, _toy_curve(), 0.0) + assert est.energy_mwh == pytest.approx(base.energy_mwh) + assert est.hardware_cost == pytest.approx(base.hardware_cost) + assert est.electricity_cost == pytest.approx(est.energy_mwh * 1000.0 * ti["electricity_price"]) + assert carbon_kg == pytest.approx(est.energy_mwh * 1000.0 * ti["carbon_intensity_paid"]) + + +def test_lookup_curve_keys(): + doc = {"result": {"scenario": {"workload": {"utilization": 0.7}}, "pairs": {"x": {"curves": { + hc.curve_key("timestral_28M", 0.3, 24, 0.7, 0.5): {"tag": "a"}, + hc.curve_key("no_shift", 0.3, 24, 0.7, None): {"tag": "b"}}}}}} + assert hc.lookup_curve(doc, "x", "timestral_28M", 0.3, 24)["tag"] == "a" + assert hc.lookup_curve(doc, "x", "no_shift", 0.3, 24)["tag"] == "b" + + +# --------------------------------------------------------------------------------------------- +# Forecast use is causal +# --------------------------------------------------------------------------------------------- + + +def test_rolling_forecast_only_reads_history(): + forecast = pytest.importorskip("heatwave.forecast") + values = synthetic_series(24 * 40)[0] + fc = forecast.SeasonalNaiveForecaster(24, max_context=168) + t0 = [24 * 20, 24 * 21] + a = hc.rolling_forecast(values, t0, fc, 168, 48, batch=2) + changed = values.copy() + changed[24 * 20:] += 1000.0 # change everything at and after the first decision time + b = hc.rolling_forecast(changed, t0[:1], fc, 168, 48, batch=1) + assert a.shape == (2, 48, 21) + assert np.allclose(a[0], b[0]) + + +# --------------------------------------------------------------------------------------------- +# The committed result +# --------------------------------------------------------------------------------------------- + + +@pytest.mark.skipif(not RESULT.exists(), reason="result file not written yet") +def test_committed_result_has_provenance_and_consistent_curves(): + doc = json.loads(RESULT.read_text()) + prov = doc["provenance"] + assert prov["data_kind"] == "public" + assert len(prov["script_sha256"]) == 64 and prov["git"]["commit"] != "unknown" + assert any("public_caiso_np15_lmp_dam" in k for k in prov["inputs"]) + res = doc["result"] + assert res["gpu_stack_git"]["commit"] + scenario = json.loads(SCENARIO.read_text()) + assert res["scenario"]["scenario_id"] == scenario["scenario_id"] + for pair, block in res["pairs"].items(): + assert block["same_region"] is True and block["plan_fallbacks_total"] == 0 + for key, curve in block["curves"].items(): + n = len(curve["lambda"]) + for field in ("price_paid", "carbon_paid", "electricity_usd_per_mtoken", "total_usd_per_mtoken", "co2_g_per_mtoken"): + assert len(curve[field]) == n and len(curve[field + "_ci"]) == n + # total cost = electricity + the calculator's hardware cost, the same for every policy + hw = res["reference_run_terms"]["hardware_usd_per_mtoken"] + tot = np.array(curve["total_usd_per_mtoken"]) - np.array(curve["electricity_usd_per_mtoken"]) + assert np.allclose(tot, hw) + none = block["curves"][hc.curve_key("no_shift", 0.3, 24, 0.7, None)] + assert np.allclose(none["saving_pct_electricity"], 0.0) + assert np.allclose(none["price_paid"], block["mean_price_test"]) From 27807c87fb202dd4132cd262da0a831ceb4dd34d Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:07:21 -0700 Subject: [PATCH 06/52] e003: scenario v2 with GPU power levels calibrated on public H100 telemetry Compute, idle and communication-phase levels, compute share of a step and the GPU-to-air offset come from Elsayed et al. 2026 (CC BY 4.0) via the Heatwave calibration result (sha256 recorded); the checkpoint level is a measured laptop ratio. The simulator accepts calibration_notes so run metadata labels each level. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- experiments/e003-heatwave/make_scenario_v2.py | 80 ++++ .../e003-heatwave/sim-scenario-v2.json | 426 ++++++++++++++++++ gpu_stack/research/heatwave_sim.py | 16 +- tests/test_research_heatwave_sim.py | 28 ++ 4 files changed, 548 insertions(+), 2 deletions(-) create mode 100644 experiments/e003-heatwave/make_scenario_v2.py create mode 100644 experiments/e003-heatwave/sim-scenario-v2.json diff --git a/experiments/e003-heatwave/make_scenario_v2.py b/experiments/e003-heatwave/make_scenario_v2.py new file mode 100644 index 0000000..1775fdc --- /dev/null +++ b/experiments/e003-heatwave/make_scenario_v2.py @@ -0,0 +1,80 @@ +"""Build sim-scenario-v2.json: scenario v1 with GPU power levels calibrated on data. + +Usage: + python experiments/e003-heatwave/make_scenario_v2.py /results/calibration/datacenter_power.json + +The calibration result is produced by the Heatwave repo's +experiments/calibration/datacenter_power.py from PUBLIC H100 SXM telemetry +(Elsayed et al. 2026, Scientific Data 13:1268, doi:10.1038/s41597-026-07496-6, +data CC BY 4.0) and one MEASURED laptop ratio (checkpoint/compute from the NVML +energy counter of an RTX 3060 Laptop GPU run). This script copies the medians +into the scenario and records the calibration file's sha256, so every changed +number traces to that file. Everything else stays as in v1 (simulated +scenario choices and labeled assumptions). +""" + +from __future__ import annotations + +import copy +import hashlib +import json +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent + + +def main(cal_path: str) -> None: + cal_file = Path(cal_path) + cal = json.loads(cal_file.read_text()) + r = cal["result"] + v1 = json.loads((HERE / "sim-scenario-v1.json").read_text()) + v2 = copy.deepcopy(v1) + v2["scenario_id"] = v1["scenario_id"].replace("v1", "v2") if "v1" in v1["scenario_id"] else v1["scenario_id"] + "-v2" + med = lambda k: float(r[k]["median"]) # noqa: E731 + src = "Elsayed et al. 2026 H100 SXM llm_training, median over %d GPU-sessions" % r["n_gpu_sessions"] + v2["gpu"]["compute_fraction_of_tdp"] = med("compute_fraction_of_limit") + v2["gpu"]["compute_fraction_of_step"] = 1.0 - med("low_phase_fraction_of_active_time") + pl = v2["power_levels"] + pl["idle_ratio_override"] = med("idle_ratio") + pl["comm_ratio"] = med("low_phase_ratio") + pl["checkpoint_ratio"] = float(r["laptop_checkpoint"]["ratio"]) + pl["calibration_notes"] = { + "compute_w": f"public data: compute power / 700 W limit = {med('compute_fraction_of_limit'):.4f} ({src})", + "idle_w": f"public data: idle/compute = {med('idle_ratio'):.4f} ({src})", + "comm_w": f"public data: low-phase/compute = {med('low_phase_ratio'):.4f} ({src})", + "checkpoint_w": ("measured laptop data: checkpoint/compute = %.4f from the NVML energy counter " + "(RTX 3060 Laptop GPU; transfer to a datacenter GPU is an assumption)" % r["laptop_checkpoint"]["ratio"]), + } + v2["thermal"]["gpu_to_air_k_per_w"] = med("temp_per_power_k_per_w") + v2["calibration"] = { + "file": "heatwave/results/calibration/datacenter_power.json", + "sha256": hashlib.sha256(cal_file.read_bytes()).hexdigest(), + "producer_script": cal["provenance"]["script"], + "producer_script_sha256": cal["provenance"]["script_sha256"], + "producer_git_commit": cal["provenance"]["git"]["commit"], + "fields": { + "gpu.compute_fraction_of_tdp": "compute_fraction_of_limit.median", + "gpu.compute_fraction_of_step": "1 - low_phase_fraction_of_active_time.median", + "power_levels.idle_ratio_override": "idle_ratio.median", + "power_levels.comm_ratio": "low_phase_ratio.median", + "power_levels.checkpoint_ratio": "laptop_checkpoint.ratio (measured laptop)", + "thermal.gpu_to_air_k_per_w": "temp_per_power_k_per_w.median", + }, + } + v2["assumptions"] = [a for a in v1["assumptions"]] + [ + "v2 calibration: compute level, idle level, communication (low-phase) level, compute share of a step, " + "and the GPU-to-air temperature offset are medians from public H100 SXM telemetry (Elsayed et al. 2026, " + "CC BY 4.0); the checkpoint level is a measured laptop ratio. These replace the corresponding v1 values; " + "the v1 text above describing those fields is superseded by this calibration block.", + ] + out = HERE / "sim-scenario-v2.json" + out.write_text(json.dumps(v2, indent=1) + "\n") + print("wrote", out) + for k, v in v2["calibration"]["fields"].items(): + a, b = k.split(".") + print(f" {k} = {v2[a][b]} <- {v}") + + +if __name__ == "__main__": + main(sys.argv[1]) diff --git a/experiments/e003-heatwave/sim-scenario-v2.json b/experiments/e003-heatwave/sim-scenario-v2.json new file mode 100644 index 0000000..97f4197 --- /dev/null +++ b/experiments/e003-heatwave/sim-scenario-v2.json @@ -0,0 +1,426 @@ +{ + "schema": "gpu-stack.e003-heatwave-sim-scenario.v1", + "data_kind": "simulated", + "scenario_id": "e003-heatwave-sim-v2", + "description": "Two-site, eight-rack simulated GPU cluster over seven days with a synthetic job mix, a synthetic heat wave, and scheduled fault and grid events (with and without precursors). Nothing in this file is measured data.", + "start_utc": "2026-07-06T00:00:00Z", + "horizon_s": 604800, + "base_dt_s": 1.0, + "control_dt_s": 10.0, + "controller_interval_s": 60.0, + "history_s": 7200.0, + "known_future_horizon_s": 86400.0, + "sites": [ + { + "site": { + "site_id": "site-a", + "accelerator_type": "NVIDIA H100 SXM 80GB", + "accelerator_count": 104, + "accelerator_flops_per_second": 500000000000000.0, + "collective_bandwidth_bytes_per_second": 400000000000.0, + "state_transfer_bandwidth_bytes_per_second": 12500000000.0, + "checkpoint_bandwidth_bytes_per_second": 10000000000.0, + "base_power_w": 0.0, + "accelerator_power_w": 700.0, + "power_cap_w": 100000.0, + "cooling_capacity_w": 110000.0, + "grid_import_limit_w": 110000.0 + }, + "racks": [ + { + "rack_id": "a1", + "n_gpus": 32, + "breaker_rating_w": 24640.0 + }, + { + "rack_id": "a2", + "n_gpus": 32, + "breaker_rating_w": 24640.0 + }, + { + "rack_id": "a3", + "n_gpus": 24, + "breaker_rating_w": 18480.0 + }, + { + "rack_id": "a4", + "n_gpus": 16, + "breaker_rating_w": 12320.0 + } + ] + }, + { + "site": { + "site_id": "site-b", + "accelerator_type": "NVIDIA H100 SXM 80GB", + "accelerator_count": 80, + "accelerator_flops_per_second": 500000000000000.0, + "collective_bandwidth_bytes_per_second": 400000000000.0, + "state_transfer_bandwidth_bytes_per_second": 12500000000.0, + "checkpoint_bandwidth_bytes_per_second": 10000000000.0, + "base_power_w": 0.0, + "accelerator_power_w": 700.0, + "power_cap_w": 77000.0, + "cooling_capacity_w": 85000.0, + "grid_import_limit_w": 85000.0 + }, + "racks": [ + { + "rack_id": "b1", + "n_gpus": 32, + "breaker_rating_w": 24640.0 + }, + { + "rack_id": "b2", + "n_gpus": 24, + "breaker_rating_w": 18480.0 + }, + { + "rack_id": "b3", + "n_gpus": 16, + "breaker_rating_w": 12320.0 + }, + { + "rack_id": "b4", + "n_gpus": 8, + "breaker_rating_w": 6160.0 + } + ] + } + ], + "gpu": { + "preset": "h100_sxm_80gb_gpu", + "compute_fraction_of_tdp": 0.8550598580496651, + "host_fraction_of_compute": 0.25, + "compute_fraction_of_step": 0.8565830452214802 + }, + "power_levels": { + "e002_raw_path": "docs/data/e002-checkpoint-power-raw-v1.json", + "comm_ratio": 0.2492626550302487, + "checkpoint_ratio": 0.6123595506482253, + "idle_ratio_override": 0.21292761611145372, + "calibration_notes": { + "compute_w": "public data: compute power / 700 W limit = 0.8551 (Elsayed et al. 2026 H100 SXM llm_training, median over 72 GPU-sessions)", + "idle_w": "public data: idle/compute = 0.2129 (Elsayed et al. 2026 H100 SXM llm_training, median over 72 GPU-sessions)", + "comm_w": "public data: low-phase/compute = 0.2493 (Elsayed et al. 2026 H100 SXM llm_training, median over 72 GPU-sessions)", + "checkpoint_w": "measured laptop data: checkpoint/compute = 0.6124 from the NVML energy counter (RTX 3060 Laptop GPU; transfer to a datacenter GPU is an assumption)" + } + }, + "thermal": { + "heat_capacity_j_per_k_per_gpu": 8153.125, + "design_air_rise_k": 12.0, + "cooling_lag_s": 120.0, + "supply_setpoint_c": 24.0, + "outdoor_coupling_k_per_k": 0.2, + "outdoor_reference_c": 30.0, + "capacity_derate_per_k": 0.01, + "capacity_derate_reference_c": 30.0, + "gpu_to_air_k_per_w": 0.02558234685296796, + "gpu_to_air_rel_sigma": 0.1, + "breaker_reset_delay_s": 1800.0, + "gpu_shutdown_c": 95.0, + "thermal_shutdown_hold_s": 1800.0 + }, + "throttle": { + "throttle_threshold_c": 80.0, + "throttle_span_c": 8.0, + "min_clock_fraction": 0.5, + "power_exponent": 2.0 + }, + "cooling_power": { + "kind": "carnot", + "pue": 1.2, + "reference_outdoor_c": 20.0, + "reference_supply_c": 24.0, + "evaporator_approach_k": 10.0, + "condenser_approach_k": 10.0, + "cop_min": 1.5, + "cop_max": 30.0 + }, + "breaker": { + "nontrip_ratio": 1.13, + "trip_ratio": 1.45, + "conventional_time_s": 3600.0 + }, + "transfer": { + "wan_bandwidth_bytes_per_second": 3125000000.0, + "wan_latency_s": 0.02, + "restore_bandwidth_bytes_per_second": 200000000000.0, + "fixed_restart_latency_s": 0.02, + "membership_reconfiguration_s": 0.012, + "checkpoint_bytes_per_gpu": 4375000000.0 + }, + "jobs": { + "generate": { + "seed": 7, + "utilization_goal": 0.72, + "gpu_choices": [ + 8, + 16, + 32, + 64 + ], + "gpu_weights": [ + 3, + 3, + 2, + 1 + ], + "duration_range_s": [ + 3600, + 172800 + ], + "step_time_choices_s": [ + 2.0, + 4.0, + 8.0 + ], + "checkpoint_every_range_s": [ + 1200, + 3600 + ], + "checkpoint_duration_range_s": [ + 20, + 120 + ], + "value_range": [ + 1.0, + 4.0 + ], + "flexible_fraction": 0.4, + "multisite_fraction": 0.1, + "max_start_delay_s": 21600, + "initial_fraction": 0.5 + } + }, + "events": [ + { + "kind": "cooling_failure", + "event_id": "cf-1-precursor", + "site_id": "site-a", + "rack_id": "a1", + "onset_s": 129600, + "duration_s": 5400, + "remaining_capacity_fraction": 0.3, + "precursors": [ + { + "channel": "supply_temp_c", + "lead_s": 3600, + "strength": 3.0 + } + ] + }, + { + "kind": "cooling_failure", + "event_id": "cf-2-sudden", + "site_id": "site-b", + "rack_id": "b2", + "onset_s": 276480, + "duration_s": 3600, + "remaining_capacity_fraction": 0.2 + }, + { + "kind": "cooling_failure", + "event_id": "cf-3-fan-precursor", + "site_id": "site-a", + "onset_s": 440640, + "duration_s": 7200, + "remaining_capacity_fraction": 0.6, + "resistance_multiplier": 1.3, + "precursors": [ + { + "channel": "thermal_resistance", + "lead_s": 7200, + "strength": 0.25 + } + ] + }, + { + "kind": "breaker_trip", + "event_id": "bt-1-derate-precursor", + "site_id": "site-a", + "rack_id": "a1", + "onset_s": 198720, + "duration_s": 14400, + "mode": "derate", + "derate_fraction": 0.6, + "precursor_lead_s": 3600 + }, + { + "kind": "breaker_trip", + "event_id": "bt-2-forced", + "site_id": "site-b", + "rack_id": "b1", + "onset_s": 380160, + "duration_s": 1200, + "mode": "forced" + }, + { + "kind": "grid_curtailment", + "event_id": "gc-1-announced", + "site_id": "site-a", + "onset_s": 233280, + "duration_s": 7200, + "limit_fraction": 0.6, + "announce_lead_s": 1800 + }, + { + "kind": "grid_curtailment", + "event_id": "gc-2-unannounced", + "site_id": "site-b", + "onset_s": 483840, + "duration_s": 5400, + "limit_fraction": 0.5 + }, + { + "kind": "hardware_fault", + "event_id": "hw-1-precursor", + "site_id": "site-a", + "rack_id": "a2", + "gpu_slot": 5, + "onset_s": 103680, + "repair_s": 21600, + "precursors": [ + { + "channel": "gpu_temp_c", + "lead_s": 7200, + "strength": 6.0 + } + ] + }, + { + "kind": "hardware_fault", + "event_id": "hw-2-sudden", + "site_id": "site-b", + "rack_id": "b1", + "gpu_slot": 3, + "onset_s": 328320, + "repair_s": 14400 + }, + { + "kind": "hardware_fault", + "event_id": "hw-3-sudden", + "site_id": "site-a", + "rack_id": "a1", + "gpu_slot": 2, + "onset_s": 535680, + "repair_s": 14400 + } + ], + "environment": { + "seed": 11, + "synthetic": { + "start_hour_utc": 0.0, + "sites": { + "site-a": { + "utc_offset_h": -7.0, + "outdoor": { + "mean_c": 24.0, + "diurnal_amplitude_c": 7.0, + "peak_hour_local": 15.0, + "noise_sigma_c": 0.8, + "noise_ar1": 0.9, + "heatwaves": [ + { + "center_h": 96.0, + "width_h": 18.0, + "add_c": 11.0 + } + ] + }, + "forecast_error_sigma_c": 1.2, + "price": { + "base_usd_per_kwh": 0.08, + "diurnal_amplitude": 0.35, + "peak_hour_local": 18.0 + }, + "carbon": { + "base_g_per_kwh": 350.0, + "diurnal_amplitude": 0.2, + "peak_hour_local": 20.0 + } + }, + "site-b": { + "utc_offset_h": -5.0, + "outdoor": { + "mean_c": 22.0, + "diurnal_amplitude_c": 6.0, + "peak_hour_local": 15.0, + "noise_sigma_c": 0.8, + "noise_ar1": 0.9, + "heatwaves": [ + { + "center_h": 100.0, + "width_h": 20.0, + "add_c": 9.0 + } + ] + }, + "forecast_error_sigma_c": 1.2, + "price": { + "base_usd_per_kwh": 0.07, + "diurnal_amplitude": 0.3, + "peak_hour_local": 19.0 + }, + "carbon": { + "base_g_per_kwh": 450.0, + "diurnal_amplitude": 0.15, + "peak_hour_local": 19.0 + } + } + } + } + }, + "outputs": { + "gpu_windows": [ + [ + 0, + 3600 + ], + [ + 129600, + 133200 + ] + ] + }, + "assumptions": [ + "Everything in this scenario is simulated. No number here is a measurement of a real cluster.", + "Site capacities (power cap, cooling capacity, import limit) are scenario choices; the per-GPU accelerator fields follow the e001 screening scenario H100 SXM sites (experiments/e001-beyond-one-datacenter/screening-scenario-v1.json).", + "Compute-phase GPU power is 0.8 x the cited H100 SXM TDP (700 W): the 0.8 is the gpu_stack calculator's gpu_power_fraction assumption. Host overhead is 0.25 x compute power: the calculator's server_power_fraction assumption.", + "Idle/compute and relative compute-phase noise are measured ratios from the e002 laptop RTX telemetry. The communication-phase and checkpoint levels are set equal to the measured idle ratio (structural assumption, no measurement). The measured checkpoint/compute ratio is reported alongside in the result.", + "Fraction of a training step spent in compute (0.7) is an assumption.", + "Rack heat capacity per GPU = DGX H100 system mass 130.45 kg / 8 GPUs x an assumed effective specific heat of 500 J/(kg K). Mass source: https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html", + "Supply setpoint 24 C is an assumption inside the DGX H100 cited operating range of 5-30 C (same source). Design air rise 12 K, cooling lag 120 s, outdoor coupling 0.2 K/K above 30 C, capacity derate 1 percent per K above 30 C, GPU-to-air offset 0.05 K/W, protective GPU shutdown at 95 C and its 1800 s hold are assumptions.", + "Throttle threshold 80 C, span 8 K, minimum clock 0.5 and power exponent 2 are assumptions; they are not H100 datasheet values.", + "Cooling power: Carnot-shaped COP with second-law efficiency chosen so the overhead equals the gpu_stack calculator PUE assumption (1.2) at 20 C outdoor and 24 C supply; approach temperatures 10 K and COP bounds 1.5 to 30 are assumptions.", + "Breaker curve: IEC 60898-1 conventional values (1.13 and 1.45 x rated, 1 hour), https://www.se.com/au/en/faqs/FA369327/. Rack breaker ratings are 1.1 x the full-load rack power; reset delay 1800 s is an assumption.", + "WAN, restore, restart and membership parameters come from the e001 scenarios; the per-GPU checkpoint size is the e001 screening checkpoint (1.12 TB) divided by its 256 GPUs.", + "The job mix, outdoor temperature (including the heat wave), electricity price and carbon intensity are synthetic and seeded. Job value per GPU-hour is a synthetic number with no source.", + "Event precursors (supply-temperature creep, resistance rise, GPU temperature creep, breaker derate ramp, curtailment announcement) are assumptions of the simulation, not measured facts.", + "v2 calibration: compute level, idle level, communication (low-phase) level, compute share of a step, and the GPU-to-air temperature offset are medians from public H100 SXM telemetry (Elsayed et al. 2026, CC BY 4.0); the checkpoint level is a measured laptop ratio. These replace the corresponding v1 values; the v1 text above describing those fields is superseded by this calibration block." + ], + "provenance": [ + "https://www.nvidia.com/en-us/data-center/h100/", + "https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html", + "https://www.se.com/au/en/faqs/FA369327/", + "https://en.wikipedia.org/wiki/Coefficient_of_performance", + "docs/data/e002-checkpoint-power-raw-v1.json", + "experiments/e001-beyond-one-datacenter/recovery-scenario-v2.json", + "experiments/e001-beyond-one-datacenter/screening-scenario-v1.json" + ], + "calibration": { + "file": "heatwave/results/calibration/datacenter_power.json", + "sha256": "43967b1d3a15a5df4ee3b897c5eb6663a73af44b2154406ad5d066d22db07ad1", + "producer_script": "experiments/calibration/datacenter_power.py", + "producer_script_sha256": "f6aa2a6ca6feab65fef67693086f1662e5c157b4e6237c99e090b41638dcf3e3", + "producer_git_commit": "494f1eb58930cd0405e3881d7e021fdabc28ec21", + "fields": { + "gpu.compute_fraction_of_tdp": "compute_fraction_of_limit.median", + "gpu.compute_fraction_of_step": "1 - low_phase_fraction_of_active_time.median", + "power_levels.idle_ratio_override": "idle_ratio.median", + "power_levels.comm_ratio": "low_phase_ratio.median", + "power_levels.checkpoint_ratio": "laptop_checkpoint.ratio (measured laptop)", + "thermal.gpu_to_air_k_per_w": "temp_per_power_k_per_w.median" + } + } +} diff --git a/gpu_stack/research/heatwave_sim.py b/gpu_stack/research/heatwave_sim.py index d868425..540e20e 100644 --- a/gpu_stack/research/heatwave_sim.py +++ b/gpu_stack/research/heatwave_sim.py @@ -225,6 +225,7 @@ def from_e002( host_fraction_of_compute: float = 0.0, idle_ratio_override: Optional[float] = None, noise_scale: float = 1.0, + calibration_notes: Optional[Mapping[str, str]] = None, ) -> "PowerLevels": """Scale measured laptop ratios to a datacenter GPU. @@ -232,7 +233,9 @@ def from_e002( measured checkpoint ratio; not available for communication), ``'idle_ratio'`` (use the measured idle ratio), or a number. ``idle_ratio_override`` replaces the measured idle ratio when set (the - caller then owns the citation). + caller then owns the citation). ``calibration_notes`` replaces the + provenance label of any level (keys as in ``provenance``) when a + scenario calibrates it from data instead of assuming it. """ ratios = measured["ratio_to_compute"] compute = float(tdp_w) * float(compute_fraction_of_tdp) @@ -263,6 +266,10 @@ def resolve(spec: Union[str, float], what: str) -> float: ckpt = resolve(checkpoint_ratio, "checkpoint_w") prov["host_w_per_gpu"] = f"assumed: {host_fraction_of_compute} x compute_w" prov["noise_rel_sigma"] = f"measured laptop relative std of compute-phase board power x scale {noise_scale}" + for key, note in (calibration_notes or {}).items(): + if key not in prov: + raise ValueError(f"calibration_notes key {key!r} is not a power level") + prov[key] = str(note) return cls( compute_w=compute, comm_w=compute * comm, @@ -808,6 +815,7 @@ def build_config(scenario: Mapping[str, Any], seed: int = 0, environment: Option host_fraction_of_compute=gcfg["host_fraction_of_compute"], idle_ratio_override=pl.get("idle_ratio_override"), noise_scale=pl.get("noise_scale", 1.0), + calibration_notes=pl.get("calibration_notes"), ) th = ThermalAssumptions(**scenario["thermal"]) throttle = GPUThrottleModel(**scenario["throttle"]) @@ -2039,7 +2047,11 @@ def _meta(self) -> dict[str, Any]: "site_carbon_kg_1h": "kg CO2 per hour", }, "quantity_provenance": { - "gpu_power_levels": "simulated; idle/compute and checkpoint/compute ratios derived from measured e002 laptop telemetry, scaled to cited H100 SXM TDP; other levels assumed", + "gpu_power_levels": ( + "simulated; levels set per power_levels.provenance (scenario calibration notes applied)" + if any(str(v).startswith(("public data:", "measured laptop data:")) for v in cfg.levels.provenance.values()) + else "simulated; idle/compute and checkpoint/compute ratios derived from measured e002 laptop telemetry, scaled to cited H100 SXM TDP; other levels assumed" + ), "thermal": "simulated; RC parameters are scenario assumptions (DGX H100 mass cited)", "cooling_power": "simulated; PUE baseline from the gpu_stack calculator assumption, Carnot COP shape", "events": "simulated; precursors are assumptions of the simulation, not measured facts", diff --git a/tests/test_research_heatwave_sim.py b/tests/test_research_heatwave_sim.py index 7d643d2..a93baed 100644 --- a/tests/test_research_heatwave_sim.py +++ b/tests/test_research_heatwave_sim.py @@ -627,3 +627,31 @@ def test_default_scenario_runs_for_a_few_hours(): assert s["data_kind"] == "simulated" assert s["jobs"] > 0 and s["useful_gpu_hours"] > 0 assert res.to_arrays()["meta"]["data_kind"] == "simulated" + + +def test_scenario_v2_calibration_notes_label_levels(): + """v2 levels carry their data labels, and the run metadata says so.""" + import copy + import json as _json + from pathlib import Path as _Path + + from gpu_stack.research import heatwave_sim as _hs + + path = _Path(__file__).resolve().parents[1] / "experiments/e003-heatwave/sim-scenario-v2.json" + sc = _hs.load_scenario(path) + assert sc["calibration"]["sha256"] + small = copy.deepcopy(sc) + small["horizon_s"] = 3 * 3600 + small["outputs"] = {"gpu_windows": [[0, 60]]} + meta = _hs.run_scenario(small, seed=0).to_arrays()["meta"] + prov = meta["power_levels"]["provenance"] + assert prov["idle_w"].startswith("public data") + assert prov["checkpoint_w"].startswith("measured laptop data") + assert "calibration notes applied" in meta["quantity_provenance"]["gpu_power_levels"] + bad = copy.deepcopy(small) + bad["power_levels"]["calibration_notes"] = {"not_a_level": "x"} + import pytest as _pytest + + with _pytest.raises(ValueError): + _hs.run_scenario(bad, seed=0) + _json.dumps(meta, default=str) From d2f904f0e9ded59ba87e3da632b80bb4755887f7 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:09:05 -0700 Subject: [PATCH 07/52] Add offline optimum bound, known-covariate path, policy lists in the scenario, evaluate stage Co-Authored-By: Claude Sonnet --- .../e003-heatwave/cost-scenario-v1.json | 176 +++++++++++++++--- experiments/e003-heatwave/run_cost.py | 32 +++- gpu_stack/research/heatwave_cost.py | 97 +++++++++- tests/test_research_heatwave_cost.py | 29 +++ 4 files changed, 298 insertions(+), 36 deletions(-) diff --git a/experiments/e003-heatwave/cost-scenario-v1.json b/experiments/e003-heatwave/cost-scenario-v1.json index 6eaadec..59a8633 100644 --- a/experiments/e003-heatwave/cost-scenario-v1.json +++ b/experiments/e003-heatwave/cost-scenario-v1.json @@ -4,8 +4,8 @@ "data_kind": "public", "description": "Cost timing: a cluster running a reference training job against real public hourly price and carbon-intensity series (CAISO NP15 day-ahead price with CAISO in-state intensity, and DE-LU day-ahead price with a DE lifecycle-intensity proxy; each pair is from one region). A share f of the work may be delayed by up to a window W; each day the scheduler plans the flexible work from a 48-hour quantile forecast. The series are public data; the workload (utilization, flexible share, windows) is a scenario assumption and nothing here is measured on a real cluster.", "reference_run": { - "params": 7e9, - "tokens": 2e12, + "params": 7000000000.0, + "tokens": 2000000000000.0, "gpu": "H100-SXM", "n_gpus": 1024, "overrides": {}, @@ -17,29 +17,159 @@ "normalization_days": 90, "note": "26 whole weeks ending 2026-10-25. The 90 days before the first test day fix the z-score constants only. Nothing is tuned on the test period." }, - "decision": {"hour_utc": 0, "horizon_hours": 48, "run_hours": 24}, + "decision": { + "hour_utc": 0, + "horizon_hours": 48, + "run_hours": 24 + }, "workload": { "utilization": 0.7, "power_cap": 1.0, - "flexible_fractions": [0.1, 0.3, 0.5], - "windows_hours": [24, 72], - "headline": {"flexible_fraction": 0.3, "window_hours": 24}, - "utilization_sensitivity": [0.5, 0.9], - "note": "utilization is the mean offered load as a fraction of the cluster's full-load power (an assumption, not a measurement); power_cap is the most total load allowed in any hour, as a fraction of full-load power." - }, - "lambdas": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], - "planning_quantiles": {"default": 0.5, "risk_sweep": [0.1, 0.5, 0.75, 0.9], "risk_sweep_flexible_fraction": 0.3}, - "pairs": ["caiso", "de"], - "context_hours": {"timestral": 2048, "classical": 672}, + "flexible_fractions": [ + 0.1, + 0.3, + 0.5 + ], + "windows_hours": [ + 24, + 72 + ], + "headline": { + "flexible_fraction": 0.3, + "window_hours": 24 + }, + "utilization_sensitivity": [ + 0.5, + 0.9 + ], + "note": "utilization is the mean offered load as a fraction of the cluster's full-load power (an assumption, not a measurement); power_cap is the most total load allowed in any hour, as a fraction of full-load power.", + "utilization_sensitivity_policies": [ + "perfect", + "timestral_28M", + "seasonal_naive_24" + ] + }, + "lambdas": [ + 0.0, + 0.1, + 0.2, + 0.3, + 0.4, + 0.5, + 0.6, + 0.7, + 0.8, + 0.9, + 1.0 + ], + "planning_quantiles": { + "default": 0.5, + "risk_sweep": [ + 0.1, + 0.5, + 0.75, + 0.9 + ], + "risk_sweep_flexible_fraction": 0.3 + }, + "pairs": [ + "caiso", + "de" + ], + "context_hours": { + "timestral": 2048, + "classical": 672 + }, "forecasters": { - "seasonal_naive_24": {"kind": "seasonal_naive", "season_length": 24, "max_context": 672, "kind_label": "classical"}, - "ets_24": {"kind": "ets", "season_length": 24, "max_context": 672, "kind_label": "classical"}, - "arima_seasonal": {"kind": "arima", "order": [2, 0, 1], "season_length": 24, "seasonal_order": [1, 1, 0], "max_context": 672, "kind_label": "classical"}, - "timestral_5M": {"kind": "timestral", "size": "5M", "threads": 1, "kind_label": "timestral zero-shot"}, - "timestral_28M": {"kind": "timestral", "size": "28M", "threads": 1, "kind_label": "timestral zero-shot"}, - "timestral_209M": {"kind": "timestral", "size": "209M", "threads": 1, "kind_label": "timestral zero-shot", "subsample_only": true} - }, - "subsample_weeks": [0, 4, 8, 12, 16, 20, 24], - "reactive": {"lookback_hours": 168}, - "bootstrap": {"n": 2000, "block_days": 7, "seed": 0, "level": 0.95} + "seasonal_naive_24": { + "kind": "seasonal_naive", + "season_length": 24, + "max_context": 672, + "kind_label": "classical" + }, + "ets_24": { + "kind": "ets", + "season_length": 24, + "max_context": 672, + "kind_label": "classical" + }, + "arima_seasonal": { + "kind": "arima", + "order": [ + 2, + 0, + 1 + ], + "season_length": 24, + "seasonal_order": [ + 1, + 1, + 0 + ], + "max_context": 672, + "kind_label": "classical" + }, + "timestral_5M": { + "kind": "timestral", + "size": "5M", + "threads": 1, + "kind_label": "timestral zero-shot" + }, + "timestral_28M": { + "kind": "timestral", + "size": "28M", + "threads": 1, + "kind_label": "timestral zero-shot" + }, + "timestral_28M_cov": { + "kind": "timestral", + "size": "28M", + "threads": 1, + "known_covariates": true, + "kind_label": "timestral zero-shot with day-ahead covariates" + }, + "timestral_209M": { + "kind": "timestral", + "size": "209M", + "threads": 1, + "kind_label": "timestral zero-shot", + "subsample_only": true + } + }, + "subsample_weeks": [ + 0, + 4, + 8, + 12, + 16, + 20, + 24 + ], + "reactive": { + "lookback_hours": 168 + }, + "bootstrap": { + "n": 2000, + "block_days": 7, + "seed": 0, + "level": 0.95 + }, + "policies": { + "planned": [ + "seasonal_naive_24", + "ets_24", + "arima_seasonal", + "timestral_5M", + "timestral_28M", + "timestral_28M_cov" + ], + "subsample_extra": [ + "timestral_209M" + ], + "unplanned": [ + "no_shift", + "reactive" + ], + "upper_bound": "perfect" + } } diff --git a/experiments/e003-heatwave/run_cost.py b/experiments/e003-heatwave/run_cost.py index b80cb20..9a17a7e 100644 --- a/experiments/e003-heatwave/run_cost.py +++ b/experiments/e003-heatwave/run_cost.py @@ -70,7 +70,7 @@ def series_for(pair: str, scenario: dict) -> dict: def forecast_tasks(scenario: dict) -> list: - order = ["seasonal_naive_24", "timestral_5M", "timestral_28M", "ets_24", "arima_seasonal", "timestral_209M"] + order = ["seasonal_naive_24", "timestral_5M", "timestral_28M", "timestral_28M_cov", "ets_24", "arima_seasonal", "timestral_209M"] tasks = [] for fk in order: spec = scenario["forecasters"][fk] @@ -102,7 +102,12 @@ def forecast_stage(scenario: dict, worker: int, of: int) -> None: fc_cache = {fk: hc.make_forecaster(spec)} ctx = ctx_cfg["timestral"] if spec["kind"] == "timestral" else ctx_cfg["classical"] t0 = time.time() - vals = hc.compute_week(data[pair][what], data[pair]["test_start"], week, fc_cache[fk], ctx, horizon) + known = None + if spec.get("known_covariates"): + if "known" not in data[pair]: + data[pair]["known"] = hc.known_covariate_matrix(pair, data[pair]["index"]) + known = data[pair]["known"]["matrix"] + vals = hc.compute_week(data[pair][what], data[pair]["test_start"], week, fc_cache[fk], ctx, horizon, known=known) tmp = path.with_suffix(".tmp.npz") np.savez_compressed(tmp, values=vals, levels=np.array(hc.QUANTILE_LEVELS)) tmp.rename(path) @@ -113,7 +118,9 @@ def forecast_stage(scenario: dict, worker: int, of: int) -> None: # Schedule stage: one cached task = one policy configuration swept over all lambdas # --------------------------------------------------------------------------------------------- -LP_POLICIES = ["seasonal_naive_24", "ets_24", "arima_seasonal", "timestral_5M", "timestral_28M", "perfect"] +def planned_policies(scenario: dict) -> list: + """Policies that solve the planning problem; the perfect-foresight upper bound is last.""" + return list(scenario["policies"]["planned"]) + [scenario["policies"]["upper_bound"]] def day_blocks_for(scenario: dict, which: str) -> list: @@ -130,19 +137,22 @@ def schedule_task_list(scenario: dict) -> list: tasks = [] for pair in scenario["pairs"]: for which in ("full", "sub"): - pols = LP_POLICIES + (["timestral_209M"] if which == "sub" else []) + pols = planned_policies(scenario) + (list(scenario["policies"]["subsample_extra"]) if which == "sub" else []) for pol in pols: for f in wl["flexible_fractions"]: for w in wl["windows_hours"]: tasks.append(dict(pair=pair, which=which, policy=pol, f=f, W=w, rho=rho0, q=q_default)) + for f in wl["flexible_fractions"]: + for w in wl["windows_hours"]: + tasks.append(dict(pair=pair, which="full", policy="offline_optimum", f=f, W=w, rho=rho0, q=q_default)) for q in scenario["planning_quantiles"]["risk_sweep"]: if q == q_default: continue - for pol in LP_POLICIES[:-1]: + for pol in scenario["policies"]["planned"]: for w in wl["windows_hours"]: tasks.append(dict(pair=pair, which="full", policy=pol, f=f_risk, W=w, rho=rho0, q=q)) for rho in wl["utilization_sensitivity"]: - for pol in ("perfect", "timestral_28M", "seasonal_naive_24"): + for pol in wl["utilization_sensitivity_policies"]: hf = wl["headline"] tasks.append(dict(pair=pair, which="full", policy=pol, f=hf["flexible_fraction"], W=hf["window_hours"], rho=rho, q=q_default)) return tasks @@ -193,6 +203,9 @@ def run_schedule_task(ctx: PairContext, scenario: dict, t: dict) -> dict: price_real, carbon_real = ctx.realised(real_blocks) model = hc.FlexModel(t["rho"], t["f"], t["W"], scenario["workload"]["power_cap"]) lambdas = scenario["lambdas"] + if t["policy"] == "offline_optimum": + sums, flex = hc.sweep_offline(price_real, carbon_real, lambdas, model, ctx.norm) + return {"sums": sums, "flex": flex, "fallbacks": 0} if t["policy"] == "perfect": pq, cq = ctx.perfect("price", days), ctx.perfect("carbon", days) else: @@ -229,7 +242,7 @@ def schedule_stage(scenario: dict, worker: int, of: int) -> None: # Evaluate stage: bootstrap, per-token numbers, result JSON # --------------------------------------------------------------------------------------------- -POLICY_ORDER = ["no_shift", "reactive", "seasonal_naive_24", "ets_24", "arima_seasonal", "timestral_5M", "timestral_28M", "timestral_209M", "perfect"] +POLICY_ORDER = ["no_shift", "reactive", "seasonal_naive_24", "ets_24", "arima_seasonal", "timestral_5M", "timestral_28M", "timestral_28M_cov", "timestral_209M", "perfect", "offline_optimum"] def load_fx() -> tuple: @@ -426,11 +439,16 @@ def evaluate_stage(scenario: dict) -> None: def main() -> None: + global OUT, SCHED, SCENARIO ap = argparse.ArgumentParser() ap.add_argument("stage", choices=["forecast", "schedule", "evaluate"]) ap.add_argument("--worker", type=int, default=0) ap.add_argument("--of", type=int, default=1) + ap.add_argument("--scenario", default=str(SCENARIO)) + ap.add_argument("--out", default=str(OUT)) + ap.add_argument("--sched-dir", default=str(SCHED)) a = ap.parse_args() + OUT, SCHED, SCENARIO = Path(a.out), Path(a.sched_dir), Path(a.scenario) scenario = hc.load_scenario(SCENARIO) if a.stage == "forecast": forecast_stage(scenario, a.worker, a.of) diff --git a/gpu_stack/research/heatwave_cost.py b/gpu_stack/research/heatwave_cost.py index 5e1b0c6..153fd19 100644 --- a/gpu_stack/research/heatwave_cost.py +++ b/gpu_stack/research/heatwave_cost.py @@ -232,6 +232,35 @@ def schedule_forecast( return y_all, {"plan_fallbacks": float(fallbacks)} +def offline_optimum(cost: np.ndarray, model: FlexModel) -> np.ndarray: + """Whole-period optimum with every cost known: one linear program over all hours. + + Minimises ``sum_t (cost[t] - mean(cost)) * y[t]`` with the same constraints as the daily plan, + using the cumulative flexible work ``Y[t]`` as the variable (sparse bidiagonal rows). This is + the tightest bound for the mixed cost; the perfect-foresight policy (the daily scheduler fed + realised values) can differ from it because it plans 48 hours at a time. + """ + import scipy.sparse as sp + from scipy.optimize import linprog + + cost = np.asarray(cost, dtype=np.float64) + n = cost.size + t = np.arange(n) + c_adj = cost - cost.mean() + nxt = np.append(c_adj[1:], 0.0) + obj = c_adj - nxt # coefficient of Y[t] in sum_t c_adj[t] * (Y[t] - Y[t-1]) with Y[-1] = 0 + diff = sp.diags([np.ones(n), -np.ones(n - 1)], [0, -1], format="csr") # (Y[t] - Y[t-1]) + a_ub = sp.vstack([diff, -diff], format="csr") + b_ub = np.concatenate([np.full(n, model.flexible_cap), np.zeros(n)]) + lo = model.due(t) - _TOL + hi = model.arrived(t) + _TOL + res = linprog(obj, A_ub=a_ub, b_ub=b_ub, bounds=list(zip(lo, hi)), method="highs") + if res.status != 0: + raise RuntimeError(f"offline optimum not solved: {res.message}") + y = np.diff(res.x, prepend=0.0) + return np.clip(y, 0.0, model.flexible_cap) + + def schedule_none(model: FlexModel, n_hours: int) -> np.ndarray: """No shifting: flexible work runs in its arrival hour.""" return np.full(n_hours, model.flexible_rate) @@ -479,15 +508,59 @@ def forecast_windows(values: np.ndarray, t0: Sequence[int], context_len: int, ho return ctx, fut -def rolling_forecast(values: np.ndarray, t0: Sequence[int], forecaster: Any, context_len: int, horizon: int, batch: int = 4) -> np.ndarray: +def known_covariate_matrix(pair: str, index: Any, include_weather: bool = True) -> Dict[str, Any]: + """Day-ahead covariates that are known over the forecast horizon, on the hourly ``index``. + + CAISO: the day-ahead demand forecast. DE-LU: day-ahead load, solar and wind forecasts. Weather + forecasts (Open-Meteo, day-ahead vintage) are added when they have been collected; if the file + is absent the status says so and the matrix is built without them. The archived forecasts hold + one value per hour; their issue time relative to a 00:00 UTC decision is not verified for the + hours beyond the first day. + """ + from heatwave.data import public as P + + region = PAIR_SOURCES[pair]["price"][0] + cov = P.load_forecast_covariates(region).resample("1h").mean().reindex(index) + names = list(cov.columns) + cols = [cov[c].to_numpy() for c in names] + status = {"weather_forecast": "not requested"} + if include_weather: + wregion = {"caiso": "bayarea", "de": "frankfurt"}[region] + try: + w = P.load_weather(wregion, kind="forecast", vintage="dayahead").resample("1h").mean().reindex(index) + for c in w.columns: + names.append("weather_" + c) + cols.append(w[c].to_numpy()) + status = {"weather_forecast": "included", "weather_columns": list(w.columns)} + except FileNotFoundError: + status = {"weather_forecast": "not collected (Open-Meteo quota); the path is built and skipped"} + return {"matrix": np.stack(cols).astype(np.float64), "names": names, "status": status} + + +def covariate_windows(known: np.ndarray, t0: Sequence[int], context_len: int, horizon: int) -> np.ndarray: + """Known-covariate windows (n, C, context_len + horizon); hours past the end of the series are NaN.""" + out = np.full((len(t0), known.shape[0], context_len + horizon), np.nan) + for k, t in enumerate(t0): + seg = known[:, t - context_len:t + horizon] + out[k, :, :seg.shape[1]] = seg + return out + + +def rolling_forecast(values: np.ndarray, t0: Sequence[int], forecaster: Any, context_len: int, horizon: int, batch: int = 4, + known: Optional[np.ndarray] = None) -> np.ndarray: """Forecast ``horizon`` hours at every ``t0`` from the ``context_len`` hours before it. Returns (n, horizon, 21). - Only values before ``t0`` enter a forecast. + Only values before ``t0`` enter a forecast. ``known`` is an optional (C, N) matrix of + covariates known in advance, aligned with ``values``; their values over the horizon are passed to the forecaster. """ out = [] for k in range(0, len(t0), batch): - ctx, _ = forecast_windows(values, t0[k:k + batch], context_len, horizon) - q = forecaster.forecast(ctx, horizon) + chunk = list(t0[k:k + batch]) + ctx, _ = forecast_windows(values, chunk, context_len, horizon) + if known is None: + q = forecaster.forecast(ctx, horizon) + else: + q = forecaster.forecast(ctx, horizon, known_covariates=covariate_windows(known, chunk, context_len, horizon)) out.append(q.values[:, 0]) return np.concatenate(out, axis=0) @@ -496,10 +569,11 @@ def week_cache_path(cache_dir: "str | Path", series_key: str, forecaster_key: st return Path(cache_dir) / f"{series_key}__{forecaster_key}__w{week:02d}.npz" -def compute_week(values: np.ndarray, test_start: int, week: int, forecaster: Any, context_len: int, horizon: int, batch: int = 4) -> np.ndarray: +def compute_week(values: np.ndarray, test_start: int, week: int, forecaster: Any, context_len: int, horizon: int, batch: int = 4, + known: Optional[np.ndarray] = None) -> np.ndarray: """Forecasts for the 7 daily decision times (00:00 UTC) of one test week. Returns (7, horizon, 21), float32.""" t0 = [test_start + (7 * week + d) * HOURS_PER_DAY for d in range(7)] - return rolling_forecast(values, t0, forecaster, context_len, horizon, batch).astype(np.float32) + return rolling_forecast(values, t0, forecaster, context_len, horizon, batch, known).astype(np.float32) def quantile_index(q: float) -> int: @@ -653,3 +727,14 @@ def estimate_with_timing(ref: Mapping[str, Any], curve: Mapping[str, Any], lam: electricity_price=ti["electricity_price"], **dict(ref.get("overrides", {}))) carbon_kg = est.energy_mwh * ti["carbon_intensity_paid"] * 1000.0 return est, carbon_kg, ti + + +def sweep_offline(price_real: np.ndarray, carbon_real: np.ndarray, lambdas: Sequence[float], model: FlexModel, + norm: Mapping[str, float]) -> Tuple[np.ndarray, np.ndarray]: + """Whole-period optimum for every ``lam`` (one block); daily sums as in :func:`sweep_forecast_policy`.""" + sums, flex = [], [] + for lam in lambdas: + y = offline_optimum(mixed_cost(price_real, carbon_real, lam, norm), model) + sums.append(daily_sums(y, model, price_real, carbon_real)) + flex.append(flexible_daily_sums(y, price_real, carbon_real)) + return np.stack(sums), np.stack(flex) diff --git a/tests/test_research_heatwave_cost.py b/tests/test_research_heatwave_cost.py index cfc94ce..c2309a7 100644 --- a/tests/test_research_heatwave_cost.py +++ b/tests/test_research_heatwave_cost.py @@ -399,3 +399,32 @@ def test_committed_result_has_provenance_and_consistent_curves(): none = block["curves"][hc.curve_key("no_shift", 0.3, 24, 0.7, None)] assert np.allclose(none["saving_pct_electricity"], 0.0) assert np.allclose(none["price_paid"], block["mean_price_test"]) + + +def test_offline_optimum_is_feasible_and_no_worse_than_rolling_perfect_foresight(): + n_days = 10 + n = n_days * 24 + price, carbon = synthetic_series(n + 48, seed=9) + m = hc.FlexModel(0.7, 0.5, 24) + cost = hc.mixed_cost(price[:n], carbon[:n], 0.5, NORM) + y_off = hc.offline_optimum(cost, m) + t = np.arange(n) + cum = np.cumsum(y_off) + assert np.all(cum >= m.due(t) - 1e-6) and np.all(cum <= m.arrived(t) + 1e-6) + assert np.all(y_off <= m.flexible_cap + 1e-9) and np.all(y_off >= -1e-9) + pf = hc.padded_future(price, [24 * d for d in range(n_days)], 48) + pc = hc.padded_future(carbon, [24 * d for d in range(n_days)], 48) + cost_rows = np.stack([hc.mixed_cost(pf[d], pc[d], 0.5, NORM) for d in range(n_days)]) + y_roll, _ = hc.schedule_forecast(cost_rows, m, [(0, n_days)]) + ref = cost.mean() + obj_off = float(np.dot(cost - ref, y_off)) + obj_roll = float(np.dot(cost - ref, y_roll)) + assert obj_off <= obj_roll + 1e-7 + + +def test_covariate_windows_pad_past_the_end_with_nan(): + known = np.arange(2 * 100, dtype=float).reshape(2, 100) + w = hc.covariate_windows(known, [90], 20, 24) + assert w.shape == (1, 2, 44) + assert w[0, 0, 0] == known[0, 70] and w[0, 0, 29] == known[0, 99] + assert np.isnan(w[0, :, 30:]).all() From dd4fd45b3a3c01ccd9f413cdfcc6b7c11cf8f883 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:09:33 -0700 Subject: [PATCH 08/52] Add power-cap packing module, scenario, runner and tests (work in progress, simulated data) Co-Authored-By: Claude Sonnet --- .../e003-heatwave/packing-scenario-v1.json | 224 ++++ experiments/e003-heatwave/run_packing.py | 471 +++++++++ gpu_stack/research/heatwave_packing.py | 975 ++++++++++++++++++ tests/test_research_heatwave_packing.py | 266 +++++ 4 files changed, 1936 insertions(+) create mode 100644 experiments/e003-heatwave/packing-scenario-v1.json create mode 100644 experiments/e003-heatwave/run_packing.py create mode 100644 gpu_stack/research/heatwave_packing.py create mode 100644 tests/test_research_heatwave_packing.py diff --git a/experiments/e003-heatwave/packing-scenario-v1.json b/experiments/e003-heatwave/packing-scenario-v1.json new file mode 100644 index 0000000..3319a4e --- /dev/null +++ b/experiments/e003-heatwave/packing-scenario-v1.json @@ -0,0 +1,224 @@ +{ + "schema": "gpu-stack.e003-heatwave-packing-scenario.v1", + "data_kind": "simulated", + "scenario_id": "e003-heatwave-packing-v1", + "description": "Power-cap packing episodes on the two-site e003 simulator. Every quantity is simulated; every number below is a scenario assumption (nothing is measured data). Power levels, cooling and thermal parameters are read from sim-scenario-v1.json and the measured e002 ratios it points to; the site caps are computed at run time from a commissioning calibration run.", + "base_scenario": "sim-scenario-v1.json", + "episode": { + "horizon_s": 147600, + "warmup_s": 90000, + "base_dt_s": 10.0, + "control_dt_s": 10.0, + "controller_interval_s": 100.0, + "decision_interval_s": 300.0, + "forecast_bin_s": 300.0, + "forecast_horizon_bins": 36, + "context_bins": 576, + "min_context_bins": 24, + "warm_ratio_bins": 24, + "start_hour_utc": 13.0 + }, + "cap": { + "load_fraction": 0.8, + "design_outdoor_c": 35.0, + "calibration": { + "idle_hours": 1, + "full_hours": 2 + } + }, + "jobs": { + "generate": { + "utilization_goal": 1.5, + "gpu_choices": [ + 8, + 16, + 32, + 64 + ], + "gpu_weights": [ + 3, + 3, + 2, + 1 + ], + "duration_range_s": [ + 5400, + 36000 + ], + "step_time_choices_s": [ + 2.0, + 4.0, + 8.0 + ], + "checkpoint_every_range_s": [ + 1200, + 3600 + ], + "checkpoint_duration_range_s": [ + 20, + 120 + ], + "value_range": [ + 1.0, + 4.0 + ], + "flexible_fraction": 1.0, + "multisite_fraction": 0.1, + "max_start_delay_s": 0, + "initial_fraction": 0.5 + } + }, + "heatwave_draw": { + "center_h_range": [ + 28.0, + 36.0 + ], + "site_offset_h": 2.0, + "width_h_range": [ + 6.0, + 10.0 + ], + "add_c_range": [ + 6.0, + 12.0 + ] + }, + "events": { + "rates_per_day": { + "cooling_failure": 0.3, + "breaker_trip": 0.3, + "grid_curtailment": 0.3, + "hardware_fault": 1.0 + }, + "precursor_probability": 0.5, + "lead_range_s": [ + 1800, + 7200 + ], + "strength_ranges": { + "supply_temp_c": [ + 2.0, + 5.0 + ], + "gpu_temp_c": [ + 3.0, + 8.0 + ] + }, + "duration_range_s": [ + 1800, + 7200 + ], + "remaining_capacity_range": [ + 0.3, + 0.7 + ], + "derate_range": [ + 0.6, + 0.9 + ], + "limit_fraction_range": [ + 0.5, + 0.8 + ], + "repair_range_s": [ + 7200, + 21600 + ] + }, + "penalty": { + "default_usd_per_kwh": 10.0, + "sweep_usd_per_kwh": [ + 2.5, + 10.0, + 40.0, + 160.0 + ] + }, + "reward_window_s": 10800, + "queue_order": "value_density", + "reactive": { + "margin": 0.97 + }, + "seeds": { + "calibration": 9000, + "dev": [ + 9101, + 9102, + 9103 + ], + "train": [ + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108 + ], + "validation": [ + 107, + 108 + ], + "test": [ + 500, + 501, + 502, + 503, + 504, + 505 + ] + }, + "policies": { + "full_grid_forecasters": [ + "timestral-5M" + ], + "subset_forecasters": [ + "timestral-28M", + "seasonal-naive", + "ets", + "arima" + ], + "subset_q": [ + 0.5, + 0.8, + 0.9, + 0.95, + 0.99 + ], + "use_covariates": [ + "timestral-5M", + "timestral-28M" + ] + }, + "learning": { + "bandwidths": [ + 0.5, + 1.0, + 2.0 + ], + "ridges": [ + 1.0, + 10.0, + 100.0 + ], + "n_rff": 256, + "model_seed": 0 + }, + "bootstrap": { + "n_boot": 2000, + "level": 0.95, + "seed": 0 + }, + "assumptions": [ + "Everything here is simulated; no number is a measurement of a real cluster.", + "Cap: each site cap is its calibrated idle power plus cap.load_fraction of the calibrated idle-to-full-load power span, both measured in the simulator at a constant design outdoor temperature (cap.design_outdoor_c). The design temperature and load fraction are assumptions chosen on calibration and dev seeds, not on test seeds.", + "Jobs: synthetic queue with a backlog (utilization goal above 1), GPU counts, durations (1.5 to 10 h, log-uniform), checkpoint cadence and value per GPU-hour (USD, no source) are assumptions. All jobs are marked flexible so the packer may hold them; a job is available at its submit time.", + "Heat wave: each episode draws a heat wave (centre, width, size) added to the sim scenario's diurnal outdoor temperature; episode time 0 is 06:00 local at the first site (start_hour_utc). The event draw rates, precursor probability and ranges are assumptions of the simulation.", + "Over-cap penalty (USD per kWh above the site cap) is a scenario parameter; the default and the sweep are assumptions.", + "Planning horizon (3 h in 5-minute steps) and the learned policy's surrogate reward window (3 h) are scenario choices.", + "Warm-up (first 25 h of each 41 h episode) uses one shared rule for all forecast policies and is excluded from every reported metric; the evaluation window is the last 16 h." + ] +} diff --git a/experiments/e003-heatwave/run_packing.py b/experiments/e003-heatwave/run_packing.py new file mode 100644 index 0000000..755532b --- /dev/null +++ b/experiments/e003-heatwave/run_packing.py @@ -0,0 +1,471 @@ +"""Power-cap packing experiment for the e003 Heatwave simulator (simulated data, task T06a). + +Stages (each stays well under 20 minutes on 2 cores; run them in this order):: + + python experiments/e003-heatwave/run_packing.py calibrate + python experiments/e003-heatwave/run_packing.py collect [--shard 0/2] + python experiments/e003-heatwave/run_packing.py train + python experiments/e003-heatwave/run_packing.py eval --group GROUP [--shard 0/2] + python experiments/e003-heatwave/run_packing.py merge + +``GROUP`` is one of ``t5m-fixed``, ``t28m-fixed``, ``seasonal-naive``, ``ets``, +``arima``, ``reactive``, ``learned``, ``oracle`` (``oracle`` needs the +``t5m-fixed`` runs at q=0.9 for its reference ratio path). Part files go to +``results/packing-v1-parts/`` (each with script hash, git commit and seeds); +``merge`` writes ``results/packing-v1.json`` through ``heatwave.provenance``. +Existing part files are kept, so a stage can be resumed. + +Every quantity is simulated. The scenario is ``packing-scenario-v1.json``; +power levels, cooling and thermal parameters are read from the simulator +scenario and the measured e002 ratios it points to, never typed here. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import subprocess +import sys +import time +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[1] +sys.path.insert(0, str(ROOT)) + +from gpu_stack.research import heatwave_packing as hp # noqa: E402 +from gpu_stack.research.heatwave_sim import load_scenario # noqa: E402 + +SCENARIO = HERE / "packing-scenario-v1.json" +SIM_SCENARIO = HERE / "sim-scenario-v1.json" +E002_RAW = ROOT / "docs" / "data" / "e002-checkpoint-power-raw-v1.json" +PARTS = HERE / "results" / "packing-v1-parts" +OUT = HERE / "results" / "packing-v1.json" +CAL_PATH = PARTS / "calibration.json" +MODEL_PATH = PARTS / "model.json" + + +# --------------------------------------------------------------------------- +# provenance and files +# --------------------------------------------------------------------------- + + +def sha_file(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def git_state() -> dict: + def git(*args: str) -> str: + return subprocess.run(["git", *args], cwd=ROOT, capture_output=True, text=True, check=True).stdout.strip() + + dirty = bool(git("status", "--porcelain", "--", ".", ":(exclude)experiments/e003-heatwave/results")) + return {"commit": git("rev-parse", "HEAD"), "branch": git("rev-parse", "--abbrev-ref", "HEAD"), "dirty": str(dirty).lower()} + + +def part_provenance(**extra) -> dict: + return { + "script": str(Path(__file__).resolve().relative_to(ROOT)), + "script_sha256": sha_file(Path(__file__)), + "module_sha256": sha_file(ROOT / "gpu_stack" / "research" / "heatwave_packing.py"), + "scenario_sha256": sha_file(SCENARIO), + "git": git_state(), + "data_kind": "simulated", + **extra, + } + + +def write_json(path: Path, obj: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.JSONEncoder(indent=1, allow_nan=False).encode(_clean(obj)) + "\n") + + +def _clean(v): + if isinstance(v, np.ndarray): + return _clean(v.tolist()) + if isinstance(v, np.generic): + return _clean(v.item()) + if isinstance(v, dict): + return {str(k): _clean(x) for k, x in v.items()} + if isinstance(v, (list, tuple)): + return [_clean(x) for x in v] + if isinstance(v, float) and not np.isfinite(v): + return None + return v + + +def load_models() -> dict: + cal = json.loads(CAL_PATH.read_text())["result"] + return { + sid: hp.SiteModel(d["site_id"], d["n_gpus"], d["idle_w"], d["full_w"], d["cap_w"], d["utc_offset_h"]) + for sid, d in cal["sites"].items() + } + + +def setup(): + packing = hp.load_packing_scenario(SCENARIO) + sim = load_scenario(SIM_SCENARIO) + return packing, sim, hp.PackingSettings.from_scenario(packing) + + +def shard_filter(items: list, shard: str) -> list: + i, n = (int(x) for x in shard.split("/")) + return [x for k, x in enumerate(items) if k % n == i] + + +# --------------------------------------------------------------------------- +# forecasters +# --------------------------------------------------------------------------- + + +def make_forecaster(name: str, packing: dict, threads: int): + ep = packing["episode"] + season = int(round(86400.0 / ep["forecast_bin_s"])) + if name.startswith("timestral-"): + from heatwave.forecast.timestral_fc import TimestralForecaster + + return TimestralForecaster(size=name.split("-")[1], threads=threads) + from heatwave.forecast import ARIMAForecaster, ETSForecaster, SeasonalNaiveForecaster + + if name == "seasonal-naive": + return SeasonalNaiveForecaster(season_length=season, max_context=int(ep["context_bins"])) + if name == "ets": + return ETSForecaster(season_length=1, max_context=season) + if name == "arima": + return ARIMAForecaster(order=(2, 1, 1), max_context=season) + raise ValueError(f"unknown forecaster {name!r}") + + +GROUPS = { + "t5m-fixed": "timestral-5M", + "t28m-fixed": "timestral-28M", + "seasonal-naive": "seasonal-naive", + "ets": "ets", + "arima": "arima", +} + + +def policy_id(forecaster: str, label: str) -> str: + return f"{forecaster}/{label}" + + +def fixed_levels(packing: dict, forecaster: str) -> list: + if forecaster in packing["policies"]["full_grid_forecasters"]: + return list(hp.QUANTILE_LEVELS) + return list(packing["policies"]["subset_q"]) + + +# --------------------------------------------------------------------------- +# stages +# --------------------------------------------------------------------------- + + +def stage_calibrate() -> None: + packing, sim, _ = setup() + from heatwave.forecast import QUANTILE_LEVELS + + assert tuple(QUANTILE_LEVELS) == hp.QUANTILE_LEVELS, "quantile grid differs from heatwave.forecast" + cal = hp.calibrate_sites(sim, packing) + cal.pop("models") + write_json(CAL_PATH, {"provenance": part_provenance(seed=packing["seeds"]["calibration"], + sim_scenario_sha256=sha_file(SIM_SCENARIO), + e002_raw_sha256=sha_file(E002_RAW)), + "result": cal}) + print(f"wrote {CAL_PATH}") + + +def run_one(packing, sim, settings, models, seed: int, controller_factory): + ep = hp.build_episode(packing, sim, models, seed) + site_ids = list(models) + ctrl = controller_factory(ep, site_ids) + t0 = time.time() + res = hp.run_episode(ep, ctrl) + wall = time.time() - t0 + return ep, ctrl, res, wall + + +def stage_collect(args) -> None: + packing, sim, settings = setup() + models = load_models() + seeds = shard_filter(list(packing["seeds"]["train"]), args.shard) + fc = make_forecaster("timestral-5M", packing, args.threads) + for seed in seeds: + out = PARTS / f"collect-seed{seed}.json" + if out.exists(): + continue + + def factory(ep, site_ids): + return hp.PackingController( + ep.jobs, models, site_ids, settings, ep.hour0_local, mode="forecast", forecaster=fc, + policy=hp.RandomQ(seed), use_covariates="timestral-5M" in packing["policies"]["use_covariates"], + log_situations=True, + ) + + ep, ctrl, res, wall = run_one(packing, sim, settings, models, seed, factory) + metrics = hp.episode_metrics(res, ctrl, ep, settings) + sit = ctrl.situations() + p_min, b_min = ctrl.minute_records() + np.savez_compressed(PARTS / f"collect-seed{seed}.npz", p_min=p_min, b_min=b_min, **sit) + write_json(out, {"provenance": part_provenance(seed=seed, role="train-exploration", forecaster="timestral-5M", + policy="random-q"), + "result": {"metrics": metrics, "wall_s": wall, "n_situations": int(sit["t"].shape[0]), + "draws": ep.draws}}) + print(f"collect seed {seed}: {sit['t'].shape[0]} situations, wall {wall:.0f}s", flush=True) + + +def stage_train(args) -> None: + packing, sim, settings = setup() + models_d = load_models() + models = list(models_d.values()) + penalties = list(packing["penalty"]["sweep_usd_per_kwh"]) + train_seeds = list(packing["seeds"]["train"]) + val_seeds = set(packing["seeds"]["validation"]) + parts = {"train": [], "val": []} + for seed in train_seeds: + z = np.load(PARTS / f"collect-seed{seed}.npz") + sit = {k: z[k] for k in z.files if k not in ("p_min", "b_min")} + costs, value = hp.hindsight_costs(sit, z["p_min"], z["b_min"], models, penalties, settings.call_s, settings.reward_window_s) + usable = sit["t"] + settings.reward_window_s <= settings.horizon_s + X_all, C_all, V_all = [], [], [] + for pi, lam in enumerate(penalties): + X = sit["x"].copy() + X[:, hp.FEATURE_NAMES.index("log_penalty")] = np.log(lam) + X_all.append(X[usable]) + C_all.append(costs[pi][usable]) + V_all.append(value[usable]) + parts["val" if seed in val_seeds else "train"].append((np.concatenate(X_all), np.concatenate(C_all), np.concatenate(V_all))) + + def stack(items): + return tuple(np.concatenate([it[i] for it in items]) for i in range(3)) + + Xtr, Ctr, Vtr = stack(parts["train"]) + Xva, Cva, Vva = stack(parts["val"]) + lc = packing["learning"] + grid = [] + best = None + for bw in lc["bandwidths"]: + for rg in lc["ridges"]: + m = hp.CostSensitiveModel(n_rff=lc["n_rff"], bandwidth=bw, ridge=rg, seed=lc["model_seed"]).fit(Xtr, Ctr, Vtr) + row = {"bandwidth": bw, "ridge": rg, "val_regret_per_value": m.regret(Xva, Cva, Vva), + "train_regret_per_value": m.regret(Xtr, Ctr, Vtr)} + grid.append(row) + if best is None or row["val_regret_per_value"] < best[0]["val_regret_per_value"]: + best = (row, m) + row = best[0] + Xall, Call, Vall = np.concatenate([Xtr, Xva]), np.concatenate([Ctr, Cva]), np.concatenate([Vtr, Vva]) + final = hp.CostSensitiveModel(n_rff=lc["n_rff"], bandwidth=row["bandwidth"], ridge=row["ridge"], seed=lc["model_seed"]).fit(Xall, Call, Vall) + # reference regrets on all rows: always the median level, and the best single level in hindsight + ref = { + "median_level_regret_per_value": float(Call[:, hp.MEDIAN_INDEX].sum() / Vall.sum()), + "best_single_level_regret_per_value": float(Call.sum(axis=0).min() / Vall.sum()), + "best_single_level": float(hp.QUANTILE_LEVELS[int(Call.sum(axis=0).argmin())]), + "refit_regret_per_value_on_all": final.regret(Xall, Call, Vall), + } + write_json(MODEL_PATH, {"provenance": part_provenance(train_seeds=train_seeds, validation_seeds=sorted(val_seeds), + penalties=penalties), + "result": {"selected": row, "grid": grid, "reference_regrets": ref, + "n_rows_train": int(Xtr.shape[0]), "n_rows_validation": int(Xva.shape[0]), + "feature_names": hp.FEATURE_NAMES, "model": final.to_dict()}}) + print(f"wrote {MODEL_PATH}: selected {row}") + + +def eval_jobs(packing: dict, group: str) -> list: + """List of (policy_id, kind, forecaster, level_or_penalty).""" + jobs = [] + if group in GROUPS: + fcn = GROUPS[group] + for q in fixed_levels(packing, fcn): + jobs.append((policy_id(fcn, f"q{q:g}"), "fixed", fcn, q)) + elif group == "reactive": + jobs.append(("reactive", "reactive", None, None)) + elif group == "oracle": + jobs.append(("oracle", "oracle", None, None)) + elif group == "learned": + for lam in packing["penalty"]["sweep_usd_per_kwh"]: + jobs.append((policy_id("timestral-5M", f"learned@{lam:g}"), "learned", "timestral-5M", lam)) + else: + raise ValueError(f"unknown group {group!r}") + return jobs + + +def stage_eval(args) -> None: + packing, sim, settings = setup() + models = load_models() + seeds = list(packing["seeds"]["test"]) + jobs = [(j, s) for j in eval_jobs(packing, args.group) for s in seeds] + jobs = shard_filter(jobs, args.shard) + cache = {} + learned = None + if args.group == "learned": + learned = hp.LearnedQ(hp.CostSensitiveModel.from_dict(json.loads(MODEL_PATH.read_text())["result"]["model"])) + for (pid, kind, fcn, arg), seed in jobs: + out = PARTS / "eval" / f"{pid.replace('/', '__').replace('@', '_at_')}__seed{seed}.json" + if out.exists(): + continue + use_cov = fcn in packing["policies"]["use_covariates"] if fcn else False + if fcn and fcn not in cache: + cache[fcn] = make_forecaster(fcn, packing, args.threads) + pen = float(packing["penalty"]["default_usd_per_kwh"]) + + def factory(ep, site_ids): + if kind == "reactive": + return hp.PackingController(ep.jobs, models, site_ids, settings, ep.hour0_local, mode="reactive") + if kind == "oracle": + tr = np.load(PARTS / f"trace-seed{seed}.npz")["ratio_bins"] + return hp.PackingController(ep.jobs, models, site_ids, settings, ep.hour0_local, mode="oracle", oracle_ratio=tr) + policy = hp.FixedQ(arg) if kind == "fixed" else learned + penalty = float(arg) if kind == "learned" else pen + return hp.PackingController( + ep.jobs, models, site_ids, settings, ep.hour0_local, mode="forecast", forecaster=cache[fcn], policy=policy, + penalty_usd_per_kwh=penalty, use_covariates=use_cov, + ) + + ep, ctrl, res, wall = run_one(packing, sim, settings, models, seed, factory) + metrics = hp.episode_metrics(res, ctrl, ep, settings) + if pid == "timestral-5M/q0.9": + np.savez_compressed(PARTS / f"trace-seed{seed}.npz", ratio_bins=ctrl.ratio_bins()) + write_json(out, {"provenance": part_provenance(seed=seed, policy=pid, forecaster=fcn, use_covariates=use_cov), + "result": {"policy": pid, "kind": kind, "seed": seed, "metrics": metrics, "wall_s": wall, + "draws": ep.draws}}) + print(f"{pid} seed {seed}: gpu_h {metrics['gpu_hours']:.0f} viol {metrics['violation_rate']:.3f} " + f"over {metrics['energy_above_cap_kwh']:.2f} kWh wall {wall:.0f}s", flush=True) + + +# --------------------------------------------------------------------------- +# merge +# --------------------------------------------------------------------------- + +METRIC_KEYS = ("gpu_hours", "value_usd", "jobs_admitted", "jobs_completed", "violation_rate", "energy_above_cap_kwh", + "seconds_above_cap", "stranded_kwh", "mean_power_over_cap", "gpu_hours_multisite_jobs", "max_excess_w") + + +def stage_merge(args) -> None: + packing, sim, settings = setup() + bs = packing["bootstrap"] + penalties = list(packing["penalty"]["sweep_usd_per_kwh"]) + runs: dict = {} + files = sorted((PARTS / "eval").glob("*.json")) + for f in files: + rec = json.loads(f.read_text())["result"] + runs.setdefault(rec["policy"], {})[rec["seed"]] = rec["metrics"] + seeds = list(packing["seeds"]["test"]) + cal = json.loads(CAL_PATH.read_text())["result"] + summary = {} + for pid, by_seed in sorted(runs.items()): + ordered = [by_seed[s] for s in seeds if s in by_seed] + ent = {"n_seeds": len(ordered), "seeds": [s for s in seeds if s in by_seed]} + for key in METRIC_KEYS: + ent[key] = hp.bootstrap_ci([m[key] for m in ordered], bs["n_boot"], bs["seed"], bs["level"]) + ent["net_value_usd"] = { + f"{lam:g}": hp.bootstrap_ci([hp.net_value(m, lam) for m in ordered], bs["n_boot"], bs["seed"], bs["level"]) + for lam in penalties + } + summary[pid] = ent + + # Pareto data per forecaster family: violation rate against GPU-hours (means and intervals). + families: dict = {} + for pid, ent in summary.items(): + fam, _, label = pid.partition("/") + if not label: + fam, label = "reference", pid + families.setdefault(fam, []).append({ + "policy": pid, "label": label, + "violation_rate": ent["violation_rate"], "gpu_hours": ent["gpu_hours"], "value_usd": ent["value_usd"], + "energy_above_cap_kwh": ent["energy_above_cap_kwh"], + }) + points = [(p["policy"], p["violation_rate"]["mean"], p["gpu_hours"]["mean"]) for fam in families.values() for p in fam] + frontier = [] + for name, v, g in points: + dominated = any((v2 <= v and g2 >= g) and (v2 < v or g2 > g) for _, v2, g2 in points) + if not dominated: + frontier.append(name) + + # Paired differences of the learned policy against every other policy, per penalty. + paired = {} + for lam in penalties: + lid = policy_id("timestral-5M", f"learned@{lam:g}") + if lid not in runs: + continue + rows = {} + for pid, by_seed in runs.items(): + if pid == lid or pid.startswith("timestral-5M/learned@"): + continue + common = [s for s in seeds if s in by_seed and s in runs[lid]] + if not common: + continue + d = [hp.net_value(runs[lid][s], lam) - hp.net_value(by_seed[s], lam) for s in common] + rows[pid] = hp.bootstrap_ci(d, bs["n_boot"], bs["seed"], bs["level"]) + paired[f"{lam:g}"] = {"learned_policy": lid, "net_value_difference_usd_vs": rows} + + selection_by_penalty = {} + for lam in penalties: + key = f"{lam:g}" + fixed = {pid: e["net_value_usd"][key]["mean"] for pid, e in summary.items() if "/q" in pid} + if fixed: + best = max(fixed, key=fixed.get) + selection_by_penalty[key] = {"best_fixed_policy_by_mean_net_value_on_test_seeds": best, "mean_net_value_usd": fixed[best]} + + model_rec = json.loads(MODEL_PATH.read_text())["result"] if MODEL_PATH.exists() else None + collect = [json.loads(p.read_text())["result"] for p in sorted(PARTS.glob("collect-seed*.json"))] + result = { + "data_kind": "simulated", + "scenario_id": packing["scenario_id"], + "scenario": {k: packing[k] for k in ("episode", "cap", "jobs", "heatwave_draw", "events", "penalty", "reward_window_s", + "queue_order", "reactive", "policies", "learning", "bootstrap", "seeds")}, + "assumptions": packing["assumptions"], + "calibration": cal, + "metric_definitions": { + "window": "evaluation window = last (horizon_s - warmup_s) seconds of each episode; warm-up excluded", + "gpu_hours": "useful GPU-hours delivered in the window (net of work lost to faults), summed over jobs", + "value_usd": "sum over jobs of window GPU-hours times value per GPU-hour", + "violation_rate": "fraction of decision intervals (300 s) in the window in which any site's power exceeded its cap, at the simulator's 10 s control-step samples", + "energy_above_cap_kwh": "sum over sites and samples of the excess over the cap, in kWh", + "seconds_above_cap": "number of site-samples above the cap times the sample length, summed over sites", + "stranded_kwh": "headroom below the cap (kWh, both sites) in intervals where a queued job that fit in free GPUs was refused", + "net_value_usd": "value_usd minus penalty (USD per kWh above cap) times energy_above_cap_kwh", + "ci": "percentile bootstrap interval of the mean over held-out episodes", + }, + "policies": summary, + "pareto": {"families": families, "non_dominated_by_mean": frontier, + "axes": {"x": "violation_rate (mean over held-out episodes)", "y": "gpu_hours (mean)"}}, + "paired_net_value_difference": paired, + "best_fixed_by_penalty": selection_by_penalty, + "learned_model": None if model_rec is None else {k: v for k, v in model_rec.items() if k != "model"}, + "training_episodes": [{"seed": c["draws"]["sim_seed"], "n_situations": c["n_situations"]} for c in collect], + "per_seed_metrics": {pid: {str(s): m for s, m in by.items()} for pid, by in sorted(runs.items())}, + } + from heatwave.provenance import write_result + + inputs = [SCENARIO, SIM_SCENARIO, E002_RAW, CAL_PATH, MODEL_PATH] + files + record = write_result(OUT, result, script=__file__, data_kind="simulated", inputs=inputs, + notes="All outputs simulated. Held-out episodes only; training episodes are listed separately.") + record["provenance"]["git"] = git_state() + record["provenance"]["script"] = str(Path(__file__).resolve().relative_to(ROOT)) + record["provenance"]["inputs"] = {str(Path(p).resolve().relative_to(ROOT)): sha_file(Path(p)) for p in inputs if Path(p).exists()} + OUT.write_text(json.JSONEncoder(indent=1, allow_nan=False).encode(record) + "\n") + print(f"wrote {OUT} ({OUT.stat().st_size / 1024:.0f} KiB)") + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("stage", choices=["calibrate", "collect", "train", "eval", "merge"]) + ap.add_argument("--group", default="") + ap.add_argument("--shard", default="0/1") + ap.add_argument("--threads", type=int, default=1) + args = ap.parse_args() + if args.stage == "calibrate": + stage_calibrate() + elif args.stage == "collect": + stage_collect(args) + elif args.stage == "train": + stage_train(args) + elif args.stage == "eval": + stage_eval(args) + else: + stage_merge(args) + + +if __name__ == "__main__": + main() diff --git a/gpu_stack/research/heatwave_packing.py b/gpu_stack/research/heatwave_packing.py new file mode 100644 index 0000000..f38d96c --- /dev/null +++ b/gpu_stack/research/heatwave_packing.py @@ -0,0 +1,975 @@ +"""Power-cap packing with forecast quantiles and a learned quantile-choice policy (e003, task T06a). + +Everything this module produces is SIMULATED (it drives the e003 simulator in +``heatwave_sim``). Nothing here is a claim about a real cluster. + +The decision +------------ +A site has a power cap. Jobs arrive and wait in a queue. Every decision +interval (``decision_interval_s``, 300 s in the v1 scenario) the controller +forecasts the site power over the next ``forecast_horizon_bins * bin_s`` seconds +(1 h in v1) for the running set plus a candidate job, and admits the candidate +if the PLANNED power stays under the cap at every step of the horizon. +"Planned" means the forecast quantile ``q``: a low ``q`` packs more jobs and +risks cap violations, a high ``q`` is safe and strands power. + +Site power model (shared by every policy) +----------------------------------------- +Forecasters see a dimensionless ratio series, not watts:: + + s(t) = P_site(t) / (idle_w + beta * busy_gpus(t)) + +``idle_w`` and ``beta`` come from a commissioning calibration run of the same +simulator (:func:`calibrate_sites`): site power with every GPU idle and with +every GPU running a training job, at the scenario's design outdoor +temperature (``cap.design_outdoor_c``), so ``s = 1`` on a design-temperature day. ``beta = (full_w - idle_w) / n_gpus`` is a telemetry-derived +estimate of the power a newly admitted GPU adds (no nameplate value is used). +The ratio ``s`` carries what the affine model cannot: cooling overhead as the +outdoor temperature moves, throttling, faults, phase and noise. A forecaster +produces 21 quantile paths of ``s``; the planned power for a set of jobs is:: + + planned_q(h) = (idle_w + beta * busy_plan(h)) * s_q(h) + +``busy_plan(h)`` drops a running job from the bin in which its declared +walltime ends (users declare GPU count and walltime at submission, as in a batch +scheduler). Every forecaster goes through this same rule, so differences +between policies come from the forecast of ``s`` and from the choice of ``q``. +The model treats a candidate as adding the fleet-average increment per GPU, so +the candidate's own power is not forecast separately. + +Policies +-------- +* ``FixedQ(q)``: the same level for every decision (q on the 21-level grid). +* ``RandomQ``: uniform level per situation (exploration for training data). +* ``LearnedQ``: a cost-sensitive model that maps situation features to a level + (:class:`CostSensitiveModel`). +* reactive: no forecast; admits if measured power plus the estimated increment + fits and throttles racks on a violation. +* oracle: the realized ratio path from a reference run (perfect foresight). + +Training signal for the learned policy +-------------------------------------- +For a logged situation (decision time, candidate, forecast) and each level +``k`` the rule gives an admit/refuse decision ``d_k``. Its value over the +reward window is ``d * V - penalty * E_over(d)`` with ``V`` the candidate's +value over the window and ``E_over`` the energy above the cap in the window, +computed from the logged realized ratio path with the candidate's power added +or removed linearly (:func:`hindsight_costs`). This is a myopic, additive +surrogate: later decisions are not re-played. Evaluation never uses it: the +held-out episodes are run through the full simulator. + +Violation measures use the simulator's control-step power samples +(``base_dt_s`` in the scenario, 10 s in v1), not 1 s samples. +""" + +from __future__ import annotations + +import copy +import hashlib +import json +import math +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Mapping, Optional, Sequence + +import numpy as np + +from .heatwave_events import random_schedule +from .heatwave_sim import ( + AdmitJob, + DeferJob, + Job, + ReactiveSiteCapController, + SiteEnvironment, + VisibleState, + generate_jobs, + run_scenario, + synthetic_environment, +) + +PACKING_SCHEMA = "gpu-stack.e003-heatwave-packing-scenario.v1" +RESULT_SCHEMA = "gpu-stack.e003-heatwave-packing-result.v1" + +# Must equal heatwave.forecast.QUANTILE_LEVELS (the runner asserts this). +QUANTILE_LEVELS: tuple[float, ...] = ( + 0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, + 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.99, +) +N_LEVELS = len(QUANTILE_LEVELS) +MEDIAN_INDEX = QUANTILE_LEVELS.index(0.5) +SLACK_FEATURE_INDICES = (5, 10, 15, 18, 19, 20) +FEATURE_NAMES = ( + "spread_rel", "value_per_gpu_hour", "log_penalty", "headroom_rel", "size_rel", + "tod_sin", "tod_cos", "trend_rel", +) + tuple(f"slack_q{int(round(QUANTILE_LEVELS[i] * 100)):02d}" for i in SLACK_FEATURE_INDICES) +N_FEATURES = len(FEATURE_NAMES) + + +def level_index(q: float) -> int: + """Index of ``q`` on the 21-level grid.""" + for i, level in enumerate(QUANTILE_LEVELS): + if abs(level - q) < 1e-9: + return i + raise ValueError(f"q={q} is not on the 21-level grid") + + +# --------------------------------------------------------------------------- +# Scenario and calibration +# --------------------------------------------------------------------------- + + +def load_packing_scenario(path: str | Path) -> dict[str, Any]: + sc = json.loads(Path(path).read_text()) + if sc.get("schema") != PACKING_SCHEMA: + raise ValueError(f"packing scenario schema must be {PACKING_SCHEMA!r}") + if sc.get("data_kind") != "simulated": + raise ValueError("packing scenario data_kind must be 'simulated'") + ep = sc["episode"] + for key in ("horizon_s", "warmup_s", "base_dt_s", "control_dt_s", "controller_interval_s", + "decision_interval_s", "forecast_bin_s", "forecast_horizon_bins"): + if key not in ep: + raise ValueError(f"episode.{key} is required") + if ep["warmup_s"] >= ep["horizon_s"]: + raise ValueError("warmup_s must be shorter than horizon_s") + for key in ("decision_interval_s", "forecast_bin_s", "warmup_s", "horizon_s"): + if abs(ep[key] / ep["controller_interval_s"] - round(ep[key] / ep["controller_interval_s"])) > 1e-9: + raise ValueError(f"episode.{key} must be a multiple of controller_interval_s") + if abs(ep["decision_interval_s"] - ep["forecast_bin_s"]) > 1e-9: + raise ValueError("decision_interval_s must equal forecast_bin_s") + if abs(sc["cap"]["load_fraction"]) > 1.5 or sc["cap"]["load_fraction"] <= 0: + raise ValueError("cap.load_fraction must be in (0, 1.5]") + return sc + + +@dataclass(frozen=True) +class SiteModel: + """Calibrated affine site-power model (simulated). Watts at the reference outdoor temperature.""" + + site_id: str + n_gpus: int + idle_w: float + full_w: float + cap_w: float + utc_offset_h: float + + @property + def beta_w_per_gpu(self) -> float: + return (self.full_w - self.idle_w) / self.n_gpus + + def base_power(self, busy: Any) -> Any: + return self.idle_w + self.beta_w_per_gpu * np.asarray(busy, dtype=float) + + def to_dict(self) -> dict[str, Any]: + return { + "site_id": self.site_id, "n_gpus": self.n_gpus, "idle_w": self.idle_w, "full_w": self.full_w, + "cap_w": self.cap_w, "beta_w_per_gpu": self.beta_w_per_gpu, "utc_offset_h": self.utc_offset_h, + } + + +def _constant_environment(sim_scenario: Mapping[str, Any], horizon_s: float, outdoor_c: float) -> dict[str, SiteEnvironment]: + n = int(round(horizon_s / 3600.0)) + 1 + out = {} + for item in sim_scenario["sites"]: + out[item["site"]["site_id"]] = SiteEnvironment( + outdoor_c=np.full(n, float(outdoor_c)), price_usd_per_kwh=np.full(n, 0.1), carbon_g_per_kwh=np.full(n, 400.0) + ) + return out + + +def _calibration_scenario(sim_scenario: Mapping[str, Any], episode: Mapping[str, Any], horizon_s: float) -> dict[str, Any]: + sc = copy.deepcopy(dict(sim_scenario)) + sc["horizon_s"] = float(horizon_s) + sc["base_dt_s"] = float(episode["base_dt_s"]) + sc["control_dt_s"] = float(episode["control_dt_s"]) + sc["controller_interval_s"] = float(episode["controller_interval_s"]) + sc["events"] = [] + sc["outputs"] = {"gpu_windows": []} + sc["jobs"] = {"list": []} + return sc + + +def _site_loaded_jobs(sim_scenario: Mapping[str, Any], busy_fraction: float, horizon_s: float) -> list[Job]: + jobs = [] + for item in sim_scenario["sites"]: + site = item["site"] + n = max(1, int(round(site["accelerator_count"] * busy_fraction))) + jobs.append( + Job( + job_id=f"cal-{site['site_id']}", n_gpus=n, submit_s=0.0, start_s=0.0, duration_s=10.0 * horizon_s, + step_time_s=4.0, checkpoint_every_s=10.0 * horizon_s, checkpoint_duration_s=0.0, + value_per_gpu_hour=1.0, flexible=False, site_id=site["site_id"], + ) + ) + return jobs + + +def _settled_mean_power(sim_scenario, episode, horizon_s, busy_fraction, settle_fraction, seed, outdoor_c) -> np.ndarray: + sc = _calibration_scenario(sim_scenario, episode, horizon_s) + env = _constant_environment(sc, horizon_s, outdoor_c) + jobs = _site_loaded_jobs(sc, busy_fraction, horizon_s) if busy_fraction > 0 else [] + res = run_scenario(sc, controller=None, seed=seed, environment=env, jobs=jobs) + n0 = int(res.site_power_w.shape[0] * (1.0 - settle_fraction)) + return res.site_power_w[n0:].mean(axis=0) + + +def calibrate_sites(sim_scenario: Mapping[str, Any], packing: Mapping[str, Any]) -> dict[str, Any]: + """Commissioning calibration: idle and full-load site power at the reference outdoor temperature. + + Runs the simulator with no events and a constant outdoor temperature equal + to ``cap.design_outdoor_c`` (no jobs, then one all-GPU job per site), and + sets each site's cap at ``idle + cap.load_fraction * (full - idle)``. Also + runs a half-loaded check and reports how far the affine model is from it. + """ + ep = packing["episode"] + cal = packing["cap"]["calibration"] + seed = int(packing["seeds"]["calibration"]) + ref_c = float(packing["cap"]["design_outdoor_c"]) + idle = _settled_mean_power(sim_scenario, ep, cal["idle_hours"] * 3600.0, 0.0, 0.5, seed, ref_c) + full = _settled_mean_power(sim_scenario, ep, cal["full_hours"] * 3600.0, 1.0, 0.5, seed, ref_c) + half = _settled_mean_power(sim_scenario, ep, cal["full_hours"] * 3600.0, 0.5, 0.5, seed, ref_c) + frac = float(packing["cap"]["load_fraction"]) + env_spec = sim_scenario["environment"]["synthetic"]["sites"] + models = {} + rows = [] + for i, item in enumerate(sim_scenario["sites"]): + site = item["site"] + sid = site["site_id"] + n = int(site["accelerator_count"]) + cap = float(idle[i] + frac * (full[i] - idle[i])) + models[sid] = SiteModel(sid, n, float(idle[i]), float(full[i]), cap, float(env_spec[sid].get("utc_offset_h", 0.0))) + busy_half = max(1, int(round(n * 0.5))) + pred = float(idle[i] + (full[i] - idle[i]) / n * busy_half) + rows.append({ + "site_id": sid, "busy_gpus": busy_half, "measured_w": float(half[i]), "affine_prediction_w": pred, + "relative_error": float((pred - half[i]) / half[i]), + }) + return { + "data_kind": "simulated", + "design_outdoor_c": ref_c, + "cap_load_fraction": frac, + "sites": {sid: m.to_dict() for sid, m in models.items()}, + "half_load_check": rows, + "models": models, + } + + +# --------------------------------------------------------------------------- +# Episodes +# --------------------------------------------------------------------------- + + +@dataclass +class Episode: + seed: int + scenario: dict[str, Any] + jobs: list[Job] + environment: dict[str, SiteEnvironment] + models: dict[str, SiteModel] + hour0_local: tuple[float, ...] + draws: dict[str, Any] = field(default_factory=dict) + + +def build_episode( + packing: Mapping[str, Any], + sim_scenario: Mapping[str, Any], + models: Mapping[str, SiteModel], + seed: int, +) -> Episode: + """One seeded episode: caps, job queue, outdoor series (with a heat wave draw) and event draws.""" + ep = packing["episode"] + horizon = float(ep["horizon_s"]) + sc = copy.deepcopy(dict(sim_scenario)) + sc["horizon_s"] = horizon + sc["base_dt_s"] = float(ep["base_dt_s"]) + sc["control_dt_s"] = float(ep["control_dt_s"]) + sc["controller_interval_s"] = float(ep["controller_interval_s"]) + sc["outputs"] = {"gpu_windows": []} + sc["jobs"] = {"list": []} + start_hour = float(ep.get("start_hour_utc", 0.0)) + sc["start_utc"] = f"2026-07-06T{int(start_hour):02d}:00:00Z" + site_ids = [item["site"]["site_id"] for item in sc["sites"]] + for item in sc["sites"]: + item["site"]["power_cap_w"] = models[item["site"]["site_id"]].cap_w + + rng = np.random.default_rng([int(seed), 11]) + sub = [int(x) for x in rng.integers(0, 2**31 - 1, size=4)] + + jspec = dict(packing["jobs"]["generate"]) + total_gpus = sum(models[s].n_gpus for s in site_ids) + for key in ("duration_range_s", "checkpoint_every_range_s", "checkpoint_duration_range_s", "value_range"): + jspec[key] = tuple(jspec[key]) + jobs = generate_jobs(seed=sub[0], horizon_s=horizon, total_gpus=total_gpus, site_ids=(), **jspec) + + env_spec = copy.deepcopy(sim_scenario["environment"]["synthetic"]) + env_spec["start_hour_utc"] = start_hour + hw = packing["heatwave_draw"] + draws: dict[str, Any] = {"heatwaves": {}} + base_center = float(rng.uniform(*hw["center_h_range"])) + for k, sid in enumerate(site_ids): + center = base_center + float(hw["site_offset_h"]) * k + width = float(rng.uniform(*hw["width_h_range"])) + add_c = float(rng.uniform(*hw["add_c_range"])) + env_spec["sites"][sid]["outdoor"]["heatwaves"] = [{"center_h": center, "width_h": width, "add_c": add_c}] + draws["heatwaves"][sid] = {"center_h": center, "width_h": width, "add_c": add_c} + environment = synthetic_environment(env_spec, site_ids, horizon, sub[1]) + + evs = packing["events"] + site_racks = {item["site"]["site_id"]: [(r["rack_id"], r["n_gpus"]) for r in item["racks"]] for item in sc["sites"]} + schedule = random_schedule( + seed=sub[2], horizon_s=horizon, site_racks=site_racks, + rates_per_day=evs["rates_per_day"], precursor_probability=evs["precursor_probability"], + lead_range_s=tuple(evs["lead_range_s"]), + strength_ranges={k: tuple(v) for k, v in evs["strength_ranges"].items()}, + duration_range_s=tuple(evs["duration_range_s"]), remaining_capacity_range=tuple(evs["remaining_capacity_range"]), + derate_range=tuple(evs["derate_range"]), limit_fraction_range=tuple(evs["limit_fraction_range"]), + repair_range_s=tuple(evs["repair_range_s"]), + ) + sc["events"] = [e.to_dict() for e in schedule.events] + draws["n_events"] = len(schedule.events) + draws["sim_seed"] = sub[3] + hour0 = tuple( + float(env_spec.get("start_hour_utc", 0.0)) + float(env_spec["sites"][sid].get("utc_offset_h", 0.0)) for sid in site_ids + ) + return Episode(int(seed), sc, list(jobs), environment, dict(models), hour0, draws) + + +def run_episode(episode: Episode, controller: Any): + """Run one episode in the simulator (simulated data). Returns the ``SimResult``.""" + return run_scenario( + episode.scenario, controller=controller, seed=int(episode.draws["sim_seed"]), + environment=episode.environment, jobs=episode.jobs, + ) + + +# --------------------------------------------------------------------------- +# Q-choice policies +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class Situation: + """What a policy sees when it must pick a quantile level for one candidate.""" + + features: np.ndarray # (N_FEATURES,) + slack: np.ndarray # (N_LEVELS,) relative headroom if admitted at each level (>= 0 means it fits) + value_per_gpu_hour: float + penalty_usd_per_kwh: float + + +class FixedQ: + def __init__(self, q: float) -> None: + self.index = level_index(q) + self.q = float(q) + self.name = f"fixed-q{q:g}" + + def choose(self, situation: Situation) -> int: + return self.index + + +class RandomQ: + """Uniform level per situation (exploration policy for the training episodes).""" + + name = "random-q" + + def __init__(self, seed: int) -> None: + self._rng = np.random.default_rng([int(seed), 77]) + + def choose(self, situation: Situation) -> int: + return int(self._rng.integers(N_LEVELS)) + + +class LearnedQ: + def __init__(self, model: "CostSensitiveModel", name: str = "learned-q") -> None: + self.model = model + self.name = name + + def choose(self, situation: Situation) -> int: + return int(self.model.choose(situation.features)) + + +# --------------------------------------------------------------------------- +# Controller +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class PackingSettings: + decision_interval_s: float + bin_s: float + horizon_bins: int + context_bins: int + min_context_bins: int + warmup_s: float + call_s: float + reward_window_s: float + queue_order: str + horizon_s: float + reactive_margin: float + warm_ratio_bins: int + + @classmethod + def from_scenario(cls, packing: Mapping[str, Any]) -> "PackingSettings": + ep = packing["episode"] + return cls( + decision_interval_s=float(ep["decision_interval_s"]), bin_s=float(ep["forecast_bin_s"]), + horizon_bins=int(ep["forecast_horizon_bins"]), context_bins=int(ep["context_bins"]), + min_context_bins=int(ep["min_context_bins"]), warmup_s=float(ep["warmup_s"]), + call_s=float(ep["controller_interval_s"]), reward_window_s=float(packing["reward_window_s"]), + queue_order=str(packing["queue_order"]), horizon_s=float(ep["horizon_s"]), + reactive_margin=float(packing["reactive"]["margin"]), warm_ratio_bins=int(ep["warm_ratio_bins"]), + ) + + +class PackingController: + """Admission controller for the e003 simulator. + + ``mode`` is ``'forecast'`` (a forecaster plus a q policy), ``'oracle'`` + (``oracle_ratio`` supplies the realized ratio path, shape ``(S, n_bins)``), + or ``'reactive'`` (measured power, throttling). All jobs are held back + with ``DeferJob`` the first time they are seen and released with + ``AdmitJob``. Every candidate evaluation before ``warmup_s`` uses the same + warm-up rule in every forecast mode: a flat ratio equal to the largest of + the last ``warm_ratio_bins`` observed ratio bins (1.0 before any). + """ + + def __init__( + self, + jobs: Sequence[Job], + models: Mapping[str, SiteModel], + site_ids: Sequence[str], + settings: PackingSettings, + hour0_local: Sequence[float], + mode: str = "forecast", + forecaster: Any = None, + policy: Any = None, + penalty_usd_per_kwh: float = 10.0, + use_covariates: bool = False, + oracle_ratio: Optional[np.ndarray] = None, + log_situations: bool = False, + ) -> None: + if mode not in ("forecast", "oracle", "reactive"): + raise ValueError("mode must be 'forecast', 'oracle' or 'reactive'") + if mode == "forecast" and (forecaster is None or policy is None): + raise ValueError("forecast mode needs a forecaster and a policy") + if mode == "oracle" and oracle_ratio is None: + raise ValueError("oracle mode needs oracle_ratio") + self.cfg = settings + self.mode = mode + self.forecaster = forecaster + self.policy = policy + self.penalty = float(penalty_usd_per_kwh) + self.use_covariates = bool(use_covariates) + self.oracle_ratio = None if oracle_ratio is None else np.asarray(oracle_ratio, dtype=float) + self.log_situations = bool(log_situations) + self.site_ids = tuple(site_ids) + self.S = len(self.site_ids) + self.models = [models[s] for s in self.site_ids] + self.idle = np.array([m.idle_w for m in self.models]) + self.beta = np.array([m.beta_w_per_gpu for m in self.models]) + self.cap = np.array([m.cap_w for m in self.models]) + self.hour0 = np.asarray(hour0_local, dtype=float) + self.job_by_id = {j.job_id: j for j in jobs} + self._known: set[str] = set() + self._admitted: set[str] = set() + self._waiting: set[str] = set() + self._reactive = ReactiveSiteCapController(settings.reactive_margin) if mode == "reactive" else None + # per-minute records: minute m covers [m*call_s, (m+1)*call_s) + self._p_min: list[np.ndarray] = [] + self._b_min: list[np.ndarray] = [] + self._o_min: list[np.ndarray] = [] + self._busy_after_prev = np.zeros(self.S) + self._rows: list[dict[str, Any]] = [] + self.admissions: list[dict[str, Any]] = [] + self.decisions: list[dict[str, Any]] = [] + self.progress_at_warmup: dict[str, float] = {} + self.done_at_warmup: set[str] = set() + self._warm_snapshot_taken = False + self.forecast_calls = 0 + + # -- helpers --------------------------------------------------------------- + + def _busy_from_views(self, vs: VisibleState) -> np.ndarray: + busy = np.zeros(self.S) + for jv in vs.jobs: + if jv.status in ("queued", "done"): + continue + job = self.job_by_id[jv.job_id] + share = job.n_gpus / 2.0 if job.multisite else float(job.n_gpus) + for sid in jv.site_ids: + busy[self.site_ids.index(sid)] += share + return busy + + def _planned_busy(self, t: float, vs: VisibleState) -> np.ndarray: + """``(S, H)`` GPUs planned busy per bin from the running set and declared walltimes.""" + H, bs = self.cfg.horizon_bins, self.cfg.bin_s + mids = t + (np.arange(H) + 0.5) * bs + out = np.zeros((self.S, H)) + for jv in vs.jobs: + if jv.status in ("queued", "done"): + continue + job = self.job_by_id[jv.job_id] + remaining = max(job.duration_s * (1.0 - jv.progress_steps / jv.total_steps), 0.0) + active = (mids < t + remaining).astype(float) + share = job.n_gpus / 2.0 if job.multisite else float(job.n_gpus) + for sid in jv.site_ids: + out[self.site_ids.index(sid)] += share * active + return out + + def ratio_bins(self) -> np.ndarray: + """Observed ratio ``s`` in decision-interval bins, shape ``(S, n_bins)``.""" + k = int(round(self.cfg.bin_s / self.cfg.call_s)) + m = len(self._p_min) // k + if m == 0: + return np.zeros((self.S, 0)) + p = np.asarray(self._p_min[: m * k]).reshape(m, k, self.S).sum(axis=1) + b = np.asarray(self._b_min[: m * k]).reshape(m, k, self.S) + denom = (self.idle[None, None, :] + self.beta[None, None, :] * b).sum(axis=1) + return (p / denom).T + + def minute_records(self) -> tuple[np.ndarray, np.ndarray]: + """``(P, B)``: mean site power (W) and mean busy GPUs per call interval, shape ``(M, S)``.""" + return np.asarray(self._p_min), np.asarray(self._b_min) + + def _ingest(self, t: float, vs: VisibleState) -> np.ndarray: + busy_end = self._busy_from_views(vs) + if t > 0: + _, vals = vs.history("site_power_w", self.cfg.call_s) + self._p_min.append(np.asarray(vals).mean(axis=0)) + self._b_min.append(0.5 * (self._busy_after_prev + busy_end)) + hs = vs.known_future["hour_start_s"] + fc = vs.known_future["outdoor_forecast_c"] + self._o_min.append(np.array([np.interp(t - 0.5 * self.cfg.call_s, hs, fc[:, s]) for s in range(self.S)])) + return busy_end + + # -- forecast paths ----------------------------------------------------------- + + def _warm_ratio(self, bins: np.ndarray) -> np.ndarray: + if bins.shape[1] == 0: + return np.ones(self.S) + return bins[:, -self.cfg.warm_ratio_bins:].max(axis=1) + + def _ratio_paths(self, t: float, vs: VisibleState, bins: np.ndarray) -> np.ndarray: + """``(S, H, Q)`` ratio quantile paths for the next horizon.""" + H = self.cfg.horizon_bins + if self.mode == "reactive": + raise RuntimeError("reactive mode does not use ratio paths") + in_warmup = t < self.cfg.warmup_s - 1e-9 + if self.mode == "oracle" and not in_warmup: + i0 = int(round(t / self.cfg.bin_s)) + tr = self.oracle_ratio + idx = np.clip(np.arange(i0, i0 + H), 0, tr.shape[1] - 1) + return np.repeat(tr[:, idx][:, :, None], N_LEVELS, axis=2) + if in_warmup or bins.shape[1] < self.cfg.min_context_bins or self.mode == "oracle": + return np.broadcast_to(self._warm_ratio(bins)[:, None, None], (self.S, H, N_LEVELS)).copy() + ctx = bins[:, -self.cfg.context_bins:] + kwargs: dict[str, Any] = {} + if self.use_covariates: + kwargs["known_covariates"] = self._covariates(t, vs, ctx.shape[1]) + fc = self.forecaster.forecast(ctx[:, None, :], H, **kwargs) + self.forecast_calls += 1 + vals = np.asarray(fc.values, dtype=float)[:, 0, :, :] + return np.maximum(vals, 0.05) + + def _covariates(self, t: float, vs: VisibleState, n_ctx: int) -> np.ndarray: + k = int(round(self.cfg.bin_s / self.cfg.call_s)) + o = np.asarray(self._o_min) + m = o.shape[0] // k + past = o[: m * k].reshape(m, k, self.S).mean(axis=1).T[:, -n_ctx:] + hs = vs.known_future["hour_start_s"] + fc = vs.known_future["outdoor_forecast_c"] + mids = t + (np.arange(self.cfg.horizon_bins) + 0.5) * self.cfg.bin_s + future = np.stack([np.interp(mids, hs, fc[:, s]) for s in range(self.S)]) + return np.concatenate([past, future], axis=1)[:, None, :] + + # -- slack ---------------------------------------------------------------------- + + def _slack(self, sites: tuple[int, ...], shares: tuple[float, ...], active: np.ndarray, bplan: np.ndarray, + paths: np.ndarray) -> np.ndarray: + """Relative headroom at every level (min over the sites involved); negative means over the cap.""" + out = np.full(N_LEVELS, np.inf) + for j, share in zip(sites, shares): + base = self.idle[j] + self.beta[j] * (bplan[j] + share * active) + planned = base[:, None] * paths[j] + out = np.minimum(out, (self.cap[j] - planned.max(axis=0)) / self.cap[j]) + return out + + def _features(self, t: float, job: Job, sites: tuple[int, ...], shares: tuple[float, ...], active: np.ndarray, + bplan: np.ndarray, paths: np.ndarray, slack: np.ndarray) -> np.ndarray: + spread, headroom, size, trend = [], [], 0.0, [] + for j, share in zip(sites, shares): + p50 = paths[j][:, MEDIAN_INDEX] + p10, p90 = paths[j][:, level_index(0.1)], paths[j][:, level_index(0.9)] + spread.append(float(np.mean((p90 - p10) / np.maximum(p50, 1e-6)))) + cur = (self.idle[j] + self.beta[j] * bplan[j]) * p50 + headroom.append(float((self.cap[j] - cur.max()) / self.cap[j])) + size += share * self.beta[j] * float(p50[0]) / self.cap[j] + trend.append(float(p50[-1] / max(p50[0], 1e-6))) + hour = (self.hour0[sites[0]] + t / 3600.0) % 24.0 + x = [ + max(spread), job.value_per_gpu_hour, math.log(max(self.penalty, 1e-9)), min(headroom), size, + math.sin(2 * math.pi * hour / 24.0), math.cos(2 * math.pi * hour / 24.0), max(trend), + ] + [float(slack[i]) for i in SLACK_FEATURE_INDICES] + return np.asarray(x, dtype=float) + + # -- controller entry ------------------------------------------------------------- + + def decide(self, t: float, vs: VisibleState) -> Sequence[Any]: + actions: list[Any] = [] + busy_end = self._ingest(t, vs) + defer_until = self.cfg.horizon_s + 3600.0 + for jv in vs.jobs: + if jv.job_id not in self._known: + self._known.add(jv.job_id) + if jv.status == "queued" and jv.job_id not in self._admitted: + actions.append(DeferJob(jv.job_id, defer_until)) + self._waiting.add(jv.job_id) + for pj in vs.known_future["planned_jobs"]: + if pj["job_id"] not in self._known: + self._known.add(pj["job_id"]) + actions.append(DeferJob(pj["job_id"], defer_until)) + self._waiting.add(pj["job_id"]) + if not self._warm_snapshot_taken and t >= self.cfg.warmup_s - 1e-9: + self._warm_snapshot_taken = True + live = {jv.job_id for jv in vs.jobs} + for jv in vs.jobs: + self.progress_at_warmup[jv.job_id] = jv.progress_steps / jv.total_steps + self.done_at_warmup = { + jid for jid in self._admitted if jid not in live and self.job_by_id[jid].submit_s <= t + } + if self._reactive is not None: + actions.extend(self._reactive.decide(t, vs)) + added = np.zeros(self.S) + if abs(t / self.cfg.decision_interval_s - round(t / self.cfg.decision_interval_s)) < 1e-9: + added = self._admission_round(t, vs, actions) + self._busy_after_prev = busy_end + added + return actions + + def _admission_round(self, t: float, vs: VisibleState, actions: list[Any]) -> np.ndarray: + cfg = self.cfg + bins = self.ratio_bins() + H, bs = cfg.horizon_bins, cfg.bin_s + free = np.asarray(vs.now["free_gpus"], dtype=float).copy() + added_busy = np.zeros(self.S) + views = {jv.job_id: jv for jv in vs.jobs} + cands = [ + self.job_by_id[jid] for jid in self._waiting + if jid in views and views[jid].status == "queued" and jid not in self._admitted + ] + if cfg.queue_order == "value_density": + cands.sort(key=lambda j: (-j.value_per_gpu_hour, j.submit_s, j.job_id)) + else: + cands.sort(key=lambda j: (j.submit_s, j.job_id)) + if self.mode == "reactive": + bplan = np.repeat(self._busy_from_views(vs)[:, None], H, axis=1) + if bins.shape[1]: + s_now = bins[:, -1] + p_meas = np.asarray(self._p_min[-int(round(bs / cfg.call_s)):]).mean(axis=0) + else: + s_now, p_meas = np.ones(self.S), self.idle.copy() + paths = None + else: + bplan = self._planned_busy(t, vs) + paths = self._ratio_paths(t, vs, bins) + s_now = p_meas = None + added_w = np.zeros(self.S) + refused_with_gpus = 0 + evaluated = 0 + in_eval = (t >= cfg.warmup_s - 1e-9) and self.mode in ("forecast", "oracle") + for job in cands: + n = job.n_gpus + if job.multisite: + options = [(tuple(range(self.S)), tuple([n / 2.0] * self.S))] if np.all(free >= n / 2.0) else [] + else: + options = [((j,), (float(n),)) for j in range(self.S) if free[j] >= n] + if not options: + continue + evaluated += 1 + active = ((np.arange(H) + 0.5) * bs < job.duration_s).astype(float) + slacks = [] + for sites, shares in options: + if self.mode == "reactive": + sl = np.inf + for j, share in zip(sites, shares): + planned = p_meas[j] + added_w[j] + share * self.beta[j] * s_now[j] + sl = min(sl, (self.cap[j] - planned) / self.cap[j]) + slacks.append(np.full(N_LEVELS, sl)) + else: + slacks.append(self._slack(sites, shares, active, bplan, paths)) + best = int(np.argmax([s[MEDIAN_INDEX] for s in slacks])) + sites, shares = options[best] + slack = slacks[best] + if self.mode == "reactive": + k, admit, x = MEDIAN_INDEX, bool(slack[0] >= 0.0), None + elif not in_eval: + k, admit, x = MEDIAN_INDEX, bool(slack[MEDIAN_INDEX] >= 0.0), None + else: + x = self._features(t, job, sites, shares, active, bplan, paths, slack) + if self.mode == "oracle": + k = MEDIAN_INDEX + else: + k = int(self.policy.choose(Situation(x, slack, job.value_per_gpu_hour, self.penalty))) + admit = bool(slack[k] >= 0.0) + if self.log_situations and x is not None and self.mode == "forecast": + self._rows.append({ + "t": float(t), "sites": sites, "shares": shares, "x": x, "slack": slack.copy(), "k": k, + "admit": admit, "value": job.value_per_gpu_hour, "n_gpus": n, "life_s": job.duration_s, + }) + if not admit: + refused_with_gpus += 1 + continue + site_arg = self.site_ids[sites[0]] if not job.multisite else None + actions.append(AdmitJob(job.job_id, site_arg)) + self._admitted.add(job.job_id) + self._waiting.discard(job.job_id) + self.admissions.append({"t_s": float(t), "job_id": job.job_id, "sites": [self.site_ids[j] for j in sites], "q_index": int(k)}) + for j, share in zip(sites, shares): + free[j] -= share + bplan[j] += share * active + added_busy[j] += share + added_w[j] += share * self.beta[j] * (s_now[j] if s_now is not None else 1.0) + self.decisions.append({"t_s": float(t), "refused_with_gpus": refused_with_gpus, "evaluated": evaluated, + "waiting": len(self._waiting)}) + return added_busy + + def situations(self) -> dict[str, np.ndarray]: + """Logged candidate situations as arrays (only when ``log_situations`` was set).""" + rows = self._rows + n = len(rows) + sites = np.full((n, self.S), -1, dtype=int) + shares = np.zeros((n, self.S)) + for i, r in enumerate(rows): + for j, sh in zip(r["sites"], r["shares"]): + sites[i, j], shares[i, j] = j, sh + return { + "t": np.array([r["t"] for r in rows]), + "sites": sites, "shares": shares, + "x": np.array([r["x"] for r in rows]).reshape(n, N_FEATURES), + "slack": np.array([r["slack"] for r in rows]).reshape(n, N_LEVELS), + "k": np.array([r["k"] for r in rows], dtype=int), + "admit": np.array([r["admit"] for r in rows], dtype=bool), + "value": np.array([r["value"] for r in rows]), + "n_gpus": np.array([r["n_gpus"] for r in rows], dtype=float), + "life_s": np.array([r["life_s"] for r in rows]), + } + + +# --------------------------------------------------------------------------- +# Metrics +# --------------------------------------------------------------------------- + + +def episode_metrics(result: Any, controller: PackingController, episode: Episode, settings: PackingSettings) -> dict[str, Any]: + """Metrics over the evaluation window ``[warmup_s, horizon_s)`` (simulated).""" + dt = float(result.dt) + power = np.asarray(result.site_power_w) + n0 = int(round(settings.warmup_s / dt)) + w = power[n0:] + cap = controller.cap + over = np.maximum(w - cap[None, :], 0.0) + per_int = int(round(settings.decision_interval_s / dt)) + n_int = w.shape[0] // per_int + any_over = (over[: n_int * per_int].reshape(n_int, per_int, -1) > 0).any(axis=(1, 2)) + site_int_over = (over[: n_int * per_int].reshape(n_int, per_int, -1) > 0).any(axis=1) + head = np.maximum(cap[None, :] - w, 0.0) + dec = [d for d in controller.decisions if d["t_s"] >= settings.warmup_s - 1e-9] + stranded_kwh = 0.0 + stranded_intervals = 0 + for i, d in enumerate(dec[:n_int]): + if d["refused_with_gpus"] > 0: + seg = head[i * per_int:(i + 1) * per_int] + stranded_kwh += float(seg.sum() * dt / 3.6e6) + stranded_intervals += 1 + rows = result.jobs_table + gpu_h = value = 0.0 + gpu_h_multi = 0.0 + completed = 0 + for r in rows: + job = controller.job_by_id[r["job_id"]] + if r["job_id"] in controller.done_at_warmup: + continue + start_frac = controller.progress_at_warmup.get(r["job_id"], 0.0) + gross_start = job.n_gpus * job.duration_s / 3600.0 * start_frac + delta = r["useful_gpu_hours"] - gross_start + gpu_h += delta + value += delta * job.value_per_gpu_hour + if r["multisite"]: + gpu_h_multi += delta + if r["status"] == "done": + completed += 1 + admitted_in_window = sum(1 for a in controller.admissions if a["t_s"] >= settings.warmup_s - 1e-9) + energy_over = float(over.sum() * dt / 3.6e6) + out = { + "data_kind": "simulated", + "window_s": [settings.warmup_s, settings.horizon_s], + "gpu_hours": float(gpu_h), "gpu_hours_multisite_jobs": float(gpu_h_multi), + "value_usd": float(value), + "jobs_admitted": int(admitted_in_window), "jobs_completed": int(completed), + "violation_rate": float(any_over.mean()) if n_int else 0.0, + "violation_rate_by_site": [float(x) for x in site_int_over.mean(axis=0)], + "energy_above_cap_kwh": energy_over, + "energy_above_cap_kwh_by_site": [float(x) for x in over.sum(axis=0) * dt / 3.6e6], + "seconds_above_cap": float((over > 0).sum() * dt), + "max_excess_w": float(over.max()) if over.size else 0.0, + "stranded_kwh": stranded_kwh, "stranded_intervals": int(stranded_intervals), + "mean_power_over_cap": float((w / cap[None, :]).mean()), + "energy_kwh": float(w.sum() * dt / 3.6e6), + "forecast_calls": int(controller.forecast_calls), + "job_faults": int(sum(r["faults"] for r in rows)), + } + return out + + +def net_value(metrics: Mapping[str, Any], penalty_usd_per_kwh: float) -> float: + """Value delivered minus the over-cap penalty for one episode.""" + return float(metrics["value_usd"]) - float(penalty_usd_per_kwh) * float(metrics["energy_above_cap_kwh"]) + + +def bootstrap_ci(values: Sequence[float], n_boot: int = 2000, seed: int = 0, level: float = 0.95) -> dict[str, float]: + """Percentile bootstrap interval of the mean over episodes.""" + v = np.asarray(values, dtype=float) + if v.size == 0: + raise ValueError("no values") + rng = np.random.default_rng([int(seed), 5]) + idx = rng.integers(0, v.size, size=(n_boot, v.size)) + means = v[idx].mean(axis=1) + a = (1.0 - level) / 2.0 + return {"mean": float(v.mean()), "lo": float(np.quantile(means, a)), "hi": float(np.quantile(means, 1 - a)), "n": int(v.size)} + + +# --------------------------------------------------------------------------- +# Hindsight costs and the learned model +# --------------------------------------------------------------------------- + + +def hindsight_costs( + sit: Mapping[str, np.ndarray], + p_min: np.ndarray, + b_min: np.ndarray, + models: Sequence[SiteModel], + penalties: Sequence[float], + call_s: float, + window_s: float, +) -> tuple[np.ndarray, np.ndarray]: + """Per-level costs of every logged situation under each penalty, from the logged realized path. + + Returns ``(costs, value)`` with ``costs`` shaped ``(len(penalties), N, N_LEVELS)`` + (regret in USD against the best level) and ``value`` shaped ``(N,)`` (the + candidate's value over the window). Surrogate: the candidate's power is + added to or removed from the logged realized site power as + ``beta * n_gpus * s_real(m)`` while it is alive, so later decisions are not + re-played. Situations whose window runs past the logged record are given + zero cost for every level (they carry no signal). + """ + n = sit["t"].shape[0] + S = p_min.shape[1] + idle = np.array([m.idle_w for m in models]) + beta = np.array([m.beta_w_per_gpu for m in models]) + cap = np.array([m.cap_w for m in models]) + s_real = p_min / (idle[None, :] + beta[None, :] * b_min) + W = int(round(window_s / call_s)) + m0 = np.round(sit["t"] / call_s).astype(int) + idx = m0[:, None] + np.arange(W)[None, :] + valid = idx[:, -1] < p_min.shape[0] + idx = np.clip(idx, 0, p_min.shape[0] - 1) + alive = ((np.arange(W)[None, :] + 0.5) * call_s < sit["life_s"][:, None]).astype(float) + dlog = sit["admit"].astype(float) + value = sit["n_gpus"] * sit["value"] * np.minimum(window_s, sit["life_s"]) / 3600.0 + excess = np.zeros((2, n)) + for d in (0.0, 1.0): + total = np.zeros(n) + for j in range(S): + share = sit["shares"][:, j] + p = p_min[idx, j] + s_real[idx, j] * beta[j] * share[:, None] * (d - dlog)[:, None] * alive + total += (np.maximum(p - cap[j], 0.0) * call_s / 3.6e6).sum(axis=1) + excess[int(d)] = total + costs = np.zeros((len(penalties), n, N_LEVELS)) + fits = sit["slack"] >= 0.0 + for pi, lam in enumerate(penalties): + r0 = -lam * excess[0] + r1 = value - lam * excess[1] + reward = np.where(fits, r1[:, None], r0[:, None]) + costs[pi] = reward.max(axis=1, keepdims=True) - reward + costs[pi][~valid] = 0.0 + return costs, value + + +class CostSensitiveModel: + """Random-Fourier-feature ridge regression of per-level cost; the policy picks the level with least predicted cost. + + Inputs are standardized with the training mean and standard deviation. + The regression goal is the cost divided by the candidate's value (so the + scale is the same across jobs), weighted by that value. numpy only. + """ + + def __init__(self, n_features: int = N_FEATURES, n_levels: int = N_LEVELS, n_rff: int = 256, + bandwidth: float = 1.0, ridge: float = 1.0, seed: int = 0) -> None: + self.n_features, self.n_levels, self.n_rff = n_features, n_levels, n_rff + self.bandwidth, self.ridge, self.seed = float(bandwidth), float(ridge), int(seed) + rng = np.random.default_rng([self.seed, 31]) + self.W = rng.normal(0.0, 1.0 / self.bandwidth, size=(n_features, n_rff)) + self.b = rng.uniform(0.0, 2 * np.pi, size=n_rff) + self.mean = np.zeros(n_features) + self.std = np.ones(n_features) + self.theta: Optional[np.ndarray] = None + + def _phi(self, X: np.ndarray) -> np.ndarray: + Z = (np.atleast_2d(X) - self.mean) / self.std + rff = math.sqrt(2.0 / self.n_rff) * np.cos(Z @ self.W + self.b) + return np.concatenate([np.ones((Z.shape[0], 1)), Z, rff], axis=1) + + def fit(self, X: np.ndarray, costs: np.ndarray, value: np.ndarray) -> "CostSensitiveModel": + X = np.asarray(X, dtype=float) + self.mean = X.mean(axis=0) + self.std = np.where(X.std(axis=0) > 1e-12, X.std(axis=0), 1.0) + scale = np.maximum(value, 1e-9) + Y = costs / scale[:, None] + wgt = scale / scale.mean() + d = 1 + self.n_features + self.n_rff + A = np.zeros((d, d)) + B = np.zeros((d, self.n_levels)) + for lo in range(0, X.shape[0], 20000): + P = self._phi(X[lo:lo + 20000]) + w = wgt[lo:lo + 20000, None] + A += (P * w).T @ P + B += (P * w).T @ Y[lo:lo + 20000] + self.theta = np.linalg.solve(A + self.ridge * np.eye(d), B) + return self + + def predict_costs(self, X: np.ndarray) -> np.ndarray: + if self.theta is None: + raise RuntimeError("model is not fitted") + return self._phi(X) @ self.theta + + def choose(self, x: np.ndarray) -> int: + return int(np.argmin(self.predict_costs(x)[0])) + + def regret(self, X: np.ndarray, costs: np.ndarray, value: np.ndarray) -> float: + """Mean over situations of the cost of the chosen level, divided by the total value.""" + pick = np.argmin(self.predict_costs(X), axis=1) + chosen = costs[np.arange(costs.shape[0]), pick] + return float(chosen.sum() / max(value.sum(), 1e-9)) + + def to_dict(self) -> dict[str, Any]: + assert self.theta is not None + return { + "n_rff": self.n_rff, "bandwidth": self.bandwidth, "ridge": self.ridge, "seed": self.seed, + "mean": self.mean.tolist(), "std": self.std.tolist(), "theta": self.theta.tolist(), + } + + @classmethod + def from_dict(cls, d: Mapping[str, Any]) -> "CostSensitiveModel": + m = cls(n_rff=int(d["n_rff"]), bandwidth=float(d["bandwidth"]), ridge=float(d["ridge"]), seed=int(d["seed"])) + m.mean, m.std, m.theta = np.array(d["mean"]), np.array(d["std"]), np.array(d["theta"]) + return m + + +def sha256_bytes(data: bytes) -> str: + return hashlib.sha256(data).hexdigest() + + +__all__ = [ + "CostSensitiveModel", "Episode", "FEATURE_NAMES", "FixedQ", "LearnedQ", "MEDIAN_INDEX", "N_FEATURES", "N_LEVELS", + "PackingController", "PackingSettings", "QUANTILE_LEVELS", "RandomQ", "SiteModel", "Situation", + "bootstrap_ci", "build_episode", "calibrate_sites", "episode_metrics", "hindsight_costs", "level_index", + "load_packing_scenario", "net_value", "run_episode", +] diff --git a/tests/test_research_heatwave_packing.py b/tests/test_research_heatwave_packing.py new file mode 100644 index 0000000..f8d8850 --- /dev/null +++ b/tests/test_research_heatwave_packing.py @@ -0,0 +1,266 @@ +"""Tests for the e003 power-cap packing module (all simulated, short horizons).""" + +from __future__ import annotations + +import copy +import json +from pathlib import Path +from types import SimpleNamespace + +import numpy as np +import pytest + +from gpu_stack.research import heatwave_packing as hp +from gpu_stack.research.heatwave_sim import load_scenario + +ROOT = Path(__file__).resolve().parents[1] +E003 = ROOT / "experiments" / "e003-heatwave" + + +class StubForecaster: + """Persistence of the last ratio with a symmetric spread that grows with the level distance from the median.""" + + name = "stub" + + def __init__(self, spread: float = 0.1) -> None: + self.spread = spread + + def forecast(self, context, horizon, known_covariates=None, past_covariates=None): + last = np.asarray(context)[:, 0, -1] + levels = np.asarray(hp.QUANTILE_LEVELS) + vals = last[:, None, None, None] * (1.0 + self.spread * (levels - 0.5))[None, None, None, :] + return SimpleNamespace(values=np.broadcast_to(vals, (last.shape[0], 1, horizon, levels.size)).copy()) + + +@pytest.fixture(scope="module") +def tiny(): + packing = hp.load_packing_scenario(E003 / "packing-scenario-v1.json") + packing = copy.deepcopy(packing) + packing["episode"].update({"horizon_s": 10800, "warmup_s": 3600, "min_context_bins": 6, "warm_ratio_bins": 6}) + packing["cap"]["calibration"] = {"idle_hours": 1, "full_hours": 1} + packing["jobs"]["generate"].update({"duration_range_s": [1800, 7200], "utilization_goal": 1.5}) + sim = load_scenario(E003 / "sim-scenario-v1.json") + cal = hp.calibrate_sites(sim, packing) + return packing, sim, cal + + +def make_controller(tiny, ep, mode="forecast", q=0.5, **kw): + packing, sim, cal = tiny + settings = hp.PackingSettings.from_scenario(packing) + site_ids = list(cal["models"]) + if mode == "forecast": + kw.setdefault("forecaster", StubForecaster()) + kw.setdefault("policy", hp.FixedQ(q)) + return hp.PackingController(ep.jobs, cal["models"], site_ids, settings, ep.hour0_local, mode=mode, **kw), settings + + +def test_levels_and_feature_names(): + assert len(hp.QUANTILE_LEVELS) == hp.N_LEVELS == 21 + assert hp.QUANTILE_LEVELS[hp.MEDIAN_INDEX] == 0.5 + assert hp.level_index(0.9) == 18 + with pytest.raises(ValueError): + hp.level_index(0.42) + assert len(hp.FEATURE_NAMES) == hp.N_FEATURES + assert "log_penalty" in hp.FEATURE_NAMES and "value_per_gpu_hour" in hp.FEATURE_NAMES + + +def test_scenario_file_loads_and_rejects_bad_schema(tmp_path): + sc = hp.load_packing_scenario(E003 / "packing-scenario-v1.json") + assert sc["data_kind"] == "simulated" + bad = dict(sc, schema="nope") + p = tmp_path / "bad.json" + p.write_text(json.JSONEncoder().encode(bad)) + with pytest.raises(ValueError): + hp.load_packing_scenario(p) + + +def test_calibration_is_affine_and_caps_sit_between_idle_and_full(tiny): + _, _, cal = tiny + for sid, m in cal["models"].items(): + assert m.full_w > m.idle_w > 0 + assert m.idle_w < m.cap_w < m.full_w + assert m.beta_w_per_gpu == pytest.approx((m.full_w - m.idle_w) / m.n_gpus) + for row in cal["half_load_check"]: + assert abs(row["relative_error"]) < 0.01 + + +def test_build_episode_is_seeded_and_runs(tiny): + packing, sim, cal = tiny + a = hp.build_episode(packing, sim, cal["models"], 3) + b = hp.build_episode(packing, sim, cal["models"], 3) + c = hp.build_episode(packing, sim, cal["models"], 4) + assert [j.to_dict() for j in a.jobs] == [j.to_dict() for j in b.jobs] + assert [j.to_dict() for j in a.jobs] != [j.to_dict() for j in c.jobs] + assert all(j.flexible and j.site_id is None and j.start_s == j.submit_s for j in a.jobs) + for item in a.scenario["sites"]: + assert item["site"]["power_cap_w"] == pytest.approx(cal["models"][item["site"]["site_id"]].cap_w) + assert a.scenario["data_kind"] == "simulated" + + +def test_slack_decreases_with_level_and_with_extra_jobs(tiny): + packing, sim, cal = tiny + ep = hp.build_episode(packing, sim, cal["models"], 1) + ctrl, st = make_controller(tiny, ep) + H = st.horizon_bins + levels = np.asarray(hp.QUANTILE_LEVELS) + paths = np.broadcast_to((1.0 + 0.1 * (levels - 0.5))[None, None, :], (2, H, 21)).copy() + bplan = np.zeros((2, H)) + active = np.ones(H) + s_small = ctrl._slack((0,), (8.0,), active, bplan, paths) + s_big = ctrl._slack((0,), (64.0,), active, bplan, paths) + assert np.all(np.diff(s_small) <= 1e-12) + assert np.all(s_big <= s_small + 1e-12) + # a job that ends after the first bin only counts in that bin + short = np.zeros(H) + short[0] = 1.0 + assert np.all(ctrl._slack((0,), (64.0,), short, bplan, paths) >= s_big - 1e-12) + # two sites: slack is the minimum over the sites + both = ctrl._slack((0, 1), (32.0, 32.0), active, bplan, paths) + one = [ctrl._slack((j,), (32.0,), active, bplan, paths) for j in (0, 1)] + assert np.allclose(both, np.minimum(one[0], one[1])) + + +def test_jobs_wait_for_admission_and_higher_q_admits_no_more(tiny): + packing, sim, cal = tiny + ep = hp.build_episode(packing, sim, cal["models"], 5) + out = {} + for q in (0.5, 0.99): + ctrl, st = make_controller(tiny, ep, q=q) + res = hp.run_episode(ep, ctrl) + starts = {e["job_id"]: e["t_s"] for e in res.events if e["kind"] == "job_start"} + admitted_at = {a["job_id"]: a["t_s"] for a in ctrl.admissions} + # no job starts unless the controller admitted it, and never before the admission time + assert set(starts) <= set(admitted_at) + assert all(starts[j] >= admitted_at[j] - 1e-9 for j in starts) + out[q] = (len(ctrl.admissions), hp.episode_metrics(res, ctrl, ep, st)) + # before the warm-up ends both runs use the same rule + assert out[0.5][1]["data_kind"] == "simulated" + assert out[0.5][0] >= out[0.99][0] + assert out[0.5][1]["gpu_hours"] >= out[0.99][1]["gpu_hours"] - 1e-6 + + +def test_warmup_decisions_do_not_depend_on_the_policy(tiny): + packing, sim, cal = tiny + ep = hp.build_episode(packing, sim, cal["models"], 6) + logs = [] + for q in (0.1, 0.9): + ctrl, st = make_controller(tiny, ep, q=q) + hp.run_episode(ep, ctrl) + logs.append([(a["t_s"], a["job_id"]) for a in ctrl.admissions if a["t_s"] < st.warmup_s]) + assert logs[0] == logs[1] and len(logs[0]) > 0 + + +def test_metrics_window_and_ranges(tiny): + packing, sim, cal = tiny + ep = hp.build_episode(packing, sim, cal["models"], 7) + ctrl, st = make_controller(tiny, ep, q=0.9) + res = hp.run_episode(ep, ctrl) + m = hp.episode_metrics(res, ctrl, ep, st) + assert 0.0 <= m["violation_rate"] <= 1.0 + assert m["energy_above_cap_kwh"] >= 0.0 and m["seconds_above_cap"] >= 0.0 + assert m["stranded_kwh"] >= 0.0 + assert m["window_s"] == [st.warmup_s, st.horizon_s] + assert hp.net_value(m, 0.0) == pytest.approx(m["value_usd"]) + assert hp.net_value(m, 5.0) == pytest.approx(m["value_usd"] - 5.0 * m["energy_above_cap_kwh"]) + + +def test_reactive_controller_throttles_when_over_cap(tiny): + packing, sim, cal = tiny + low = copy.deepcopy(packing) + low["cap"]["load_fraction"] = 0.05 + models = { + sid: hp.SiteModel(m.site_id, m.n_gpus, m.idle_w, m.full_w, m.idle_w + 0.05 * (m.full_w - m.idle_w), m.utc_offset_h) + for sid, m in cal["models"].items() + } + ep = hp.build_episode(low, sim, models, 8) + ctrl, st = make_controller(tiny, ep, mode="reactive") + ctrl.cap = np.array([m.cap_w for m in models.values()]) + res = hp.run_episode(ep, ctrl) + names = [a["action"] for a in res.actions] + assert "SetRackPowerCap" in names + assert "AdmitJob" in names + + +def test_oracle_follows_the_reference_ratio(tiny): + packing, sim, cal = tiny + ep = hp.build_episode(packing, sim, cal["models"], 9) + ref, st = make_controller(tiny, ep, q=0.9) + hp.run_episode(ep, ref) + trace = ref.ratio_bins() + assert trace.shape[0] == 2 and trace.shape[1] > 0 + ctrl, _ = make_controller(tiny, ep, mode="oracle", oracle_ratio=trace) + res = hp.run_episode(ep, ctrl) + m = hp.episode_metrics(res, ctrl, ep, st) + assert m["forecast_calls"] == 0 + assert len(ctrl.admissions) > 0 + + +def test_ratio_bins_match_the_definition(tiny): + packing, sim, cal = tiny + ep = hp.build_episode(packing, sim, cal["models"], 10) + ctrl, st = make_controller(tiny, ep, q=0.9) + res = hp.run_episode(ep, ctrl) + p, b = ctrl.minute_records() + bins = ctrl.ratio_bins() + k = int(st.bin_s / st.call_s) + i = 3 + expect = p[i * k:(i + 1) * k, 0].sum() / (ctrl.idle[0] + ctrl.beta[0] * b[i * k:(i + 1) * k, 0]).sum() + assert bins[0, i] == pytest.approx(expect) + # the recorded minute means equal the simulator's 10 s samples averaged over the minute + m0 = res.site_power_w[:6, 0].mean() + assert p[0, 0] == pytest.approx(m0) + + +def test_hindsight_costs_hand_example(): + models = [hp.SiteModel("s", 10, 1000.0, 2000.0, 1500.0, 0.0)] # beta = 100 W per GPU + M = 70 + p_min = np.full((M, 1), 1450.0) + b_min = np.full((M, 1), 5.0) # idle + beta*b = 1500, s_real = 1450/1500 + t = np.array([0.0]) + # a candidate of 10 GPUs that was NOT admitted: admitting adds 10 * 100 * (1450/1500) = 966.67 W + sit = { + "t": t, "sites": np.array([[0]]), "shares": np.array([[10.0]]), "x": np.zeros((1, hp.N_FEATURES)), + "slack": np.array([np.linspace(0.1, -0.1, hp.N_LEVELS)]), "k": np.array([0]), "admit": np.array([False]), + "value": np.array([2.0]), "n_gpus": np.array([10.0]), "life_s": np.array([7200.0]), + } + costs, value = hp.hindsight_costs(sit, p_min, b_min, models, [0.0, 10.0], 60.0, 3600.0) + assert value[0] == pytest.approx(10 * 2.0 * 1.0) + fits = sit["slack"][0] >= 0 + # no penalty: admitting is best, so refusing levels cost the value + assert np.allclose(costs[0][0][fits], 0.0) + assert np.allclose(costs[0][0][~fits], 20.0) + # excess energy if admitted: (1450 + 966.67 - 1500) W for one hour = 0.91667 kWh; penalty 10 => 9.1667 USD + excess_kwh = (1450.0 + 100 * 10 * 1450.0 / 1500.0 - 1500.0) / 1000.0 + assert costs[1][0][fits][0] == pytest.approx(0.0 + 0.0) # admit reward 20 - 9.17 > refuse reward 0 + assert costs[1][0][~fits][0] == pytest.approx(20.0 - 10.0 * excess_kwh) + # a very large penalty flips the best action to refusing + c3, _ = hp.hindsight_costs(sit, p_min, b_min, models, [100.0], 60.0, 3600.0) + assert np.allclose(c3[0][0][~fits], 0.0) + assert np.allclose(c3[0][0][fits], 100.0 * excess_kwh - 20.0) + + +def test_cost_sensitive_model_learns_a_threshold_rule(): + rng = np.random.default_rng(0) + n = 3000 + X = rng.normal(size=(n, hp.N_FEATURES)) + best = (X[:, 0] > 0).astype(int) * 10 + 5 # level 15 when the first feature is positive, else level 5 + costs = np.abs(np.arange(hp.N_LEVELS)[None, :] - best[:, None]).astype(float) + value = np.ones(n) + model = hp.CostSensitiveModel(n_rff=128, bandwidth=1.0, ridge=1.0).fit(X[:2000], costs[:2000], value[:2000]) + pick = np.argmin(model.predict_costs(X[2000:]), axis=1) + assert np.mean(np.abs(pick - best[2000:]) <= 2) > 0.9 + again = hp.CostSensitiveModel.from_dict(model.to_dict()) + assert np.allclose(again.predict_costs(X[2000:2010]), model.predict_costs(X[2000:2010])) + assert model.regret(X[2000:], costs[2000:], value[2000:]) < 1.0 + + +def test_bootstrap_ci(): + v = [1.0, 2.0, 3.0, 4.0] + a = hp.bootstrap_ci(v, n_boot=500, seed=1) + b = hp.bootstrap_ci(v, n_boot=500, seed=1) + assert a == b + assert a["mean"] == pytest.approx(2.5) and a["lo"] <= 2.5 <= a["hi"] and a["n"] == 4 + one = hp.bootstrap_ci([3.0], n_boot=50) + assert one["lo"] == one["hi"] == 3.0 + with pytest.raises(ValueError): + hp.bootstrap_ci([]) From 8610b883cf3c97d0bf008f27f9000b4c5639319b Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:10:39 -0700 Subject: [PATCH 09/52] Add forecast-driven outage-response module, scenario, runner and tests (simulated) Warning rule on 21-quantile forecasts of rack temperature, power and precursor channels, checkpoint-then-move-or-pause response, perfect-foresight upper bound, event attribution, scoring and seed-bootstrap aggregation, built on the e003 simulator with the v2 power calibration. Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- .../e003-heatwave/outage-scenario-v1.json | 174 ++ experiments/e003-heatwave/run_outage.py | 512 ++++ gpu_stack/research/heatwave_outage.py | 2102 +++++++++++++++++ tests/test_research_heatwave_outage.py | 286 +++ 4 files changed, 3074 insertions(+) create mode 100644 experiments/e003-heatwave/outage-scenario-v1.json create mode 100644 experiments/e003-heatwave/run_outage.py create mode 100644 gpu_stack/research/heatwave_outage.py create mode 100644 tests/test_research_heatwave_outage.py diff --git a/experiments/e003-heatwave/outage-scenario-v1.json b/experiments/e003-heatwave/outage-scenario-v1.json new file mode 100644 index 0000000..bbb5ddc --- /dev/null +++ b/experiments/e003-heatwave/outage-scenario-v1.json @@ -0,0 +1,174 @@ +{ + "schema": "gpu-stack.e003-heatwave-outage-scenario.v1", + "data_kind": "simulated", + "scenario_id": "e003-heatwave-outage-v1", + "description": "Outage-response experiment on the e003 two-site simulated cluster. Each seed is a 15-hour simulated window with its own synthetic job mix, synthetic weather and a randomly drawn schedule of cooling failures, breaker events, grid curtailments and hardware faults (some with precursors). Every number here is a simulation assumption, not a measurement.", + "base_scenario": "sim-scenario-v2.json", + "base_overrides": { + "horizon_s": 54000, + "base_dt_s": 60.0, + "control_dt_s": 60.0, + "controller_interval_s": 600.0, + "outputs": { + "gpu_windows": [] + }, + "environment": { + "synthetic": { + "start_hour_utc": 17.0, + "sites": { + "site-a": { + "outdoor": { + "heatwaves": [ + { + "center_h": 6.0, + "width_h": 3.0, + "add_c": 8.0 + } + ] + } + }, + "site-b": { + "outdoor": { + "heatwaves": [ + { + "center_h": 5.0, + "width_h": 3.0, + "add_c": 7.0 + } + ] + } + } + } + } + }, + "jobs": { + "generate": { + "duration_range_s": [ + 1800, + 21600 + ] + } + } + }, + "seeds": { + "train": [ + 101, + 102, + 103, + 104 + ], + "test": [ + 201, + 202, + 203, + 204, + 205, + 206, + 207, + 208, + 209, + 210 + ] + }, + "seed_streams": { + "job_seed_offset": 1000, + "environment_seed_offset": 2000, + "event_seed_offset": 3000, + "sim_seed_offset": 0 + }, + "event_generation": { + "warmup_s": 10800.0, + "tail_s": 3600.0, + "rates_per_site_per_day": { + "cooling_failure": 1.5, + "breaker_trip": 1.5, + "grid_curtailment": 1.5, + "hardware_fault": 3.0 + }, + "precursor_probability": 0.5, + "lead_range_s": [ + 1800.0, + 5400.0 + ], + "strength_ranges": { + "supply_temp_c": [ + 2.0, + 4.0 + ], + "gpu_temp_c": [ + 4.0, + 8.0 + ] + }, + "duration_range_s": [ + 1800.0, + 5400.0 + ], + "remaining_capacity_range": [ + 0.15, + 0.5 + ], + "derate_range": [ + 0.5, + 0.7 + ], + "limit_fraction_range": [ + 0.4, + 0.7 + ], + "repair_range_s": [ + 3600.0, + 14400.0 + ] + }, + "outage": { + "decision_interval_s": 600.0, + "context_steps": 18, + "horizon_steps": 6, + "clear_decisions": 2, + "recheckpoint_s": 1200.0, + "max_pause_s": 7200.0, + "calibration_quantile": 1.0, + "anomaly_baseline_window_s": 14400.0, + "anomaly_baseline_gap_s": 2700.0, + "anomaly_smooth_steps": 2, + "curtail_shed_margin": 0.02, + "oracle_extra_lead_steps": 2, + "episode_merge_gap_s": 1200.0, + "threshold_grid": [ + 0.05, + 0.1, + 0.2, + 0.3, + 0.5, + 0.7, + 0.9 + ], + "observed_threshold": 0.5, + "classical": { + "seasonal_naive_season_length": 1, + "ets_season_length": 1, + "arima_order": [ + 2, + 1, + 1 + ] + }, + "timestral_sizes": [ + "5M", + "28M" + ], + "bootstrap_resamples": 2000 + }, + "assumptions": [ + "Everything in this file is a simulation setting. No number here is a measurement of a real cluster.", + "The two-site cluster, job generator, thermal, breaker and power parameters come from sim-scenario-v2.json (not edited here). Power levels are read through the simulator at run time, so a recalibration of the simulator scenario flows through.", + "Job durations are drawn log-uniform between 1800 s and 21600 s (the base scenario uses 3600 s to 172800 s) so that a 15-hour window holds dozens of jobs; the job mix is otherwise the base scenario's, including its 10 percent of two-shard jobs that already follow the averaging rule.", + "The simulated clock starts at 17:00 UTC (late morning to midday local time); heat-wave centers and amplitudes are chosen to fall inside the 15-hour window and are simulation assumptions.", + "Event rates, precursor probability, precursor lead times and strengths, durations and severities are simulation assumptions chosen to produce several events per window; they are not rates of real facilities.", + "Controller decisions, forecasts and warnings use a 600 s interval; the simulator control step is 60 s and the base step is 60 s (the sim-scenario-v2.json steps are 10 s and 1 s).", + "Warning horizon 6 steps x 600 s = 60 min; forecast context 18 steps x 600 s = 3 h, equal to the 3 h warm-up before the first scheduled event.", + "Probability levels for the supply-temperature and GPU-temperature anomaly channels are the largest value (calibration_quantile 1.0) seen in event-free runs on training seeds. Level of the GPU temperature channel is the throttle threshold of the simulator scenario; level of the breaker load channel is 1.0 (rack power above rating); level of the site channel is the grid import limit including announced curtailments.", + "recheckpoint_s, clear_decisions, max_pause_s and oracle_extra_lead_steps are response-policy design choices that are not tuned." + ] +} diff --git a/experiments/e003-heatwave/run_outage.py b/experiments/e003-heatwave/run_outage.py new file mode 100644 index 0000000..36a7ee3 --- /dev/null +++ b/experiments/e003-heatwave/run_outage.py @@ -0,0 +1,512 @@ +"""Outage-response experiment on the e003 Heatwave simulator (all data simulated). + +Compares warning-and-response policies on the simulated two-site cluster of +``sim-scenario-v2.json`` with the settings in ``outage-scenario-v1.json``: + +* reactive (no forecast; the simulator rolls work back to the last checkpoint), +* the e001 averaging rule alone (every splittable job runs as two shards), +* observed crossing (persistence forecast, warn when the current value is past its limit), +* seasonal naive, ETS and ARIMA forecasts driving the warning rule, +* Timestral zero-shot 5M and 28M driving the warning rule, +* a fine-tuned Timestral through ``--finetuned-weights`` (not run without weights), +* perfect foresight (upper bound: reads the event times of the reactive run). + +Stages, each cached in ``--work-dir`` so an interrupted run resumes: + +``prepare`` event-free and reactive runs per seed (traces, event tables, reactive scores) +``openloop`` warning probabilities of every forecaster on the recorded reactive traces +``tune`` warning threshold per forecaster on training seeds only (replay objective) +``closed`` closed-loop runs on held-out seeds (the response actually runs in the simulator) +``report`` aggregation with seed-bootstrap intervals, written with provenance + + python experiments/e003-heatwave/run_outage.py --work-dir DIR --stage all + +``--policies`` and ``--forecasters`` restrict a stage so several processes can +share the work. Needs numpy, scipy, statsforecast and (for Timestral) torch +plus the ``timestral`` package. Provenance is written with +``heatwave.provenance`` and then pointed at this repository's git state. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import pickle +import subprocess +import sys +import time +import warnings +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[1] +sys.path.insert(0, str(ROOT)) + +from gpu_stack.research.heatwave_outage import ( # noqa: E402 + EVENT_TYPES, + ForecastWarningController, + OracleAlarm, + OracleController, + OutageSettings, + PersistenceForecaster, + SeriesIndex, + TraceRecorder, + aggregate_runs, + build_event_table, + calibrate, + detection_stats, + episodes_from_pmax, + file_sha256, + load_outage_scenario, + openloop_pmax, + oracle_alarms_from_reference, + oracle_scenario, + oracle_settings, + paired_difference, + periodic_local_jobs, + pooled_quantile_ci, + ratio_ci, + bootstrap_ci, + replay_objective, + run_policy, + score_run, + seed_scenario, + throttle_episodes, +) +from gpu_stack.research.heatwave_sim import build_config # noqa: E402 + +SCENARIO = HERE / "outage-scenario-v1.json" +BASE_SCENARIO = HERE / "sim-scenario-v2.json" +OUT = HERE / "results" / "outage-v1.json" + +FORECASTERS = ("naive", "ets", "arima", "ts5m", "ts28m", "finetuned") +CLASSICAL = ("naive", "ets", "arima") +POLICIES = ("reactive", "e001", "observed", "naive", "ets", "arima", "ts5m", "ts28m", "finetuned", "perfect_foresight") +NO_WARNING_POLICIES = ("reactive", "e001") +POLICY_LABELS = { + "reactive": "reactive (no forecast)", + "e001": "e001 averaging rule alone", + "observed": "observed crossing (persistence)", + "naive": "seasonal naive", + "ets": "ETS", + "arima": "ARIMA", + "ts5m": "Timestral 5M zero-shot", + "ts28m": "Timestral 28M zero-shot", + "finetuned": "Timestral fine-tuned", + "perfect_foresight": "perfect foresight (upper bound, reads event times)", +} + + +def log(msg: str) -> None: + print(time.strftime("%H:%M:%S"), msg, flush=True) + + +def load_pickle(path: Path): + with open(path, "rb") as f: + return pickle.load(f) + + +def save_pickle(path: Path, obj) -> None: + tmp = path.with_suffix(path.suffix + ".tmp") + with open(tmp, "wb") as f: + pickle.dump(obj, f) + tmp.replace(path) + + +def make_forecaster(name: str, outage: dict, args: argparse.Namespace): + from heatwave.forecast import ARIMAForecaster, ETSForecaster, SeasonalNaiveForecaster + + cl = outage["outage"]["classical"] + if name == "naive": + return SeasonalNaiveForecaster(int(cl["seasonal_naive_season_length"])) + if name == "ets": + return ETSForecaster(int(cl["ets_season_length"])) + if name == "arima": + return ARIMAForecaster(tuple(cl["arima_order"])) + from heatwave.forecast import TimestralForecaster + + if name == "ts5m": + return TimestralForecaster("5M", threads=args.threads) + if name == "ts28m": + return TimestralForecaster("28M", threads=args.threads) + if name == "finetuned": + return TimestralForecaster(args.finetuned_size, weights_dir=args.finetuned_weights, threads=args.threads) + raise ValueError(name) + + +def finetuned_available(args: argparse.Namespace) -> bool: + return bool(args.finetuned_weights) and Path(args.finetuned_weights).exists() + + +def seeds_for(outage: dict, which: str, args: argparse.Namespace) -> list[int]: + train = [int(s) for s in outage["seeds"]["train"]] + test = [int(s) for s in outage["seeds"]["test"]] + if args.n_test: + test = test[: args.n_test] + return {"train": train, "test": test, "all": train + test}[which] + + +# --------------------------------------------------------------------------- +# stage prepare +# --------------------------------------------------------------------------- + + +def prepare_seed(outage: dict, S: OutageSettings, seed: int, train: bool, work: Path) -> dict: + path = work / f"prep_{seed}.pkl" + if path.exists(): + return load_pickle(path) + t0 = time.time() + free = seed_scenario(outage, seed, with_events=False) + out: dict = {"seed": seed} + if train: + rec_free = TraceRecorder(S) + res_free, _ = run_policy(free, rec_free, seed) + tr = rec_free.trace() + out["ef_samples"], out["ef_times"] = tr["samples"], tr["times_s"] + else: + res_free, _ = run_policy(free, None, seed) + ref_useful = float(res_free.summary()["useful_gpu_hours"]) + sc = seed_scenario(outage, seed) + rec = TraceRecorder(S) + res, cfg = run_policy(sc, rec, seed) + trace = rec.trace() + table = build_event_table(cfg, res.events, S) + export = rec.export() + export["episodes"] = [] + score = score_run(res, cfg, table, export, S, ref_useful) + thr = throttle_episodes(res.events, cfg.horizon_s, S.episode_merge_gap_s) + alarms = oracle_alarms_from_reference(cfg, res.events, thr, oracle_settings(S, cfg.control_dt_s)) + out.update( + ref_useful=ref_useful, table=table, score=score, trace=trace, oracle_alarms=[a.__dict__ for a in alarms], + n_events=len(table), wall_s=time.time() - t0, + ) + save_pickle(path, out) + log(f"prepare seed {seed}: {len(table)} events, reactive lost {score['summary']['lost_gpu_hours']:.1f} GPU-h ({time.time() - t0:.0f} s)") + return out + + +def stage_prepare(outage, S, args, work) -> None: + for seed in seeds_for(outage, "all", args): + prepare_seed(outage, S, seed, seed in outage["seeds"]["train"], work) + + +def load_calibration(outage, S, args, work): + train = seeds_for(outage, "train", args) + preps = [prepare_seed(outage, S, s, True, work) for s in train] + cfg = build_config(seed_scenario(outage, train[0], with_events=False), seed=train[0]) + index = SeriesIndex.from_config(cfg) + return calibrate([p["ef_samples"] for p in preps], index, S, [p["ef_times"] for p in preps], "event-free runs on training seeds"), index + + +# --------------------------------------------------------------------------- +# stage openloop +# --------------------------------------------------------------------------- + + +def stage_openloop(outage, S, args, work) -> None: + cal, _ = load_calibration(outage, S, args, work) + if args.forecasters: + names = list(args.forecasters) + else: + names = [n for n in FORECASTERS if n != "finetuned" or finetuned_available(args)] + ["perfect"] + for name in names: + fc = None + for seed in seeds_for(outage, "all", args): + path = work / f"pmax_{name}_{seed}.npy" + if path.exists(): + continue + prep = prepare_seed(outage, S, seed, seed in outage["seeds"]["train"], work) + cfg = build_config(seed_scenario(outage, seed), seed=seed) + if name != "perfect" and fc is None: + fc = make_forecaster(name, outage, args) + t0 = time.time() + pm = openloop_pmax(fc, prep["trace"], cfg, S, cal, batch_size=args.batch_size, perfect=(name == "perfect")) + np.save(path, pm) + log(f"openloop {name} seed {seed}: {time.time() - t0:.0f} s") + + +# --------------------------------------------------------------------------- +# stage tune +# --------------------------------------------------------------------------- + + +def threshold_table(outage, S, args, work, name: str, seeds: list[int], index: SeriesIndex) -> dict: + """Replay objective per threshold, summed over ``seeds``.""" + rows = {} + for thr in S.threshold_grid: + miss = alarm = 0.0 + for seed in seeds: + prep = load_pickle(work / f"prep_{seed}.pkl") + pm = np.load(work / f"pmax_{name}_{seed}.npy") + eps = episodes_from_pmax(prep["trace"]["times_s"], pm, thr, S.clear_decisions, index) + eps = [e for e in eps if e["start_s"] >= 0.0] + r = replay_objective(prep["table"], prep["score"]["by_event"], eps, prep["trace"], index, S, prep["score"]["min_lead_s"]) + miss += r["miss_gpu_h"] + alarm += r["alarm_gpu_h"] + rows[f"{thr:g}"] = {"threshold": thr, "miss_gpu_h": miss, "alarm_gpu_h": alarm, "total_gpu_h": miss + alarm} + return rows + + +def choose_threshold(rows: dict) -> float: + best = min(rows.values(), key=lambda r: (round(r["total_gpu_h"], 9), -r["threshold"])) + return float(best["threshold"]) + + +def stage_tune(outage, S, args, work) -> dict: + _, index = load_calibration(outage, S, args, work) + train = seeds_for(outage, "train", args) + out = {} + names = [n for n in FORECASTERS if (work / f"pmax_{n}_{train[0]}.npy").exists()] + ["perfect"] + for name in names: + if not all((work / f"pmax_{name}_{s}.npy").exists() for s in train): + continue + rows = threshold_table(outage, S, args, work, name, train, index) + out[name] = {"train_seeds": train, "objective_by_threshold": rows, "chosen_threshold": choose_threshold(rows)} + log(f"tune {name}: chosen {out[name]['chosen_threshold']}") + (work / "tuning.json").write_text(json.JSONEncoder(indent=1, allow_nan=False).encode(out) + "\n") + return out + + +# --------------------------------------------------------------------------- +# stage closed +# --------------------------------------------------------------------------- + + +def closed_policy_seed(outage, S, cal, name: str, seed: int, work: Path, tuning: dict, args) -> dict: + path = work / f"closed_{name}_{seed}.pkl" + if path.exists(): + return load_pickle(path) + prep = prepare_seed(outage, S, seed, seed in outage["seeds"]["train"], work) + table, ref_useful = prep["table"], prep["ref_useful"] + sc = seed_scenario(outage, seed) + t0 = time.time() + if name == "reactive": + score = prep["score"] + threshold = None + elif name == "e001": + res, cfg = run_policy(sc, None, seed, job_transform=periodic_local_jobs) + score = score_run(res, cfg, table, {"policy": "e001", "episodes": [], "meta_actions": []}, S, ref_useful) + threshold = None + elif name == "perfect_foresight": + So = oracle_settings(S, float(sc["control_dt_s"])) + alarms = [OracleAlarm(**a) for a in prep["oracle_alarms"]] + ctrl = OracleController(So, alarms) + res, cfg = run_policy(oracle_scenario(sc), ctrl, seed) + score = score_run(res, cfg, table, ctrl.export(), So, ref_useful) + threshold = None + else: + if name == "observed": + fc, threshold = PersistenceForecaster(), S.observed_threshold + else: + fc, threshold = make_forecaster(name, outage, args), tuning[name]["chosen_threshold"] + ctrl = ForecastWarningController(S, fc, threshold, cal, name, batch_size=args.batch_size) + res, cfg = run_policy(sc, ctrl, seed) + score = score_run(res, cfg, table, ctrl.export(), S, ref_useful) + out = {"seed": seed, "policy": name, "threshold": threshold, "score": score, "wall_s": time.time() - t0} + save_pickle(path, out) + s = score["summary"] + log(f"closed {name} seed {seed}: lost {s['lost_gpu_hours']:.1f} GPU-h, alarms {score['response']['n_alarms']}, {out['wall_s']:.0f} s") + return out + + +def stage_closed(outage, S, args, work) -> None: + cal, _ = load_calibration(outage, S, args, work) + tuning = json.loads((work / "tuning.json").read_text()) if (work / "tuning.json").exists() else {} + pols = args.policies or [p for p in POLICIES if p != "finetuned" or finetuned_available(args)] + for name in pols: + if name in tuning or name in ("reactive", "e001", "observed", "perfect_foresight"): + for seed in seeds_for(outage, "test", args): + closed_policy_seed(outage, S, cal, name, seed, work, tuning, args) + else: + log(f"skip {name}: no tuned threshold (run openloop and tune first)") + + +# --------------------------------------------------------------------------- +# stage report +# --------------------------------------------------------------------------- + + +def sweep_rows(outage, S, args, work, name: str, seeds: list[int], index: SeriesIndex) -> dict: + B = S.bootstrap_resamples + rows = {} + for thr in S.threshold_grid: + stats = [] + costs = [] + for seed in seeds: + prep = load_pickle(work / f"prep_{seed}.pkl") + pm = np.load(work / f"pmax_{name}_{seed}.npy") + eps = episodes_from_pmax(prep["trace"]["times_s"], pm, thr, S.clear_decisions, index) + eps = [e for e in eps if e["start_s"] >= 0.0] + stats.append(detection_stats(prep["table"], eps, S, float(prep["score"]["response"]["evaluated_days"]), prep["score"]["min_lead_s"])) + costs.append(replay_objective(prep["table"], prep["score"]["by_event"], eps, prep["trace"], index, S, prep["score"]["min_lead_s"])) + n_ev = [sum(b["n"] for b in s["by_kind"].values()) for s in stats] + caught = [sum(b["caught"] for b in s["by_kind"].values()) for s in stats] + in_time = [sum(b["caught_in_time"] for b in s["by_kind"].values()) for s in stats] + leads = [[x / 60.0 for b in s["by_kind"].values() for x in b["leads_s"]] for s in stats] + by_kind = {} + for kind in EVENT_TYPES: + nk = [s["by_kind"].get(kind, {"n": 0})["n"] for s in stats] + if sum(nk) == 0: + continue + by_kind[kind] = { + "n_events": int(sum(nk)), + "recall": ratio_ci([s["by_kind"].get(kind, {"caught": 0})["caught"] for s in stats], nk, B, 41), + "recall_in_time": ratio_ci([s["by_kind"].get(kind, {"caught_in_time": 0})["caught_in_time"] for s in stats], nk, B, 42), + } + rows[f"{thr:g}"] = { + "threshold": thr, + "recall": ratio_ci(caught, n_ev, B, 43), + "recall_in_time": ratio_ci(in_time, n_ev, B, 44), + "false_alarms_per_day": bootstrap_ci([s["false_alarms_per_day"] for s in stats], B, 45), + "n_alarms": bootstrap_ci([s["n_alarms"] for s in stats], B, 46), + "lead_minutes_median": pooled_quantile_ci(leads, 0.5, B, 47), + "replay_total_gpu_h": bootstrap_ci([c["total_gpu_h"] for c in costs], B, 48), + "replay_miss_gpu_h": bootstrap_ci([c["miss_gpu_h"] for c in costs], B, 49), + "replay_alarm_gpu_h": bootstrap_ci([c["alarm_gpu_h"] for c in costs], B, 50), + "by_event_type": by_kind, + } + return rows + + +def git_state() -> dict: + def git(*a: str) -> str: + return subprocess.run(["git", *a], cwd=ROOT, capture_output=True, text=True, check=True).stdout.strip() + + dirty = bool(git("status", "--porcelain", "--", ".", ":(exclude)experiments/e003-heatwave/results")) + return {"commit": git("rev-parse", "HEAD"), "branch": git("rev-parse", "--abbrev-ref", "HEAD"), "dirty": str(dirty).lower()} + + +def stage_report(outage, S, args, work) -> None: + B = S.bootstrap_resamples + cal, index = load_calibration(outage, S, args, work) + tuning = json.loads((work / "tuning.json").read_text()) + test = seeds_for(outage, "test", args) + train = seeds_for(outage, "train", args) + preps = {s: load_pickle(work / f"prep_{s}.pkl") for s in train + test} + policies: dict = {} + runs_by_policy: dict = {} + not_run: dict = {} + for name in POLICIES: + files = [work / f"closed_{name}_{s}.pkl" for s in test] + if name == "finetuned" and not finetuned_available(args): + not_run[name] = "no fine-tuned weights were found (pass --finetuned-weights); the hook is wired but was not run" + continue + if not all(f.exists() for f in files): + not_run[name] = "closed-loop runs missing for some held-out seeds" + continue + recs = [load_pickle(f) for f in files] + runs = [r["score"] for r in recs] + runs_by_policy[name] = runs + agg = aggregate_runs(runs, S, has_warnings=name not in NO_WARNING_POLICIES) + agg["label"] = POLICY_LABELS[name] + agg["threshold"] = recs[0]["threshold"] + agg["wall_s_total"] = float(sum(r["wall_s"] for r in recs)) + agg["response_gpu_h_by_action"] = { + k: bootstrap_ci([r["response"]["gpu_h_by_action"].get(k, 0.0) for r in runs], B, 60) + for k in sorted({k for r in runs for k in r["response"]["gpu_h_by_action"]}) + } + agg["per_seed"] = { + str(r["seed"]): { + "lost_gpu_h": r["score"]["summary"]["lost_gpu_hours"], "lost_steps": r["score"]["summary"]["lost_steps"], + "deficit_gpu_h": r["score"]["deficit_gpu_h"], "response_gpu_h": r["score"]["response"]["total_gpu_h"], + "n_alarms": r["score"]["response"]["n_alarms"], "n_false_alarms": r["score"]["response"]["n_false_alarms"], + } + for r in recs + } + policies[name] = agg + paired = {} + if "reactive" in runs_by_policy: + base = runs_by_policy["reactive"] + for name, runs in runs_by_policy.items(): + if name == "reactive": + continue + paired[name] = { + "lost_gpu_h_minus_reactive": paired_difference([r["summary"]["lost_gpu_hours"] for r in runs], [r["summary"]["lost_gpu_hours"] for r in base], B, 70), + "lost_steps_minus_reactive": paired_difference([r["summary"]["lost_steps"] for r in runs], [r["summary"]["lost_steps"] for r in base], B, 71), + "deficit_gpu_h_minus_reactive": paired_difference([r["deficit_gpu_h"] for r in runs], [r["deficit_gpu_h"] for r in base], B, 72), + "import_energy_above_kwh_minus_reactive": paired_difference( + [r["summary"]["import_energy_above_kwh"] for r in runs], [r["summary"]["import_energy_above_kwh"] for r in base], B, 73), + } + sweep = {} + for name in tuning: + if all((work / f"pmax_{name}_{s}.npy").exists() for s in test): + sweep[name] = sweep_rows(outage, S, args, work, name, test, index) + event_counts = {} + for s in test: + for ev in preps[s]["table"]: + c = event_counts.setdefault(ev["kind"], {"n": 0, "with_precursor": 0, "scheduled": 0}) + c["n"] += 1 + c["with_precursor"] += int(bool(ev["has_precursor"])) + c["scheduled"] += int(ev["source"] == "scheduled") + first = preps[test[0]] + cfg0 = build_config(seed_scenario(outage, test[0]), seed=test[0]) + result = { + "data_kind": "simulated", + "scenario_id": outage["scenario_id"], + "seeds": {"train": train, "test": test}, + "settings": {k: (list(v) if isinstance(v, tuple) else v) for k, v in S.__dict__.items()}, + "calibration": cal.to_dict(), + "series": {"n_series": len(index), "keys": [list(k) for k in index.keys]}, + "horizon_s": cfg0.horizon_s, + "event_counts_test_seeds": event_counts, + "tuning": tuning, + "threshold_sweep_open_loop_test_seeds": sweep, + "policies": policies, + "paired_vs_reactive": paired, + "not_run": not_run, + "finetuned_weights": args.finetuned_weights if finetuned_available(args) else None, + "notes": [ + "All data are simulated; no measured cluster data are used.", + "Thresholds were chosen on training seeds with a replay objective, then used unchanged on held-out seeds.", + "The sweep is open loop on the recorded reactive traces; closed-loop numbers are in policies.", + "The perfect-foresight policy reads event times of the reactive run and is an upper bound, not a deployable policy.", + "deficit_gpu_h is the useful GPU-hours of the event-free run minus the policy run on the same seed.", + ], + "assumptions": outage["assumptions"], + } + from heatwave.provenance import write_result + + inputs = [SCENARIO, BASE_SCENARIO] + record = write_result(OUT, result, script=__file__, data_kind="simulated", inputs=inputs, + notes="Held-out seeds; thresholds tuned on training seeds; all outputs simulated.") + record["provenance"]["git"] = git_state() + record["provenance"]["script"] = str(Path(__file__).resolve().relative_to(ROOT)) + record["provenance"]["script_sha256"] = file_sha256(__file__) + record["provenance"]["module_sha256"] = file_sha256(ROOT / "gpu_stack" / "research" / "heatwave_outage.py") + record["provenance"]["inputs"] = {str(Path(p).resolve().relative_to(ROOT)): file_sha256(p) for p in inputs} + record["provenance"]["seeds"] = {"train": train, "test": test} + OUT.write_text(json.JSONEncoder(indent=1, allow_nan=False).encode(record) + "\n") + log(f"wrote {OUT} ({OUT.stat().st_size / 1024:.0f} KiB)") + + +def main() -> None: + warnings.filterwarnings("ignore") + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--work-dir", required=True, type=Path) + ap.add_argument("--stage", default="all", choices=("prepare", "openloop", "tune", "closed", "report", "all")) + ap.add_argument("--policies", nargs="*", default=None, choices=POLICIES) + ap.add_argument("--forecasters", nargs="*", default=None, choices=FORECASTERS + ("perfect",)) + ap.add_argument("--n-test", type=int, default=0, help="use only the first N held-out seeds (0 = all)") + ap.add_argument("--threads", type=int, default=None) + ap.add_argument("--batch-size", type=int, default=512) + ap.add_argument("--finetuned-weights", default=None) + ap.add_argument("--finetuned-size", default="28M") + args = ap.parse_args() + args.work_dir.mkdir(parents=True, exist_ok=True) + outage = load_outage_scenario(SCENARIO) + S = OutageSettings.from_scenario(outage) + work = args.work_dir + stages = ("prepare", "openloop", "tune", "closed", "report") if args.stage == "all" else (args.stage,) + for st in stages: + t0 = time.time() + {"prepare": stage_prepare, "openloop": stage_openloop, "tune": stage_tune, "closed": stage_closed, "report": stage_report}[st]( + outage, S, args, work + ) + log(f"stage {st} done in {time.time() - t0:.0f} s") + + +if __name__ == "__main__": + main() diff --git a/gpu_stack/research/heatwave_outage.py b/gpu_stack/research/heatwave_outage.py new file mode 100644 index 0000000..48e1668 --- /dev/null +++ b/gpu_stack/research/heatwave_outage.py @@ -0,0 +1,2102 @@ +"""Forecast-driven outage response for the e003 Heatwave simulator (all simulated). + +Everything in this module acts on, or scores, the SIMULATED two-site cluster of +:mod:`gpu_stack.research.heatwave_sim`. Event rates, precursors, lead times and +severities are simulation assumptions; nothing here is a claim about a real +facility. + +Decision loop +------------- +Every decision interval a controller + +1. reads the visible telemetry (never the ground-truth event list), +2. appends one sample per monitored series (see ``Channels``), +3. forecasts every series with 21 quantiles over the warning horizon using any + :class:`heatwave.forecast.Forecaster` (Timestral, seasonal naive, ETS, ARIMA, + a persistence reference, or a fine-tuned Timestral through ``weights_dir``), +4. converts the quantiles into the probability that the series crosses its + trouble level within the horizon, +5. raises a warning for a series when that probability reaches a threshold, and +6. responds: checkpoint the affected jobs immediately, move flexible jobs to the + other site if it has free GPUs (the simulator's ``MigrateJob`` checkpoints + first), otherwise pause them after the checkpoint, and resume when the + warning clears. + +Monitored series (``Channels``) +------------------------------- +``gpu_temp_max`` (rack, class ``thermal``) + Hottest GPU of the rack. Trouble level: the simulator's throttle threshold + (read from the scenario). +``supply_anom`` (rack, class ``cooling``) + Supply air temperature minus the supply setpoint implied by the outdoor + forecast. Carries the cooling-failure supply-temperature precursor. + Trouble level: a high quantile of the same channel in event-free runs on + training seeds (:func:`calibrate`). +``breaker_ratio`` (rack, class ``power``) + Rack IT power divided by the effective breaker rating (the derate precursor + shows up here). Trouble level: 1.0, rack power above the rating. +``gpu_anom`` (rack, class ``hardware``) + Largest per-GPU temperature rise of the rack relative to the GPU's own slow + baseline and its peers in the same job. Carries the hardware-fault + temperature precursor. Level: calibrated like ``supply_anom``. +``site_power`` (site, class ``curtail``) + Site power against the grid import limit, including curtailments announced + in the known-future schedule. + +Information boundary +-------------------- +Controllers see the simulator's :class:`VisibleState`. Two scheduler-side +facts are added through :class:`PlacementView`: which job holds which GPUs. +That is operational state of any scheduler, not event ground truth. The oracle +policy (perfect foresight, an upper bound) is the only one that reads event +times, and it says so in its name. + +Simulator note +-------------- +At simulator commit b6adcfa the per-GPU temperature offsets of the hardware +fault precursor are applied inside the simulator's thermal step but not to the +visible per-GPU temperature, so that precursor is invisible to every +controller. :class:`OutageRun` adds the offsets to the visible state (and +skips the addition if the simulator already exposes them). The request to fix +this upstream is in the experiment report. + +Scoring +------- +:func:`score_run` turns one simulator result plus the controller export into +per-event outcomes (lead time, work lost in steps and GPU-hours, curtailment +violation energy, throttle slowdown), warning-alarm outcomes (true or false +alarm, response cost in GPU-hours) and response costs. Aggregation with seed +bootstrap intervals is in :func:`aggregate_runs`. +""" + +from __future__ import annotations + +import copy +import hashlib +import json +import math +import re +import warnings +from dataclasses import dataclass, field, replace +from pathlib import Path +from typing import Any, Callable, Iterable, Mapping, Optional, Sequence + +import numpy as np + +from .heatwave_events import ( + KIND_BREAKER_TRIP, + KIND_COOLING_FAILURE, + KIND_GRID_CURTAILMENT, + KIND_HARDWARE_FAULT, + KIND_THERMAL_THROTTLE, + EventSchedule, + random_schedule, +) +from .heatwave_sim import ( + CheckpointNow, + MigrateJob, + NullController, + PauseJob, + ResumeJob, + SimConfig, + SimResult, + VisibleState, + _Run, + build_config, + load_scenario, +) + +SCHEMA = "gpu-stack.e003-heatwave-outage-scenario.v1" +RESULT_SCHEMA = "gpu-stack.e003-heatwave-outage-result.v1" + +CLASS_THERMAL = "thermal" +CLASS_COOLING = "cooling" +CLASS_POWER = "power" +CLASS_HARDWARE = "hardware" +CLASS_CURTAIL = "curtail" +CLASSES = (CLASS_THERMAL, CLASS_COOLING, CLASS_POWER, CLASS_HARDWARE, CLASS_CURTAIL) + +CH_GPU_TEMP = "gpu_temp_max" +CH_SUPPLY = "supply_anom" +CH_BREAKER = "breaker_ratio" +CH_GPU_ANOM = "gpu_anom" +CH_SITE_POWER = "site_power" +RACK_CHANNELS = (CH_GPU_TEMP, CH_SUPPLY, CH_BREAKER, CH_GPU_ANOM) +SITE_CHANNELS = (CH_SITE_POWER,) +CHANNEL_CLASS = { + CH_GPU_TEMP: CLASS_THERMAL, + CH_SUPPLY: CLASS_COOLING, + CH_BREAKER: CLASS_POWER, + CH_GPU_ANOM: CLASS_HARDWARE, + CH_SITE_POWER: CLASS_CURTAIL, +} + +# Which warning classes may explain which kind of event. +EVENT_CLASSES = { + KIND_COOLING_FAILURE: (CLASS_THERMAL, CLASS_COOLING), + KIND_BREAKER_TRIP: (CLASS_POWER,), + KIND_GRID_CURTAILMENT: (CLASS_CURTAIL,), + KIND_HARDWARE_FAULT: (CLASS_HARDWARE,), + KIND_THERMAL_THROTTLE: (CLASS_THERMAL, CLASS_COOLING), +} +EVENT_TYPES = (KIND_THERMAL_THROTTLE, KIND_COOLING_FAILURE, KIND_BREAKER_TRIP, KIND_GRID_CURTAILMENT, KIND_HARDWARE_FAULT) + +MIN_VALID_SAMPLES = 12 +MIN_BASELINE_SAMPLES = 6 + +# --------------------------------------------------------------------------- +# validation helpers +# --------------------------------------------------------------------------- + + +def _num(value: Any, name: str, *, minimum: float = 0.0, strict: bool = False, maximum: float = math.inf) -> float: + if isinstance(value, bool) or not isinstance(value, (int, float, np.integer, np.floating)): + raise ValueError(f"{name} must be a real number") + v = float(value) + if not math.isfinite(v): + raise ValueError(f"{name} must be finite") + if (strict and not v > minimum) or (not strict and v < minimum): + raise ValueError(f"{name} must be {'>' if strict else '>='} {minimum}") + if v > maximum: + raise ValueError(f"{name} must be <= {maximum}") + return v + + +def _int(value: Any, name: str, minimum: int = 1) -> int: + if isinstance(value, bool) or not isinstance(value, (int, np.integer)) or int(value) < minimum: + raise ValueError(f"{name} must be an integer >= {minimum}") + return int(value) + + +# --------------------------------------------------------------------------- +# settings and scenario handling +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class OutageSettings: + """Response-policy and evaluation settings (all simulation assumptions).""" + + decision_interval_s: float + warmup_s: float + context_steps: int + horizon_steps: int + clear_decisions: int + recheckpoint_s: float + max_pause_s: float + calibration_quantile: float + anomaly_baseline_window_s: float + anomaly_baseline_gap_s: float + anomaly_smooth_steps: int + curtail_shed_margin: float + oracle_extra_lead_steps: int + episode_merge_gap_s: float + threshold_grid: tuple[float, ...] + observed_threshold: float + bootstrap_resamples: int + + def __post_init__(self) -> None: + _num(self.decision_interval_s, "decision_interval_s", strict=True) + _num(self.warmup_s, "warmup_s") + _int(self.context_steps, "context_steps", MIN_VALID_SAMPLES) + _int(self.horizon_steps, "horizon_steps", 1) + _int(self.clear_decisions, "clear_decisions", 1) + _num(self.recheckpoint_s, "recheckpoint_s", strict=True) + _num(self.max_pause_s, "max_pause_s", strict=True) + _num(self.calibration_quantile, "calibration_quantile", strict=True, maximum=1.0) + _num(self.anomaly_baseline_window_s, "anomaly_baseline_window_s", strict=True) + _num(self.anomaly_baseline_gap_s, "anomaly_baseline_gap_s") + _int(self.anomaly_smooth_steps, "anomaly_smooth_steps", 1) + _num(self.curtail_shed_margin, "curtail_shed_margin", maximum=0.5) + _int(self.oracle_extra_lead_steps, "oracle_extra_lead_steps", 0) + _num(self.episode_merge_gap_s, "episode_merge_gap_s") + _num(self.observed_threshold, "observed_threshold", strict=True, maximum=1.0) + _int(self.bootstrap_resamples, "bootstrap_resamples", 10) + if not self.threshold_grid or any(not 0.0 < g < 1.0 for g in self.threshold_grid): + raise ValueError("threshold_grid needs values strictly between 0 and 1") + + @property + def horizon_s(self) -> float: + return self.horizon_steps * self.decision_interval_s + + @classmethod + def from_scenario(cls, outage: Mapping[str, Any]) -> "OutageSettings": + block = outage["outage"] + gen = outage["event_generation"] + return cls( + decision_interval_s=float(block["decision_interval_s"]), + warmup_s=float(gen["warmup_s"]), + context_steps=int(block["context_steps"]), + horizon_steps=int(block["horizon_steps"]), + clear_decisions=int(block["clear_decisions"]), + recheckpoint_s=float(block["recheckpoint_s"]), + max_pause_s=float(block["max_pause_s"]), + calibration_quantile=float(block["calibration_quantile"]), + anomaly_baseline_window_s=float(block["anomaly_baseline_window_s"]), + anomaly_baseline_gap_s=float(block["anomaly_baseline_gap_s"]), + anomaly_smooth_steps=int(block["anomaly_smooth_steps"]), + curtail_shed_margin=float(block["curtail_shed_margin"]), + oracle_extra_lead_steps=int(block["oracle_extra_lead_steps"]), + episode_merge_gap_s=float(block["episode_merge_gap_s"]), + threshold_grid=tuple(float(x) for x in block["threshold_grid"]), + observed_threshold=float(block["observed_threshold"]), + bootstrap_resamples=int(block["bootstrap_resamples"]), + ) + + +def deep_merge(base: Mapping[str, Any], over: Mapping[str, Any]) -> dict[str, Any]: + """Recursive dict merge; lists and scalars in ``over`` replace those in ``base``.""" + out = copy.deepcopy(dict(base)) + for key, value in over.items(): + if isinstance(value, Mapping) and isinstance(out.get(key), Mapping): + out[key] = deep_merge(out[key], value) + else: + out[key] = copy.deepcopy(value) + return out + + +def load_outage_scenario(path: Path | str) -> dict[str, Any]: + """Load the outage scenario and attach the merged simulator scenario as ``sim``. + + ``sim`` is the base simulator scenario (``base_scenario``, relative to the + outage file) with ``base_overrides`` merged in. No power parameter is set + here, so a recalibrated base scenario flows straight through. + """ + path = Path(path) + outage = json.loads(path.read_text()) + if outage.get("schema") != SCHEMA: + raise ValueError(f"outage scenario schema must be {SCHEMA!r}") + if outage.get("data_kind") != "simulated": + raise ValueError("outage scenario data_kind must be 'simulated'") + base = load_scenario(path.parent / outage["base_scenario"]) + outage = dict(outage) + outage["sim"] = deep_merge(base, outage.get("base_overrides", {})) + s = OutageSettings.from_scenario(outage) + sim = outage["sim"] + if abs(sim["controller_interval_s"] - s.decision_interval_s) > 1e-9: + raise ValueError("sim controller_interval_s must equal outage decision_interval_s") + if sim["horizon_s"] <= s.warmup_s + s.horizon_s: + raise ValueError("horizon too short for warm-up plus one warning horizon") + return outage + + +def seed_scenario(outage: Mapping[str, Any], seed: int, *, with_events: bool = True) -> dict[str, Any]: + """Simulator scenario for one seed: seeded jobs, weather and (optionally) events. + + Events are drawn with :func:`random_schedule` from ``event_generation`` and + shifted by the warm-up so that none start before the forecaster has a full + context. Same seed, same scenario. + """ + offsets = outage["seed_streams"] + sim = copy.deepcopy(outage["sim"]) + sim["jobs"]["generate"]["seed"] = int(seed) + int(offsets["job_seed_offset"]) + sim["environment"]["seed"] = int(seed) + int(offsets["environment_seed_offset"]) + gen = outage["event_generation"] + if not with_events: + sim["events"] = [] + return sim + site_racks = { + item["site"]["site_id"]: [(r["rack_id"], int(r["n_gpus"])) for r in item["racks"]] for item in sim["sites"] + } + window = float(sim["horizon_s"]) - float(gen["warmup_s"]) - float(gen["tail_s"]) + if window <= 0: + raise ValueError("no room for events between warm-up and tail") + sched = random_schedule( + int(seed) + int(offsets["event_seed_offset"]), + window, + site_racks, + gen["rates_per_site_per_day"], + float(gen["precursor_probability"]), + tuple(gen["lead_range_s"]), + {k: tuple(v) for k, v in gen["strength_ranges"].items()}, + tuple(gen["duration_range_s"]), + tuple(gen["remaining_capacity_range"]), + tuple(gen["derate_range"]), + tuple(gen["limit_fraction_range"]), + tuple(gen["repair_range_s"]), + ) + events = [] + for ev in sched.events: + d = ev.to_dict() + d["onset_s"] = float(d["onset_s"]) + float(gen["warmup_s"]) + events.append(d) + sim["events"] = events + return sim + + +# --------------------------------------------------------------------------- +# running the simulator with the scheduler-side placement view +# --------------------------------------------------------------------------- + + +class OutageRun(_Run): + """Simulator run that also exposes the hardware precursor in the visible per-GPU temperature.""" + + def _visible_state(self, t: float, b: int) -> VisibleState: + vs = super()._visible_state(t, b) + offsets = self.signals.gpu_temp_offsets(t) + if not offsets: + return vs + gpu_temp = vs.now["gpu_temp_c"] + changed = False + for rack, slot, off in offsets: + idx = int(self.rack_start[rack] + slot) + if not np.isfinite(gpu_temp[idx]): + continue + base_value = self.rack_air_now[rack] + self.k_gpu[idx] * self.gpu_mean_prev[idx] + if abs(gpu_temp[idx] - base_value) > 0.5 * abs(off) and abs(off) > 1e-9: + continue # the simulator already includes the offset + gpu_temp[idx] += off + changed = True + if changed: + vs.now["max_gpu_temp_c"][:] = self._rack_max(gpu_temp) + return vs + + +class PlacementView: + """Which job holds which GPUs (scheduler state; not event ground truth).""" + + def __init__(self, run: _Run) -> None: + self._run = run + + @property + def rack_ids(self) -> list[str]: + return list(self._run.rack_ids) + + def n_gpus(self) -> int: + return int(self._run.G) + + def gpu_rack(self) -> np.ndarray: + return np.asarray(self._run.gpu_rack) + + def gpu_slot(self) -> np.ndarray: + return np.asarray(self._run.gpu_slot) + + def gpu_group(self) -> np.ndarray: + """Shard id per GPU, ``-1`` for GPUs without a job.""" + g = np.asarray(self._run.gpu_shard).astype(int) + g[g == self._run.n_shards] = -1 + return g + + def job_rack_counts(self) -> dict[str, dict[str, int]]: + out: dict[str, dict[str, int]] = {} + run = self._run + for sh in run.shards: + if not sh.gpus.size: + continue + racks = run.gpu_rack[sh.gpus] + counts = out.setdefault(sh.job.job_id, {}) + for r in np.unique(racks): + counts[run.rack_ids[int(r)]] = counts.get(run.rack_ids[int(r)], 0) + int(np.sum(racks == r)) + return out + + def job_at(self, rack_id: str, slot: int) -> Optional[str]: + run = self._run + g = int(run.rack_start[run.rack_ids.index(rack_id)] + slot) + sid = int(run.gpu_shard[g]) + if sid == run.n_shards: + return None + return run.shards[sid].job.job_id + + +def run_policy( + scenario: Mapping[str, Any], + controller: Any = None, + seed: int = 0, + job_transform: Optional[Callable[[Sequence[Any]], Sequence[Any]]] = None, +) -> tuple[SimResult, SimConfig]: + """Run the simulator with a controller; controllers with ``bind`` get a placement view first. + + ``job_transform`` rewrites the generated job list before the run (used for + the e001 averaging-rule policy). Returns the result and the config used. + """ + cfg = build_config(scenario, seed=seed) + if job_transform is not None: + cfg = replace(cfg, jobs=tuple(job_transform(cfg.jobs))) + run = OutageRun(cfg, controller, seed) + if controller is not None and hasattr(controller, "bind"): + controller.bind(run, cfg) + return run.run(), cfg + + +def periodic_local_jobs(jobs: Iterable[Any]) -> list[Any]: + """The e001 averaging rule for every job: two shards, one per site, averaged every 8 steps. + + Jobs with an odd GPU count cannot be split and are left single-site. + """ + out = [] + for j in jobs: + if j.n_gpus % 2 == 0 and not j.multisite: + out.append(replace(j, multisite=True, site_id=None, average_every_steps=8)) + else: + out.append(j) + return out + + +# --------------------------------------------------------------------------- +# series layout +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class SeriesIndex: + """Fixed order of the monitored series: rack channels first, then site channels.""" + + keys: tuple[tuple[str, str], ...] # (unit_id, channel) + rack_ids: tuple[str, ...] + site_ids: tuple[str, ...] + rack_site: tuple[str, ...] + + @classmethod + def from_config(cls, cfg: SimConfig) -> "SeriesIndex": + rack_ids = tuple(r.rack_id for lay in cfg.layouts for r in lay.racks) + rack_site = tuple(lay.site.site_id for lay in cfg.layouts for _ in lay.racks) + site_ids = tuple(lay.site.site_id for lay in cfg.layouts) + keys = [(r, ch) for ch in RACK_CHANNELS for r in rack_ids] + [(s, ch) for ch in SITE_CHANNELS for s in site_ids] + return cls(tuple(keys), rack_ids, site_ids, rack_site) + + def __len__(self) -> int: + return len(self.keys) + + def index(self, unit: str, channel: str) -> int: + return self.keys.index((unit, channel)) + + def classes(self) -> list[str]: + return [CHANNEL_CLASS[ch] for _, ch in self.keys] + + def units(self) -> list[str]: + return [u for u, _ in self.keys] + + +# --------------------------------------------------------------------------- +# probabilities from quantiles +# --------------------------------------------------------------------------- + + +def exceedance_probability(values: np.ndarray, levels: Sequence[float], limit: np.ndarray) -> np.ndarray: + """P(X > limit) from quantile forecasts. + + ``values`` is ``(n, H, Q)`` with the Q quantile levels ``levels``; ``limit`` + is ``(n, H)``. Between the quantiles the CDF is interpolated linearly; below + the lowest level the probability is capped at ``1 - levels[0]`` and above the + highest level it is floored at ``1 - levels[-1]`` (the forecast carries no + information past its outermost quantile). A forecast with zero spread (a + persistence or oracle forecast) gives exactly 0 or 1. NaN limits give 0. + """ + q = np.asarray(values, dtype=float) + lim = np.asarray(limit, dtype=float) + lv = np.asarray(levels, dtype=float) + if q.ndim != 3 or q.shape[-1] != lv.size or lim.shape != q.shape[:2]: + raise ValueError("values must be (n, H, Q) and limit (n, H)") + q = np.sort(q, axis=-1) + spread = q[..., -1] - q[..., 0] + # position of the limit among the quantiles (vectorized searchsorted) + below = (q <= lim[..., None]).sum(axis=-1) # number of quantiles <= limit + p = np.empty(lim.shape) + lo_idx = np.clip(below - 1, 0, lv.size - 2) + q_lo = np.take_along_axis(q, lo_idx[..., None], axis=-1)[..., 0] + q_hi = np.take_along_axis(q, (lo_idx + 1)[..., None], axis=-1)[..., 0] + width = np.maximum(q_hi - q_lo, 1e-12) + frac = np.clip((lim - q_lo) / width, 0.0, 1.0) + cdf = lv[lo_idx] + frac * (lv[lo_idx + 1] - lv[lo_idx]) + cdf = np.where(below == 0, lv[0], cdf) + cdf = np.where(below == lv.size, lv[-1], cdf) + p[...] = 1.0 - cdf + flat = spread <= 1e-9 + p = np.where(flat, (q[..., 0] > lim).astype(float), p) + p = np.where(np.isfinite(lim), p, 0.0) + return np.clip(p, 0.0, 1.0) + + +class PersistenceForecaster: + """No forecast: every quantile equals the last observed value. + + Used for the reactive-observed reference, which acts when the current value + is already past its trouble level. + """ + + name = "persistence" + + def forecast(self, context, horizon, *, known_covariates=None, past_covariates=None): + from heatwave.forecast.base import QUANTILE_LEVELS, QuantileForecast + + x = np.asarray(context, dtype=float) + if x.ndim == 2: + x = x[:, None, :] + last = np.full(x.shape[:2], np.nan) + for b in range(x.shape[0]): + for v in range(x.shape[1]): + ok = np.flatnonzero(np.isfinite(x[b, v])) + if ok.size: + last[b, v] = x[b, v, ok[-1]] + vals = np.repeat(last[:, :, None, None], horizon, axis=2) + vals = np.repeat(vals, len(QUANTILE_LEVELS), axis=3) + return QuantileForecast(values=np.nan_to_num(vals, nan=0.0), method=self.name) + + +def window_probabilities( + forecaster: Any, + windows: np.ndarray, + limits: np.ndarray, + horizon: int, + *, + batch_size: int = 512, +) -> np.ndarray: + """Exceedance probability per series and horizon step: ``(n, H)``. + + ``windows`` is ``(n, T)`` with NaN for missing samples; rows with fewer than + ``MIN_VALID_SAMPLES`` finite samples or whose newest sample is missing get + probability 0 (a rack that is off is not forecast). + """ + from heatwave.forecast.base import QUANTILE_LEVELS + + n = windows.shape[0] + out = np.zeros((n, horizon)) + valid = (np.isfinite(windows).sum(axis=1) >= MIN_VALID_SAMPLES) & np.isfinite(windows[:, -1]) + rows = np.flatnonzero(valid) + for start in range(0, rows.size, batch_size): + sel = rows[start:start + batch_size] + fc = forecaster.forecast(windows[sel][:, None, :], horizon) + vals = np.asarray(fc.values)[:, 0] + out[sel] = exceedance_probability(vals, getattr(fc, "levels", QUANTILE_LEVELS), limits[sel]) + return out + + +# --------------------------------------------------------------------------- +# warning episodes +# --------------------------------------------------------------------------- + + +class EpisodeTracker: + """Per-series warning state with release hysteresis. + + A series is in warning from the first decision with probability at or above + ``threshold`` until ``clear_decisions`` consecutive decisions fall below it. + An episode ends at the last decision at which the series was at or above the + threshold. + """ + + def __init__(self, n_series: int, threshold: float, clear_decisions: int) -> None: + self.n = int(n_series) + self.threshold = float(threshold) + self.clear_decisions = int(clear_decisions) + self.active = np.zeros(self.n, dtype=bool) + self._below = np.zeros(self.n, dtype=int) + self._start = np.full(self.n, np.nan) + self._last = np.full(self.n, np.nan) + self._peak = np.zeros(self.n) + self._count = np.zeros(self.n, dtype=int) + self.closed: list[dict[str, Any]] = [] + + def update(self, t_s: float, pmax: np.ndarray) -> np.ndarray: + p = np.nan_to_num(np.asarray(pmax, dtype=float), nan=0.0) + hit = p >= self.threshold + for k in np.flatnonzero(hit): + if not self.active[k]: + self.active[k] = True + self._start[k] = t_s + self._peak[k] = 0.0 + self._count[k] = 0 + self._last[k] = t_s + self._below[k] = 0 + self._peak[k] = max(self._peak[k], p[k]) + self._count[k] += 1 + for k in np.flatnonzero(self.active & ~hit): + self._below[k] += 1 + if self._below[k] >= self.clear_decisions: + self._close(k) + return self.active.copy() + + def _close(self, k: int) -> None: + self.closed.append( + {"k": int(k), "start_s": float(self._start[k]), "end_s": float(self._last[k]), + "peak_p": float(self._peak[k]), "n_decisions": int(self._count[k])} + ) + self.active[k] = False + self._below[k] = 0 + + def finish(self) -> list[dict[str, Any]]: + for k in np.flatnonzero(self.active): + self._close(int(k)) + return sorted(self.closed, key=lambda e: (e["start_s"], e["k"])) + + def since(self, k: int) -> float: + return float(self._start[k]) + + +def episodes_from_pmax( + times_s: np.ndarray, + pmax: np.ndarray, + threshold: float, + clear_decisions: int, + index: Optional["SeriesIndex"] = None, +) -> list[dict[str, Any]]: + """Episodes from a recorded ``(n_decisions, n_series)`` probability matrix. + + With ``index`` each episode also carries ``unit``, ``channel`` and ``class`` + (the same fields a controller export has). + """ + tr = EpisodeTracker(pmax.shape[1], threshold, clear_decisions) + for d in range(pmax.shape[0]): + tr.update(float(times_s[d]), pmax[d]) + out = tr.finish() + if index is not None: + out = [{**e, "unit": index.keys[e["k"]][0], "channel": index.keys[e["k"]][1], "class": CHANNEL_CLASS[index.keys[e["k"]][1]]} for e in out] + return out + + +# --------------------------------------------------------------------------- +# channel sampling +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class Calibration: + """Trouble levels for the anomaly channels, from event-free runs on training seeds.""" + + limits: Mapping[str, float] + quantile: float + n_samples: Mapping[str, int] + source: str = "" + + def to_dict(self) -> dict[str, Any]: + return {"limits": dict(self.limits), "quantile": self.quantile, "n_samples": dict(self.n_samples), "source": self.source} + + +def _peer_median(values: np.ndarray, group: np.ndarray, rack: np.ndarray) -> np.ndarray: + """Median of ``values`` over GPUs with the same job group in the same rack (NaN-aware).""" + key = (group.astype(np.int64) + 1) * 1000 + rack.astype(np.int64) + out = np.full(values.shape, np.nan) + for k in np.unique(key): + idx = np.flatnonzero(key == k) + v = values[idx] + if np.isfinite(v).any(): + out[idx] = np.nanmedian(v[np.isfinite(v)]) + return out + + +class ChannelBank: + """Turns visible state into one sample vector per decision and keeps the history.""" + + def __init__(self, cfg: SimConfig, settings: OutageSettings, placement: Optional[PlacementView] = None) -> None: + self.cfg = cfg + self.settings = settings + self.placement = placement + self.index = SeriesIndex.from_config(cfg) + self.times: list[float] = [] + self.rows: list[np.ndarray] = [] + self.site_limits: list[np.ndarray] = [] # (S, H) per decision + self._rel_hist: list[np.ndarray] = [] + self._grp_hist: list[np.ndarray] = [] + self._site_nominal_import = np.array([float(lay.site.grid_import_limit_w) for lay in cfg.layouts]) + self._site_cap = np.array([float(lay.site.power_cap_w) for lay in cfg.layouts]) + self._rack_start = None + + # -- helpers ----------------------------------------------------------------- + + def _outdoor_forecast_at(self, vs: VisibleState, t_s: float) -> np.ndarray: + hours = np.asarray(vs.known_future["hour_start_s"], dtype=float) + fc = np.asarray(vs.known_future["outdoor_forecast_c"], dtype=float) + return np.array([np.interp(t_s, hours, fc[:, s]) for s in range(fc.shape[1])]) + + def supply_setpoint_c(self, vs: VisibleState, t_s: float) -> np.ndarray: + th = self.cfg.thermal + out = self._outdoor_forecast_at(vs, t_s) + return th.supply_setpoint_c + th.outdoor_coupling_k_per_k * np.maximum(0.0, out - th.outdoor_reference_c) + + def site_limit_path(self, vs: VisibleState, t_s: float) -> np.ndarray: + """Grid import limit (W) at each horizon step, from the current limit and announced curtailments.""" + H = self.settings.horizon_steps + dt = self.settings.decision_interval_s + site_ids = list(vs.site_ids) + current = np.asarray(vs.now["site_import_limit_w"], dtype=float) + path = np.repeat(current[:, None], H, axis=1) + for c in vs.known_future.get("announced_curtailments", ()): + s = site_ids.index(c["site_id"]) + for h in range(H): + th = t_s + (h + 1) * dt + if c["onset_s"] <= th < c["end_s"]: + path[s, h] = min(path[s, h], self._site_nominal_import[s] * float(c["limit_fraction"])) + return path + + # -- sampling ------------------------------------------------------------------ + + def sample(self, t_s: float, vs: VisibleState) -> np.ndarray: + now = vs.now + R = len(self.index.rack_ids) + S = len(self.index.site_ids) + on = np.asarray(now["rack_on"], dtype=bool) + gpu_temp_max = np.where(on, np.asarray(now["max_gpu_temp_c"], dtype=float), np.nan) + sp = self.supply_setpoint_c(vs, t_s) + site_index = {s: i for i, s in enumerate(self.index.site_ids)} + rack_site_i = np.array([site_index[s] for s in self.index.rack_site]) + supply = np.where(on, np.asarray(now["supply_c"], dtype=float) - sp[rack_site_i], np.nan) + ratio = np.where(on, np.asarray(now["breaker_load_ratio"], dtype=float), np.nan) + gpu_anom = self._gpu_anomaly(vs) + site_power = np.asarray(now["site_power_w"], dtype=float) + row = np.concatenate([gpu_temp_max, supply, ratio, gpu_anom, site_power]) + row[~np.isfinite(row)] = np.nan + self.times.append(float(t_s)) + self.rows.append(row) + self.site_limits.append(self.site_limit_path(vs, t_s)) + return row + + def _gpu_anomaly(self, vs: VisibleState) -> np.ndarray: + """Per-rack largest per-GPU temperature rise against the GPU's own baseline (K). + + Each GPU's temperature above its rack air is compared with the median of + its peers (same job, same rack) so job-wide power changes cancel. The + result is smoothed over ``anomaly_smooth_steps`` decisions and compared + with the GPU's own median over the baseline window, using only samples + taken while the GPU belonged to the same job. + """ + R = len(self.index.rack_ids) + temp = np.asarray(vs.now["gpu_temp_c"], dtype=float) + gpu_rack = self.placement.gpu_rack() if self.placement is not None else self._infer_gpu_rack(temp, vs) + group = self.placement.gpu_group() if self.placement is not None else np.full(temp.shape, -1) + air = np.asarray(vs.now["rack_air_c"], dtype=float) + excess = temp - air[gpu_rack] + rel = excess - _peer_median(excess, group, gpu_rack) + self._rel_hist.append(rel) + self._grp_hist.append(group) + n = len(self._rel_hist) + dt = self.settings.decision_interval_s + gap = int(math.ceil(self.settings.anomaly_baseline_gap_s / dt)) + win = int(math.ceil(self.settings.anomaly_baseline_window_s / dt)) + sm = self.settings.anomaly_smooth_steps + out = np.full(R, np.nan) + lo, hi = max(0, n - 1 - win), max(0, n - gap) + if hi - lo < MIN_BASELINE_SAMPLES: + return out + old = np.stack(self._rel_hist[lo:hi]) + same = np.stack(self._grp_hist[lo:hi]) == group[None, :] + old = np.where(same, old, np.nan) + enough = np.isfinite(old).sum(axis=0) >= MIN_BASELINE_SAMPLES + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + base = np.nanmedian(old, axis=0) + recent = np.nanmean(np.stack(self._rel_hist[-sm:]), axis=0) + anomaly = np.where(enough, recent - base, np.nan) + for r in range(R): + v = anomaly[gpu_rack == r] + if np.isfinite(v).any(): + out[r] = np.nanmax(v) + return out + + def _infer_gpu_rack(self, temp: np.ndarray, vs: VisibleState) -> np.ndarray: + if self._rack_start is None: + counts = [lay_r.n_gpus for lay in self.cfg.layouts for lay_r in lay.racks] + self._rack_start = np.repeat(np.arange(len(counts)), counts) + return self._rack_start + + # -- views ---------------------------------------------------------------------- + + def matrix(self) -> np.ndarray: + """Samples as ``(n_series, n_decisions)``.""" + return np.stack(self.rows, axis=1) if self.rows else np.zeros((len(self.index), 0)) + + def limits_matrix(self, calibration: Optional[Calibration], d: int) -> np.ndarray: + """Trouble levels ``(n_series, H)`` for decision ``d``.""" + return series_limits(self.cfg, self.index, self.settings, calibration, self.site_limits[d]) + + +def series_limits( + cfg: SimConfig, + index: SeriesIndex, + settings: OutageSettings, + calibration: Optional[Calibration], + site_limit: np.ndarray, +) -> np.ndarray: + """Trouble level of every series at each horizon step: ``(n_series, H)``; NaN when unknown.""" + H = settings.horizon_steps + out = np.full((len(index), H), np.nan) + site_i = {s: i for i, s in enumerate(index.site_ids)} + for k, (unit, ch) in enumerate(index.keys): + if ch == CH_GPU_TEMP: + out[k] = float(cfg.throttle.throttle_threshold_c) + elif ch == CH_BREAKER: + out[k] = 1.0 + elif ch in (CH_SUPPLY, CH_GPU_ANOM): + if calibration is not None and ch in calibration.limits: + out[k] = float(calibration.limits[ch]) + elif ch == CH_SITE_POWER: + out[k] = site_limit[site_i[unit]] + return out + + +def calibrate( + traces: Sequence[np.ndarray], + index: SeriesIndex, + settings: OutageSettings, + times: Sequence[np.ndarray], + source: str = "", +) -> Calibration: + """Trouble levels for ``supply_anom`` and ``gpu_anom``: a high quantile of event-free samples. + + ``traces`` are ``(n_series, n_decisions)`` sample matrices from event-free + runs on training seeds; samples before the warm-up are ignored. + """ + limits: dict[str, float] = {} + counts: dict[str, int] = {} + for ch in (CH_SUPPLY, CH_GPU_ANOM): + ks = [k for k, (_, c) in enumerate(index.keys) if c == ch] + pool = [] + for tr, t in zip(traces, times): + keep = np.asarray(t) >= settings.warmup_s + pool.append(tr[np.ix_(ks, np.flatnonzero(keep))].ravel()) + v = np.concatenate(pool) if pool else np.zeros(0) + v = v[np.isfinite(v)] + if v.size == 0: + raise ValueError(f"no event-free samples to calibrate {ch}") + limits[ch] = float(np.quantile(v, settings.calibration_quantile)) + counts[ch] = int(v.size) + return Calibration(limits, settings.calibration_quantile, counts, source) + + +# --------------------------------------------------------------------------- +# response planning +# --------------------------------------------------------------------------- + + +def job_costs(job: Any, transfer: Any) -> dict[str, float]: + """GPU-hours a response costs one job, from the simulator's own stall and transfer times.""" + n = float(job.n_gpus) + ckpt = n * float(job.checkpoint_duration_s) / 3600.0 + move = ckpt + n * (float(transfer.transfer_s(int(job.n_gpus))) + float(transfer.restore_s(int(job.n_gpus)))) / 3600.0 + return {"checkpoint": ckpt, "move": move} + + +@dataclass +class Alarm: + """An active warning on a rack (or site) and what caused it.""" + + unit: str + classes: tuple[str, ...] + since_s: float + p: float = 1.0 + slot: Optional[int] = None # oracle only: the single GPU slot that will fail + limit_w: Optional[float] = None # site alarms: lowest import limit expected within the horizon + event_s: Optional[float] = None # oracle only: time of the event the alarm is for + gated: bool = False # oracle only: wait for each job's exact checkpoint lead + + +class ResponsePlanner: + """Checkpoint now, then move to the other site if it has room, else pause; resume when clear.""" + + def __init__(self, cfg: SimConfig, settings: OutageSettings) -> None: + self.cfg = cfg + self.settings = settings + self.jobs = {j.job_id: j for j in cfg.jobs} + self.site_ids = [lay.site.site_id for lay in cfg.layouts] + self.rack_site = {r.rack_id: lay.site.site_id for lay in cfg.layouts for r in lay.racks} + self.gate: Optional[Callable[[str, Alarm, float], bool]] = None # oracle only: is a job due for its action? + self.last_checkpoint: dict[str, float] = {} + self.staged: dict[str, float] = {} + self.paused: dict[str, tuple[str, float]] = {} + self.meta: list[dict[str, Any]] = [] + lv = cfg.levels + c = cfg.compute_fraction_of_step + self._run_w = c * lv.compute_w + (1.0 - c) * lv.comm_w + self._idle_w = lv.idle_w + self._host_w = lv.host_w_per_gpu + + # -- bookkeeping --------------------------------------------------------------- + + def _note(self, t: float, action: str, job_id: str, unit: str, why: str) -> None: + self.meta.append({"t_s": float(t), "action": action, "job_id": job_id, "unit": unit, "why": why}) + + def job_power_saving_w(self, n_gpus: int) -> float: + """Power a paused job sheds: its GPUs go from the phase-averaged running level to idle.""" + return n_gpus * max(self._run_w - self._idle_w, 0.0) + + def job_power_w(self, n_gpus: int) -> float: + return n_gpus * (self._run_w + self._host_w) + + def _dest(self, src_site: str, n: int, free: dict[str, int], blocked: set[str]) -> Optional[str]: + best, best_free = None, -1 + for s in self.site_ids: + if s == src_site or s in blocked: + continue + if free.get(s, 0) >= n and free[s] > best_free: + best, best_free = s, free[s] + return best + + # -- planning ------------------------------------------------------------------ + + def plan( + self, + t_s: float, + vs: VisibleState, + placement: Mapping[str, Mapping[str, int]], + rack_alarms: Mapping[str, Alarm], + site_alarms: Mapping[str, Alarm], + job_at: Optional[Callable[[str, int], Optional[str]]] = None, + ) -> list[Any]: + """Actions for this decision. Site alarms carry ``limit_w``; rack alarms may name one GPU slot.""" + st = self.settings + views = {jv.job_id: jv for jv in vs.jobs} + free = {s: int(vs.now["free_gpus"][i]) for i, s in enumerate(vs.site_ids)} + site_power = {s: float(vs.now["site_power_w"][i]) for i, s in enumerate(vs.site_ids)} + site_limit = {s: float(vs.now["site_import_limit_w"][i]) for i, s in enumerate(vs.site_ids)} + actions: list[Any] = [] + handled: set[str] = set() + blocked_dest = set(site_alarms) + + # -- site curtailment: shed the cheapest flexible jobs until the forecast power fits + for site, alarm in sorted(site_alarms.items()): + limit = alarm.limit_w if alarm.limit_w is not None else site_limit[site] + need = site_power[site] - limit * (1.0 - st.curtail_shed_margin) + if need <= 0: + continue + cands = [] + for job_id, racks in placement.items(): + job = self.jobs[job_id] + jv = views.get(job_id) + if jv is None or job.multisite or not jv.flexible or jv.paused: + continue + if jv.status in ("queued", "done"): + continue + if any(self.rack_site[r] == site for r in racks): + cands.append((jv.value_per_gpu_hour, -job.n_gpus, job_id)) + cands.sort() + saved = 0.0 + for _, _, job_id in cands: + if saved >= need: + break + job = self.jobs[job_id] + n = int(job.n_gpus) + jv = views[job_id] + saved += self.job_power_saving_w(n) + handled.add(job_id) + dest = self._dest(site, n, free, blocked_dest) if jv.status == "running" else None + if dest is not None and self._dest_has_power(dest, n, site_power, site_limit): + actions.append(MigrateJob(job_id, dest)) + self._note(t_s, "MigrateJob", job_id, site, "curtail") + free[dest] -= n + free[site] += n + site_power[dest] += self.job_power_w(n) + self.last_checkpoint[job_id] = t_s + continue + self._checkpoint_or_pause(t_s, job_id, jv, site, "curtail", actions) + + # -- rack alarms: protect every job that holds GPUs on the rack + for rack, alarm in sorted(rack_alarms.items()): + site = self.rack_site[rack] + only = None + if alarm.slot is not None and job_at is not None: + only = job_at(rack, alarm.slot) + if only is None: + continue + for job_id, racks in sorted(placement.items()): + if rack not in racks or job_id in handled: + continue + if only is not None and job_id != only: + continue + job = self.jobs[job_id] + jv = views.get(job_id) + if jv is None or job.multisite or jv.paused: + continue + if self.gate is not None and not self.gate(job_id, alarm, t_s): + continue + handled.add(job_id) + n = int(job.n_gpus) + dest = self._dest(site, n, free, blocked_dest) if (jv.flexible and jv.status == "running") else None + if dest is not None: + actions.append(MigrateJob(job_id, dest)) + self._note(t_s, "MigrateJob", job_id, rack, "rack") + free[dest] -= n + free[site] += n + self.last_checkpoint[job_id] = t_s + continue + self._checkpoint_or_pause(t_s, job_id, jv, rack, "rack", actions) + return actions + + def _dest_has_power(self, dest: str, n: int, site_power: Mapping[str, float], site_limit: Mapping[str, float]) -> bool: + return site_power[dest] + self.job_power_w(n) <= site_limit[dest] + + def _checkpoint_or_pause(self, t: float, job_id: str, jv: Any, unit: str, why: str, actions: list) -> None: + st = self.settings + staged = self.staged.get(job_id) + if jv.flexible and staged is not None and t - staged >= st.decision_interval_s - 1e-9 and jv.status in ("running", "ckpt", "restore", "transfer"): + actions.append(PauseJob(job_id)) + self.paused[job_id] = (unit, t) + self._note(t, "PauseJob", job_id, unit, why) + return + if jv.status == "running" and t - self.last_checkpoint.get(job_id, -math.inf) >= st.recheckpoint_s - 1e-9: + actions.append(CheckpointNow(job_id)) + self.last_checkpoint[job_id] = t + if jv.flexible: + self.staged.setdefault(job_id, t) + self._note(t, "CheckpointNow", job_id, unit, why) + + def resumes( + self, + t_s: float, + vs: VisibleState, + rack_active: set[str], + site_active: set[str], + ) -> list[Any]: + """Resume paused jobs whose warning cleared, that faulted, or that hit the pause limit.""" + views = {jv.job_id: jv for jv in vs.jobs} + actions = [] + for job_id, (unit, t0) in sorted(self.paused.items()): + jv = views.get(job_id) + if jv is None: + del self.paused[job_id] + self.staged.pop(job_id, None) + continue + still = unit in rack_active or unit in site_active + faulted = jv.status in ("queued", "restore") + too_long = t_s - t0 >= self.settings.max_pause_s - 1e-9 + if not still or faulted or too_long: + actions.append(ResumeJob(job_id)) + self._note(t_s, "ResumeJob", job_id, unit, "clear" if not still else ("fault" if faulted else "limit")) + del self.paused[job_id] + self.staged.pop(job_id, None) + # staged flags expire when the warning on their unit is over + for job_id in list(self.staged): + if job_id in self.paused: + continue + jv = views.get(job_id) + if jv is None or t_s - self.staged[job_id] > 2 * self.settings.recheckpoint_s: + self.staged.pop(job_id, None) + return actions + + +# --------------------------------------------------------------------------- +# controllers +# --------------------------------------------------------------------------- + + +class _BankController: + """Shared sampling, alarm bookkeeping and export for all outage controllers.""" + + name = "base" + + def __init__(self, settings: OutageSettings) -> None: + self.settings = settings + self.bank: Optional[ChannelBank] = None + self.placement: Optional[PlacementView] = None + self.cfg: Optional[SimConfig] = None + self.pmax_rows: list[np.ndarray] = [] + self.decision_times: list[float] = [] + self.planner: Optional[ResponsePlanner] = None + self.index: Optional[SeriesIndex] = None + + def bind(self, run: _Run, cfg: SimConfig) -> None: + self.cfg = cfg + self.placement = PlacementView(run) + self.bank = ChannelBank(cfg, self.settings, self.placement) + self.index = self.bank.index + self.planner = ResponsePlanner(cfg, self.settings) + self._on_bind() + + def _on_bind(self) -> None: # pragma: no cover - hook + pass + + def decide(self, t_s: float, visible_state: VisibleState) -> Sequence[Any]: # pragma: no cover - overridden + return () + + def export(self) -> dict[str, Any]: + return { + "policy": self.name, + "times_s": [float(t) for t in self.decision_times], + "meta_actions": list(self.planner.meta) if self.planner is not None else [], + } + + +class TraceRecorder(_BankController): + """Takes no action; records the samples that forecasters see and the costs a response would carry.""" + + name = "reactive" + + def _on_bind(self) -> None: + self.rack_checkpoint_gpu_h: list[np.ndarray] = [] + self.rack_flex_move_gpu_h: list[np.ndarray] = [] + self.rack_nonflex_checkpoint_gpu_h: list[np.ndarray] = [] + self.site_flex_move_gpu_h: list[np.ndarray] = [] + self.rack_on_rows: list[np.ndarray] = [] + + def decide(self, t_s, visible_state): + assert self.bank is not None and self.placement is not None and self.cfg is not None and self.index is not None + self.bank.sample(t_s, visible_state) + self.decision_times.append(float(t_s)) + R, S = len(self.index.rack_ids), len(self.index.site_ids) + flex_move = np.zeros(R) + nonflex = np.zeros(R) + site_flex = np.zeros(S) + jobs = {j.job_id: j for j in self.cfg.jobs} + for job_id, racks in self.placement.job_rack_counts().items(): + job = jobs[job_id] + if job.multisite: + continue + c = job_costs(job, self.cfg.transfer) + for rid in racks: + ri = self.index.rack_ids.index(rid) + if job.flexible: + flex_move[ri] += c["move"] + else: + nonflex[ri] += c["checkpoint"] + site = self.index.rack_site[self.index.rack_ids.index(next(iter(racks)))] + if job.flexible: + site_flex[self.index.site_ids.index(site)] += c["move"] + self.rack_flex_move_gpu_h.append(flex_move) + self.rack_nonflex_checkpoint_gpu_h.append(nonflex) + self.site_flex_move_gpu_h.append(site_flex) + self.rack_on_rows.append(np.asarray(visible_state.now["rack_on"], dtype=bool)) + return () + + def trace(self) -> dict[str, Any]: + assert self.bank is not None + return { + "times_s": np.array(self.decision_times), + "samples": self.bank.matrix(), + "site_limits": np.stack(self.bank.site_limits) if self.bank.site_limits else np.zeros((0, 0, 0)), + "rack_flex_move_gpu_h": np.stack(self.rack_flex_move_gpu_h), + "rack_nonflex_checkpoint_gpu_h": np.stack(self.rack_nonflex_checkpoint_gpu_h), + "site_flex_move_gpu_h": np.stack(self.site_flex_move_gpu_h), + } + + +class ForecastWarningController(_BankController): + """Forecast every series, warn on exceedance probability, respond with the planner.""" + + def __init__( + self, + settings: OutageSettings, + forecaster: Any, + threshold: float, + calibration: Optional[Calibration], + name: str, + *, + batch_size: int = 512, + ) -> None: + super().__init__(settings) + self.forecaster = forecaster + self.threshold = float(threshold) + self.calibration = calibration + self.name = name + self.batch_size = batch_size + self.tracker: Optional[EpisodeTracker] = None + + def _on_bind(self) -> None: + assert self.index is not None + self.tracker = EpisodeTracker(len(self.index), self.threshold, self.settings.clear_decisions) + + def decide(self, t_s, visible_state): + assert self.bank is not None and self.placement is not None and self.planner is not None + assert self.index is not None and self.tracker is not None + st = self.settings + self.bank.sample(t_s, visible_state) + self.decision_times.append(float(t_s)) + d = len(self.bank.rows) - 1 + mat = self.bank.matrix() + T = st.context_steps + pmax = np.zeros(len(self.index)) + if d + 1 >= T: + ctx = mat[:, d + 1 - T: d + 1] + limits = self.bank.limits_matrix(self.calibration, d) + p = window_probabilities(self.forecaster, ctx, limits, st.horizon_steps, batch_size=self.batch_size) + pmax = p.max(axis=1) + self.pmax_rows.append(pmax) + active = self.tracker.update(t_s, pmax) + return self._respond(t_s, visible_state, active) + + def _respond(self, t_s: float, vs: VisibleState, active: np.ndarray) -> list[Any]: + assert self.index is not None and self.tracker is not None and self.planner is not None and self.placement is not None + rack_alarms: dict[str, Alarm] = {} + site_alarms: dict[str, Alarm] = {} + for k in np.flatnonzero(active): + unit, ch = self.index.keys[k] + cls = CHANNEL_CLASS[ch] + by_unit = site_alarms if ch in SITE_CHANNELS else rack_alarms + a = by_unit.get(unit) + if a is None: + limit_w = None + if ch in SITE_CHANNELS: + limit_w = float(np.min(self.bank.site_limits[-1][self.index.site_ids.index(unit)])) + by_unit[unit] = Alarm(unit, (cls,), self.tracker.since(int(k)), limit_w=limit_w) + else: + a.classes = tuple(sorted(set(a.classes) | {cls})) + a.since_s = min(a.since_s, self.tracker.since(int(k))) + actions = list(self.planner.resumes(t_s, vs, set(rack_alarms), set(site_alarms))) + actions += self.planner.plan(t_s, vs, self.placement.job_rack_counts(), rack_alarms, site_alarms, self.placement.job_at) + return actions + + def export(self) -> dict[str, Any]: + out = super().export() + assert self.tracker is not None and self.index is not None + out["threshold"] = self.threshold + out["pmax"] = np.stack(self.pmax_rows) if self.pmax_rows else np.zeros((0, len(self.index))) + out["episodes"] = [ + {**e, "unit": self.index.keys[e["k"]][0], "channel": self.index.keys[e["k"]][1], + "class": CHANNEL_CLASS[self.index.keys[e["k"]][1]]} + for e in self.tracker.finish() + ] + return out + + +@dataclass(frozen=True) +class OracleAlarm: + """Alarm of the perfect-foresight upper bound. + + ``acts`` False marks a detection-only alarm (a scheduled event whose + consequences are handled by other alarms); it is exported for scoring but + never drives an action. ``event_s`` is the exact time of the event. + """ + + unit: str + cls: str + start_s: float + end_s: float + slot: Optional[int] = None + event_id: str = "" + limit_w: Optional[float] = None + event_s: Optional[float] = None + gated: bool = False + acts: bool = True + + +def oracle_settings(settings: OutageSettings, control_dt_s: float) -> OutageSettings: + """Settings for the oracle run: decisions every control step and no re-checkpoint spacing.""" + return replace(settings, decision_interval_s=float(control_dt_s), recheckpoint_s=float(control_dt_s)) + + +def oracle_scenario(sim: Mapping[str, Any]) -> dict[str, Any]: + """Simulator scenario with the controller called every control step (for the oracle run).""" + out = copy.deepcopy(dict(sim)) + out["controller_interval_s"] = out["control_dt_s"] + return out + + +def oracle_lead_s(cfg: SimConfig, settings: OutageSettings, ckpt_s: Optional[float] = None) -> float: + """Lead of the oracle: one checkpoint plus ``oracle_extra_lead_steps`` control steps.""" + if ckpt_s is None: + ckpt_s = max((float(j.checkpoint_duration_s) for j in cfg.jobs), default=0.0) + return float(ckpt_s) + settings.oracle_extra_lead_steps * cfg.control_dt_s + + +def oracle_alarms_from_reference( + cfg: SimConfig, + ref_events: Sequence[Mapping[str, Any]], + throttle_eps: Sequence[Mapping[str, Any]], + settings: OutageSettings, +) -> list[OracleAlarm]: + """Alarms of the perfect-foresight upper bound. + + Built from the reactive reference run of the same seed. Every rollback + event of that run (GPU fault, rack trip, thermal shutdown) gets an alarm + whose checkpoint of each affected job starts exactly one checkpoint time + (that job's) plus ``oracle_extra_lead_steps`` control steps before the + event. Scheduled curtailments and throttle episodes get an alarm that + starts one maximum-checkpoint lead before the event. Every scheduled event + also gets a detection-only alarm at that lead so recall is defined. Reads + event times; no policy other than this upper bound may do that. + """ + lead = oracle_lead_s(cfg, settings) + out: list[OracleAlarm] = [] + for e in ref_events: + kind = e["kind"] + t = float(e["t_s"]) + if kind == KIND_HARDWARE_FAULT: + out.append(OracleAlarm(e["rack_id"], CLASS_HARDWARE, t - lead, t + cfg.control_dt_s, int(e["gpu_slot"]), "hardware_fault", + None, t, True)) + elif kind in (KIND_BREAKER_TRIP, "thermal_shutdown"): + cls = CLASS_POWER if kind == KIND_BREAKER_TRIP else CLASS_THERMAL + out.append(OracleAlarm(e["rack_id"], cls, t - lead, t + cfg.control_dt_s, None, kind, None, t, True)) + nominal = {lay.site.site_id: float(lay.site.grid_import_limit_w) for lay in cfg.layouts} + rack_site = {r.rack_id: lay.site.site_id for lay in cfg.layouts for r in lay.racks} + site_racks: dict[str, list[str]] = {} + for r, sid in rack_site.items(): + site_racks.setdefault(sid, []).append(r) + for ev in cfg.events.events: + if ev.kind == KIND_GRID_CURTAILMENT: + out.append(OracleAlarm(ev.site_id, CLASS_CURTAIL, ev.onset_s - lead, ev.end_s, None, ev.event_id, + nominal[ev.site_id] * float(ev.limit_fraction), ev.onset_s, False, True)) + continue + if ev.kind == KIND_COOLING_FAILURE: + units, cls = ([ev.rack_id] if ev.rack_id else site_racks[ev.site_id]), CLASS_COOLING + elif ev.kind == KIND_BREAKER_TRIP: + units, cls = [ev.rack_id], CLASS_POWER + else: + units, cls = [ev.rack_id], CLASS_HARDWARE + end = ev.end_s if ev.kind != KIND_HARDWARE_FAULT else ev.onset_s + cfg.control_dt_s + for u in units: + out.append(OracleAlarm(u, cls, ev.onset_s - lead, end, None, ev.event_id, None, ev.onset_s, False, False)) + for ep in throttle_eps: + out.append(OracleAlarm(ep["rack_id"], CLASS_THERMAL, ep["onset_s"] - lead, ep["end_s"] + cfg.control_dt_s, None, + "thermal_throttle", None, ep["onset_s"], False, True)) + return out + + +class OracleController(_BankController): + """Perfect-foresight upper bound: the planner driven by exact event times (see :func:`oracle_alarms_from_reference`). + + Run it with a simulator scenario whose ``controller_interval_s`` equals the + control step and with ``settings.decision_interval_s`` set to that step, so + it can act at the exact lead. + """ + + def __init__(self, settings: OutageSettings, alarms: Sequence[OracleAlarm], name: str = "perfect_foresight") -> None: + super().__init__(settings) + self.alarms = list(alarms) + self.name = name + + def _on_bind(self) -> None: + assert self.planner is not None and self.cfg is not None + extra = self.settings.oracle_extra_lead_steps * self.cfg.control_dt_s + jobs = self.planner.jobs + + def gate(job_id: str, alarm: Alarm, t_s: float) -> bool: + if alarm.event_s is None or not alarm.gated: + return True + return t_s >= alarm.event_s - (float(jobs[job_id].checkpoint_duration_s) + extra) - 1e-9 + + self.planner.gate = gate + + def decide(self, t_s, visible_state): + assert self.bank is not None and self.placement is not None and self.planner is not None + self.bank.sample(t_s, visible_state) + self.decision_times.append(float(t_s)) + rack_alarms: dict[str, Alarm] = {} + site_alarms: dict[str, Alarm] = {} + for a in self.alarms: + if not a.acts or not (a.start_s <= t_s < a.end_s): + continue + is_site = a.unit in self.planner.site_ids + by_unit = site_alarms if is_site else rack_alarms + cur = by_unit.get(a.unit) + new = Alarm(a.unit, (a.cls,), a.start_s, 1.0, a.slot, a.limit_w, a.event_s, a.gated) + if cur is None: + by_unit[a.unit] = new + else: + cur.classes = tuple(sorted(set(cur.classes) | {a.cls})) + cur.since_s = min(cur.since_s, a.start_s) + if a.slot != cur.slot: + cur.slot = None # two causes on one rack: protect the whole rack + if a.limit_w is not None: + cur.limit_w = a.limit_w if cur.limit_w is None else min(cur.limit_w, a.limit_w) + cur.gated = cur.gated and a.gated + if a.event_s is not None: + cur.event_s = a.event_s if cur.event_s is None else min(cur.event_s, a.event_s) + actions = list(self.planner.resumes(t_s, visible_state, set(rack_alarms), set(site_alarms))) + actions += self.planner.plan(t_s, visible_state, self.placement.job_rack_counts(), rack_alarms, site_alarms, self.placement.job_at) + return actions + + def export(self) -> dict[str, Any]: + out = super().export() + step = self.settings.decision_interval_s + out["episodes"] = [ + {"unit": a.unit, "class": a.cls, "channel": "oracle", "start_s": a.start_s, "end_s": max(a.end_s - step, a.start_s), + "peak_p": 1.0, "n_decisions": 1, "k": -1} + for a in self.alarms + ] + out["threshold"] = None + return out + + +# --------------------------------------------------------------------------- +# open-loop probabilities on a recorded trace +# --------------------------------------------------------------------------- + + +def openloop_pmax( + forecaster: Any, + trace: Mapping[str, Any], + cfg: SimConfig, + settings: OutageSettings, + calibration: Optional[Calibration], + *, + batch_size: int = 512, + perfect: bool = False, +) -> np.ndarray: + """Warning probabilities ``(n_decisions, n_series)`` from a recorded no-action trace. + + Decision ``d`` sees ``samples[:, d - T + 1 : d + 1]``. With ``perfect`` the + realized future of the same trace is used as a zero-spread forecast (an upper + bound for any forecaster on that trace). + """ + index = SeriesIndex.from_config(cfg) + samples = np.asarray(trace["samples"], dtype=float) + n_series, n_dec = samples.shape + T, H = settings.context_steps, settings.horizon_steps + pmax = np.zeros((n_dec, n_series)) + site_limits = np.asarray(trace["site_limits"]) + decisions = list(range(T - 1, n_dec)) + full = decisions + + def limits_at(d: int) -> np.ndarray: + return series_limits(cfg, index, settings, calibration, site_limits[d]) + + if perfect: + from heatwave.forecast.base import QUANTILE_LEVELS + + missing = -1.0e18 + for d in decisions: + fut = np.full((n_series, H), np.nan) + n_av = min(H, n_dec - 1 - d) + if n_av > 0: + fut[:, :n_av] = samples[:, d + 1: d + 1 + n_av] + fut = np.where(np.isfinite(fut), fut, missing) + vals = np.repeat(fut[:, :, None], len(QUANTILE_LEVELS), axis=2) + pmax[d] = exceedance_probability(vals, QUANTILE_LEVELS, limits_at(d)).max(axis=1) + return pmax + + rows_per_batch = max(1, batch_size // n_series) + for start in range(0, len(full), rows_per_batch): + ds = full[start:start + rows_per_batch] + win = np.concatenate([samples[:, d + 1 - T: d + 1] for d in ds], axis=0) + lim = np.concatenate([limits_at(d) for d in ds], axis=0) + p = window_probabilities(forecaster, win, lim, H, batch_size=batch_size) + p = p.reshape(len(ds), n_series, H).max(axis=2) + for j, d in enumerate(ds): + pmax[d] = p[j] + return pmax + + +# --------------------------------------------------------------------------- +# event table and attribution +# --------------------------------------------------------------------------- + + +def throttle_episodes(events: Sequence[Mapping[str, Any]], horizon_s: float, merge_gap_s: float) -> list[dict[str, Any]]: + """Merge the simulator's throttle start/end log into one episode per rack and burst.""" + by_rack: dict[str, list[tuple[float, str]]] = {} + for e in events: + if e["kind"] == KIND_THERMAL_THROTTLE: + by_rack.setdefault(e["rack_id"], []).append((float(e["t_s"]), e["phase"])) + out = [] + for rack, items in sorted(by_rack.items()): + items.sort(key=lambda x: (x[0], 0 if x[1] == "end" else 1)) + spans: list[list[float]] = [] + for t, phase in items: + if phase == "start": + spans.append([t, horizon_s]) + elif spans and spans[-1][1] == horizon_s: + spans[-1][1] = t + merged: list[list[float]] = [] + for a, b in spans: + if merged and a - merged[-1][1] <= merge_gap_s: + merged[-1][1] = max(merged[-1][1], b) + else: + merged.append([a, b]) + for a, b in merged: + out.append({"rack_id": rack, "onset_s": a, "end_s": b}) + out.sort(key=lambda e: (e["onset_s"], e["rack_id"])) + return out + + +def build_event_table( + cfg: SimConfig, + ref_events: Sequence[Mapping[str, Any]], + settings: OutageSettings, +) -> list[dict[str, Any]]: + """Scheduled events plus the throttle episodes of the reactive reference run. + + Only events that start at or after the warm-up are scored. + """ + rack_site = {r.rack_id: lay.site.site_id for lay in cfg.layouts for r in lay.racks} + site_racks: dict[str, list[str]] = {} + for r, s in rack_site.items(): + site_racks.setdefault(s, []).append(r) + rows = [] + for gt in cfg.events.ground_truth(): + kind = gt["kind"] + rack = gt["rack_id"] + if kind == KIND_GRID_CURTAILMENT: + units = [gt["site_id"]] + elif rack is None: + units = list(site_racks[gt["site_id"]]) + else: + units = [rack] + slot = None + for ev in cfg.events.events: + if ev.event_id == gt["event_id"]: + slot = getattr(ev, "gpu_slot", None) + window_end = gt["end_s"] if kind != KIND_HARDWARE_FAULT else gt["onset_s"] + rows.append( + { + "event_id": gt["event_id"], "kind": kind, "site_id": gt["site_id"], "rack_id": rack, "units": units, + "gpu_slot": slot, "onset_s": float(gt["onset_s"]), "end_s": float(gt["end_s"]), + "window_end_s": float(window_end), "has_precursor": bool(gt["has_precursor"]), + "precursor_lead_s": float(gt["precursor_lead_s"]), "mode": gt.get("mode"), "source": "scheduled", + } + ) + for i, ep in enumerate(throttle_episodes(ref_events, cfg.horizon_s, settings.episode_merge_gap_s)): + rows.append( + { + "event_id": f"throttle-{i:03d}-{ep['rack_id']}", "kind": KIND_THERMAL_THROTTLE, "site_id": rack_site[ep["rack_id"]], + "rack_id": ep["rack_id"], "units": [ep["rack_id"]], "gpu_slot": None, "onset_s": float(ep["onset_s"]), + "end_s": float(ep["end_s"]), "window_end_s": float(ep["end_s"]), "has_precursor": False, + "precursor_lead_s": 0.0, "mode": None, "source": "emergent", + } + ) + rows = [r for r in rows if r["onset_s"] >= settings.warmup_s - 1e-9] + rows.sort(key=lambda r: (r["onset_s"], r["event_id"])) + return rows + + +_RE_GPU = re.compile(r"^gpu fault (\S+)/(\d+)$") +_RE_RACK = re.compile(r"^rack (\S+) off \((.+)\)$") + + +def attribute_faults( + events: Sequence[Mapping[str, Any]], + table: Sequence[Mapping[str, Any]], + jobs: Mapping[str, Any], + control_dt_s: float, +) -> dict[str, Any]: + """Assign each rollback in a run's event log to a scheduled event, or to ``unattributed``. + + Rules: a GPU fault goes to the hardware fault on that rack and slot at that + time; a forced rack trip to the forced breaker event; an overload trip to the + breaker derate event active on that rack; a thermal shutdown to the cooling + failure that covers the rack. Windows extend from the precursor start to + one hour past the event end. Rollback in the log carries steps; GPU-hours + use the job's step time and shard size. + """ + per: dict[str, dict[str, float]] = {r["event_id"]: {"lost_steps": 0.0, "lost_gpu_h": 0.0, "n_faults": 0} for r in table} + unattr = {"lost_steps": 0.0, "lost_gpu_h": 0.0, "n_faults": 0} + sched = [r for r in table if r["source"] == "scheduled"] + # the simulator logs the rack trip or GPU fault first, then the job faults of the same time + for e in events: + if e["kind"] != "job_fault": + continue + job = jobs[e["job_id"]] + shard = job.n_gpus // (2 if job.multisite else 1) + gpu_h = float(e["lost_steps"]) * float(job.step_time_s) * shard / 3600.0 + t = float(e["t_s"]) + reason = str(e["reason"]) + chosen = None + m = _RE_GPU.match(reason) + if m: + rack, slot = m.group(1), int(m.group(2)) + for r in sched: + if r["kind"] == KIND_HARDWARE_FAULT and r["rack_id"] == rack and r["gpu_slot"] == slot and r["onset_s"] - 1e-6 <= t <= r["onset_s"] + control_dt_s + 1e-6: + chosen = r + else: + m = _RE_RACK.match(reason) + if m: + rack, why = m.group(1), m.group(2) + if why == "forced": + for r in sched: + if r["kind"] == KIND_BREAKER_TRIP and r["rack_id"] == rack and r["mode"] == "forced" and r["onset_s"] - 1e-6 <= t <= r["onset_s"] + control_dt_s + 1e-6: + chosen = r + elif why == "overload": + chosen = _latest(sched, t, lambda r: r["kind"] == KIND_BREAKER_TRIP and r["mode"] == "derate" and r["rack_id"] == rack, 3600.0) + else: + chosen = _latest(sched, t, lambda r: r["kind"] == KIND_COOLING_FAILURE and rack in r["units"], 3600.0) + bucket = unattr if chosen is None else per[chosen["event_id"]] + bucket["lost_steps"] += float(e["lost_steps"]) + bucket["lost_gpu_h"] += gpu_h + bucket["n_faults"] += 1 + return {"per_event": per, "unattributed": unattr} + + +def _latest(rows: Sequence[Mapping[str, Any]], t: float, pred: Callable[[Mapping[str, Any]], bool], slack_s: float) -> Optional[Mapping[str, Any]]: + best = None + for r in rows: + if not pred(r): + continue + start = r["onset_s"] - max(r["precursor_lead_s"], 0.0) + if start - 1e-6 <= t <= r["end_s"] + slack_s: + if best is None or r["onset_s"] > best["onset_s"]: + best = r + return best + + +def curtailment_violation(res: SimResult, event: Mapping[str, Any]) -> dict[str, float]: + """Energy (kWh) and seconds above the effective import limit at the event's site during its window.""" + run = res._run + s = run.site_ids.index(event["site_id"]) + K = run.K + eff = run.site_import[s] * np.repeat(run.signals.import_limit_factor[:, s], K) + power = res.site_power_w[:, s] + t = np.arange(power.size) * run.dt + m = (t >= event["onset_s"] - 1e-9) & (t < event["end_s"] + 1e-9) + over = np.maximum(power[m] - eff[m], 0.0) + return {"violation_kwh": float(over.sum() * run.dt / 3.6e6), "violation_s": float((over > 0).sum() * run.dt)} + + +def throttle_slowdown_gpu_h(res: SimResult, cfg: SimConfig, rack_id: str, t0: float, t1: float, tail_s: float) -> float: + """Approximate GPU-hours of throttled operation (clock shortfall of the rack's hottest GPU). + + Counts the fraction of the rack's GPUs below the throttle threshold clock + times the shortfall of the clock at the rack's hottest GPU. An approximation: it + includes idle GPUs and uses the hottest GPU for all throttled ones. + """ + run = res._run + r = run.rack_ids.index(rack_id) + times = np.arange(run.Nb) * run.cdt + m = (times >= t0 - 1e-9) & (times < t1 + tail_s) + frac = run.rack_throttled_b[m, r] + hot = np.where(np.isfinite(run.rack_maxgpu_b[m, r]), run.rack_maxgpu_b[m, r], -np.inf) + shortfall = 1.0 - cfg.throttle.clock_fraction(hot) + return float(np.sum(frac * run.rack_n[r] * shortfall) * run.cdt / 3600.0) + + +# --------------------------------------------------------------------------- +# response costs +# --------------------------------------------------------------------------- + + +def response_costs( + res: SimResult, jobs: Mapping[str, Any], cfg: SimConfig, meta_actions: Sequence[Mapping[str, Any]] +) -> list[dict[str, Any]]: + """GPU-hours spent by every applied controller action, joined to the controller's unit tag. + + Checkpoint: the job's GPUs stall for its checkpoint duration. Migration: + checkpoint stall plus WAN transfer and restore (transfer and restore are + counted only when the simulator logged ``migration_start``). Pause: the job's + GPUs sit idle from the pause to the resume, to the job's next fault, or to + the end of the run. All from the simulator's own transfer parameters. + """ + tr = cfg.transfer + horizon = cfg.horizon_s + ev = res.events + meta_by_key: dict[tuple[float, str, str], str] = {} + for m in meta_actions: + meta_by_key[(float(m["t_s"]), m["action"], m["job_id"])] = m["unit"] + started: dict[str, list[float]] = {} + faults: dict[str, list[float]] = {} + for e in ev: + if e["kind"] == "migration_start": + started.setdefault(e["job_id"], []).append(float(e["t_s"])) + elif e["kind"] == "job_fault": + faults.setdefault(e["job_id"], []).append(float(e["t_s"])) + pending_pause: dict[str, tuple[float, str]] = {} + out = [] + for a in res.actions: + name, t, status = a["action"], float(a["t_s"]), a["status"] + job_id = a["detail"].get("job_id") + unit = meta_by_key.get((t, name, job_id), "") + if status != "applied": + out.append({"t_s": t, "action": name, "job_id": job_id, "unit": unit, "applied": False, "gpu_h": 0.0}) + continue + job = jobs[job_id] + n = float(job.n_gpus) + if name == "CheckpointNow": + cost = n * float(job.checkpoint_duration_s) / 3600.0 + elif name == "MigrateJob": + cost = n * float(job.checkpoint_duration_s) / 3600.0 + window = float(job.checkpoint_duration_s) + cfg.control_dt_s + 1.0 + if any(0.0 <= s0 - t <= window for s0 in started.get(job_id, [])): + cost += n * (float(tr.transfer_s(int(job.n_gpus))) + float(tr.restore_s(int(job.n_gpus)))) / 3600.0 + elif name == "PauseJob": + pending_pause[job_id] = (t, unit) + continue + elif name == "ResumeJob": + if job_id in pending_pause: + t0, u0 = pending_pause.pop(job_id) + end = t + nxt = [f for f in faults.get(job_id, []) if f > t0] + if nxt: + end = min(end, min(nxt)) + out.append({"t_s": t0, "action": "PauseJob", "job_id": job_id, "unit": u0, "applied": True, "gpu_h": n * max(end - t0, 0.0) / 3600.0}) + continue + else: + continue + out.append({"t_s": t, "action": name, "job_id": job_id, "unit": unit, "applied": True, "gpu_h": cost}) + for job_id, (t0, u0) in pending_pause.items(): + job = jobs[job_id] + end = horizon + nxt = [f for f in faults.get(job_id, []) if f > t0] + if nxt: + end = min(end, min(nxt)) + out.append({"t_s": t0, "action": "PauseJob", "job_id": job_id, "unit": u0, "applied": True, "gpu_h": float(job.n_gpus) * max(end - t0, 0.0) / 3600.0}) + out.sort(key=lambda r: (r["t_s"], r["action"], str(r["job_id"]))) + return out + + +# --------------------------------------------------------------------------- +# alarms, matching, scoring +# --------------------------------------------------------------------------- + + +def merge_alarms(episodes: Sequence[Mapping[str, Any]], merge_gap_s: float) -> list[dict[str, Any]]: + """Merge class episodes of the same unit that overlap or lie within ``merge_gap_s``.""" + by_unit: dict[str, list[Mapping[str, Any]]] = {} + for e in episodes: + by_unit.setdefault(e["unit"], []).append(e) + out = [] + for unit, items in sorted(by_unit.items()): + items = sorted(items, key=lambda e: e["start_s"]) + cur = None + for e in items: + if cur is not None and e["start_s"] <= cur["end_s"] + merge_gap_s: + cur["end_s"] = max(cur["end_s"], e["end_s"]) + cur["classes"].add(e["class"]) + cur["episodes"].append(e) + cur["peak_p"] = max(cur["peak_p"], e["peak_p"]) + else: + if cur is not None: + out.append(cur) + cur = {"unit": unit, "start_s": e["start_s"], "end_s": e["end_s"], "classes": {e["class"]}, "episodes": [e], "peak_p": e["peak_p"]} + if cur is not None: + out.append(cur) + out.sort(key=lambda a: (a["start_s"], a["unit"])) + return out + + +def match_events( + table: Sequence[Mapping[str, Any]], + episodes: Sequence[Mapping[str, Any]], + settings: OutageSettings, + min_lead_s: float = 0.0, +) -> dict[str, dict[str, Any]]: + """Per event: earliest covering warning (lead time), or the first late warning. + + A class episode covers an event if its unit is one of the event's units, its + class may explain the event kind, it starts at or before the onset and the + onset is no later than its end plus the warning horizon. The lead time is + onset minus the episode start. A warning that first appears after the + onset but within one warning horizon is recorded as late. + """ + L = settings.horizon_s + out: dict[str, dict[str, Any]] = {} + for ev in table: + classes = EVENT_CLASSES[ev["kind"]] + cand = [e for e in episodes if e["unit"] in ev["units"] and e["class"] in classes] + covering = [e for e in cand if e["start_s"] <= ev["onset_s"] + 1e-9 and ev["onset_s"] <= e["end_s"] + L + 1e-9] + lead = None + if covering: + lead = ev["onset_s"] - min(e["start_s"] for e in covering) + late = None + if lead is None: + after = [e["start_s"] - ev["onset_s"] for e in cand if ev["onset_s"] < e["start_s"] <= ev["onset_s"] + L + 1e-9] + if after: + late = float(min(after)) + out[ev["event_id"]] = { + "caught": lead is not None, + "caught_in_time": lead is not None and lead >= min_lead_s, + "lead_s": None if lead is None else float(lead), + "late_s": late, + } + return out + + +def classify_alarms( + alarms: Sequence[Mapping[str, Any]], table: Sequence[Mapping[str, Any]], settings: OutageSettings +) -> list[dict[str, Any]]: + """True or false for each alarm: true if it overlaps the effect window of an event it could explain.""" + L = settings.horizon_s + out = [] + for a in alarms: + explained = None + for ev in table: + if a["unit"] not in ev["units"]: + continue + classes = set(EVENT_CLASSES[ev["kind"]]) + if not (set(a["classes"]) & classes): + continue + w0 = ev["onset_s"] + w1 = ev["window_end_s"] + L + if a["start_s"] <= w1 and a["end_s"] + L >= w0: + explained = ev["event_id"] + break + out.append({"unit": a["unit"], "start_s": a["start_s"], "end_s": a["end_s"], "classes": sorted(a["classes"]), "peak_p": a["peak_p"], + "true": explained is not None, "event_id": explained}) + return out + + +def score_run( + res: SimResult, + cfg: SimConfig, + table: Sequence[Mapping[str, Any]], + export: Mapping[str, Any], + settings: OutageSettings, + reference_useful_gpu_h: float, +) -> dict[str, Any]: + """Outcomes of one policy run on one seed (all simulated).""" + jobs = {j.job_id: j for j in cfg.jobs} + episodes = [e for e in export.get("episodes", []) if e["start_s"] >= 0.0] + ckpt_max = max((float(j.checkpoint_duration_s) for j in cfg.jobs), default=0.0) + min_lead = ckpt_max + cfg.control_dt_s + matches = match_events(table, episodes, settings, min_lead) + attr = attribute_faults(res.events, table, jobs, cfg.control_dt_s) + by_event = [] + for ev in table: + row = {k: ev[k] for k in ("event_id", "kind", "site_id", "rack_id", "onset_s", "end_s", "has_precursor", "mode", "source")} + row.update(matches[ev["event_id"]]) + row.update(attr["per_event"][ev["event_id"]]) + row["violation_kwh"] = 0.0 + row["violation_s"] = 0.0 + row["throttle_slowdown_gpu_h"] = 0.0 + if ev["kind"] == KIND_GRID_CURTAILMENT: + row.update(curtailment_violation(res, ev)) + if ev["kind"] == KIND_THERMAL_THROTTLE: + row["throttle_slowdown_gpu_h"] = throttle_slowdown_gpu_h(res, cfg, ev["rack_id"], ev["onset_s"], ev["end_s"], settings.episode_merge_gap_s) + by_event.append(row) + costs = response_costs(res, jobs, cfg, export.get("meta_actions", [])) + alarms = classify_alarms(merge_alarms(episodes, settings.episode_merge_gap_s), table, settings) + for a in alarms: + lo, hi = a["start_s"], a["end_s"] + settings.decision_interval_s * settings.clear_decisions + mine = [c for c in costs if c["unit"] == a["unit"] and lo - 1e-9 <= c["t_s"] <= hi + 1e-9] + a["cost_gpu_h"] = float(sum(c["gpu_h"] for c in mine)) + a["n_actions"] = len(mine) + summary = res.summary() + by_action: dict[str, float] = {} + n_actions: dict[str, int] = {} + rejected = 0 + for c in costs: + if not c["applied"]: + rejected += 1 + continue + by_action[c["action"]] = by_action.get(c["action"], 0.0) + c["gpu_h"] + n_actions[c["action"]] = n_actions.get(c["action"], 0) + 1 + total_cost = float(sum(by_action.values())) + false_cost = float(sum(a["cost_gpu_h"] for a in alarms if not a["true"])) + days = (cfg.horizon_s - settings.warmup_s) / 86400.0 + total_viol = float(np.sum(res.import_violation["energy_above_kwh"])) + return { + "policy": export.get("policy"), + "data_kind": "simulated", + "summary": { + "useful_gpu_hours": summary["useful_gpu_hours"], "lost_gpu_hours": summary["lost_gpu_hours"], + "lost_steps": summary["lost_steps"], "job_faults": summary["job_faults"], "migrations": summary["migrations"], + "import_energy_above_kwh": total_viol, "jobs_done": summary["jobs_done"], "thermal_shutdowns": summary["thermal_shutdowns"], + "breaker_trips": summary["breaker_trips"], "throttle_episodes": summary["throttle_episodes"], + }, + "deficit_gpu_h": float(reference_useful_gpu_h - summary["useful_gpu_hours"]), + "by_event": by_event, + "unattributed": attr["unattributed"], + "alarms": alarms, + "response": { + "gpu_h_by_action": by_action, "n_actions": n_actions, "rejected": rejected, "total_gpu_h": total_cost, + "false_alarm_gpu_h": false_cost, "n_alarms": len(alarms), "n_false_alarms": sum(1 for a in alarms if not a["true"]), + "false_alarms_per_day": sum(1 for a in alarms if not a["true"]) / days, "evaluated_days": days, + }, + "min_lead_s": min_lead, + } + + +# --------------------------------------------------------------------------- +# threshold tuning and sweep on recorded traces +# --------------------------------------------------------------------------- + + +def replay_objective( + table: Sequence[Mapping[str, Any]], + reactive_by_event: Sequence[Mapping[str, Any]], + episodes: Sequence[Mapping[str, Any]], + trace: Mapping[str, Any], + index: SeriesIndex, + settings: OutageSettings, + min_lead_s: float, +) -> dict[str, float]: + """Replay estimate of total loss (GPU-hours) for one warning rule on one recorded trace. + + Misses cost what the reactive run lost on the event (rollback work); every + alarm costs the checkpoint of non-flexible jobs on its rack at its start, + re-done every ``recheckpoint_s``, plus the move of flexible jobs. Events + with no rollback (curtailment, throttle) add no miss cost. This is an + approximation used only to choose a threshold on training seeds; closed-loop + runs report the realized numbers. + """ + matches = match_events(table, episodes, settings, min_lead_s) + lost = {r["event_id"]: float(r["lost_gpu_h"]) for r in reactive_by_event} + miss = float(sum(lost.get(ev["event_id"], 0.0) for ev in table if not matches[ev["event_id"]]["caught_in_time"])) + times = np.asarray(trace["times_s"]) + alarm_cost = 0.0 + for a in merge_alarms(episodes, settings.episode_merge_gap_s): + d0 = int(np.argmin(np.abs(times - a["start_s"]))) + dur = a["end_s"] - a["start_s"] + if a["unit"] in index.rack_ids: + r = index.rack_ids.index(a["unit"]) + alarm_cost += float(trace["rack_flex_move_gpu_h"][d0, r]) + alarm_cost += float(trace["rack_nonflex_checkpoint_gpu_h"][d0, r]) * (1 + math.floor(dur / settings.recheckpoint_s)) + else: + s = index.site_ids.index(a["unit"]) + alarm_cost += float(trace["site_flex_move_gpu_h"][d0, s]) + return {"miss_gpu_h": miss, "alarm_gpu_h": float(alarm_cost), "total_gpu_h": miss + float(alarm_cost)} + + +def detection_stats( + table: Sequence[Mapping[str, Any]], + episodes: Sequence[Mapping[str, Any]], + settings: OutageSettings, + evaluated_days: float, + min_lead_s: float, +) -> dict[str, Any]: + """Recall, false alarms and lead times of one warning rule on one seed (no actions).""" + matches = match_events(table, episodes, settings, min_lead_s) + alarms = classify_alarms(merge_alarms(episodes, settings.episode_merge_gap_s), table, settings) + by_kind: dict[str, dict[str, Any]] = {} + for ev in table: + m = matches[ev["event_id"]] + b = by_kind.setdefault(ev["kind"], {"n": 0, "caught": 0, "caught_in_time": 0, "leads_s": []}) + b["n"] += 1 + b["caught"] += int(m["caught"]) + b["caught_in_time"] += int(m["caught_in_time"]) + if m["lead_s"] is not None: + b["leads_s"].append(m["lead_s"]) + return { + "by_kind": by_kind, + "n_alarms": len(alarms), + "n_false_alarms": sum(1 for a in alarms if not a["true"]), + "false_alarms_per_day": sum(1 for a in alarms if not a["true"]) / evaluated_days, + } + + +# --------------------------------------------------------------------------- +# aggregation with seed-bootstrap intervals +# --------------------------------------------------------------------------- + + +def bootstrap_ci(values: Sequence[float], n_resamples: int, seed: int = 0, stat: Callable[[np.ndarray], float] = np.mean) -> dict[str, Any]: + """Percentile 95 percent interval of ``stat`` over seeds (resampling seeds).""" + v = np.asarray([x for x in values if x is not None and np.isfinite(x)], dtype=float) + if v.size == 0: + return {"mean": None, "lo": None, "hi": None, "n": 0} + rng = np.random.default_rng(seed) + idx = rng.integers(0, v.size, size=(n_resamples, v.size)) + boots = np.array([stat(v[i]) for i in idx]) + return {"mean": float(stat(v)), "lo": float(np.quantile(boots, 0.025)), "hi": float(np.quantile(boots, 0.975)), "n": int(v.size)} + + +def ratio_ci(num: Sequence[float], den: Sequence[float], n_resamples: int, seed: int = 0) -> dict[str, Any]: + """Pooled ratio (sum num / sum den) with a seed-cluster bootstrap interval.""" + n = np.asarray(num, dtype=float) + d = np.asarray(den, dtype=float) + if n.size == 0 or d.sum() == 0: + return {"value": None, "lo": None, "hi": None, "n_seeds": int(n.size), "den": float(d.sum())} + rng = np.random.default_rng(seed) + idx = rng.integers(0, n.size, size=(n_resamples, n.size)) + den_b = d[idx].sum(axis=1) + ok = den_b > 0 + boots = n[idx].sum(axis=1)[ok] / den_b[ok] + return {"value": float(n.sum() / d.sum()), "lo": float(np.quantile(boots, 0.025)), "hi": float(np.quantile(boots, 0.975)), + "n_seeds": int(n.size), "den": float(d.sum())} + + +def pooled_quantile_ci(per_seed: Sequence[Sequence[float]], q: float, n_resamples: int, seed: int = 0) -> dict[str, Any]: + """Quantile of values pooled over seeds, with a seed-cluster bootstrap interval.""" + flat = [x for s in per_seed for x in s] + if not flat: + return {"value": None, "lo": None, "hi": None, "n": 0} + rng = np.random.default_rng(seed) + k = len(per_seed) + boots = [] + for _ in range(n_resamples): + pick = rng.integers(0, k, size=k) + pool = [x for i in pick for x in per_seed[i]] + if pool: + boots.append(np.quantile(pool, q)) + return {"value": float(np.quantile(flat, q)), "lo": float(np.quantile(boots, 0.025)) if boots else None, + "hi": float(np.quantile(boots, 0.975)) if boots else None, "n": len(flat)} + + +def aggregate_runs(runs: Sequence[Mapping[str, Any]], settings: OutageSettings, has_warnings: bool = True) -> dict[str, Any]: + """Pool per-seed ``score_run`` outputs of one policy into overall and per-event-type statistics. + + ``has_warnings`` is False for policies without a warning rule (reactive, + averaging rule alone); their detection fields (recall, lead time, alarm + counts) are reported as ``None`` instead of zero. + """ + B = settings.bootstrap_resamples + out: dict[str, Any] = {"n_seeds": len(runs), "data_kind": "simulated", "has_warnings": bool(has_warnings)} + + def per_seed(fn: Callable[[Mapping[str, Any]], float]) -> list[float]: + return [fn(r) for r in runs] + + out["overall"] = { + "deficit_gpu_h": bootstrap_ci(per_seed(lambda r: r["deficit_gpu_h"]), B, 1), + "useful_gpu_h": bootstrap_ci(per_seed(lambda r: r["summary"]["useful_gpu_hours"]), B, 2), + "lost_gpu_h": bootstrap_ci(per_seed(lambda r: r["summary"]["lost_gpu_hours"]), B, 3), + "lost_steps": bootstrap_ci(per_seed(lambda r: r["summary"]["lost_steps"]), B, 4), + "import_energy_above_kwh": bootstrap_ci(per_seed(lambda r: r["summary"]["import_energy_above_kwh"]), B, 5), + "response_gpu_h": bootstrap_ci(per_seed(lambda r: r["response"]["total_gpu_h"]), B, 6), + "false_alarm_gpu_h": bootstrap_ci(per_seed(lambda r: r["response"]["false_alarm_gpu_h"]), B, 7), + "false_alarms_per_day": bootstrap_ci(per_seed(lambda r: r["response"]["false_alarms_per_day"]), B, 8), + "n_alarms": bootstrap_ci(per_seed(lambda r: r["response"]["n_alarms"]), B, 9), + "job_faults": bootstrap_ci(per_seed(lambda r: r["summary"]["job_faults"]), B, 10), + "migrations": bootstrap_ci(per_seed(lambda r: r["summary"]["migrations"]), B, 11), + } + n_ev = [len(r["by_event"]) for r in runs] + caught = [sum(1 for e in r["by_event"] if e["caught"]) for r in runs] + in_time = [sum(1 for e in r["by_event"] if e["caught_in_time"]) for r in runs] + out["overall"]["recall"] = ratio_ci(caught, n_ev, B, 12) + out["overall"]["recall_in_time"] = ratio_ci(in_time, n_ev, B, 13) + leads = [[e["lead_s"] / 60.0 for e in r["by_event"] if e["lead_s"] is not None] for r in runs] + out["overall"]["lead_minutes"] = { + "median": pooled_quantile_ci(leads, 0.5, B, 14), "p10": pooled_quantile_ci(leads, 0.1, B, 15), + "p90": pooled_quantile_ci(leads, 0.9, B, 16), + } + alarms_true = [sum(1 for a in r["alarms"] if a["true"]) for r in runs] + alarms_all = [len(r["alarms"]) for r in runs] + out["overall"]["alarm_precision"] = ratio_ci(alarms_true, alarms_all, B, 17) + by_type: dict[str, Any] = {} + for kind in EVENT_TYPES: + sel = [[e for e in r["by_event"] if e["kind"] == kind] for r in runs] + n_k = [len(s) for s in sel] + if sum(n_k) == 0: + continue + entry: dict[str, Any] = { + "n_events": int(sum(n_k)), + "n_with_precursor": int(sum(1 for s in sel for e in s if e["has_precursor"])), + "recall": ratio_ci([sum(1 for e in s if e["caught"]) for s in sel], n_k, B, 20), + "recall_in_time": ratio_ci([sum(1 for e in s if e["caught_in_time"]) for s in sel], n_k, B, 21), + "lead_minutes_median": pooled_quantile_ci([[e["lead_s"] / 60.0 for e in s if e["lead_s"] is not None] for s in sel], 0.5, B, 22), + "lost_gpu_h": bootstrap_ci([sum(e["lost_gpu_h"] for e in s) for s in sel], B, 23), + "lost_steps": bootstrap_ci([sum(e["lost_steps"] for e in s) for s in sel], B, 24), + } + if kind == KIND_GRID_CURTAILMENT: + entry["violation_kwh"] = bootstrap_ci([sum(e["violation_kwh"] for e in s) for s in sel], B, 25) + entry["violation_s"] = bootstrap_ci([sum(e["violation_s"] for e in s) for s in sel], B, 26) + if kind == KIND_THERMAL_THROTTLE: + entry["throttle_slowdown_gpu_h"] = bootstrap_ci([sum(e["throttle_slowdown_gpu_h"] for e in s) for s in sel], B, 27) + by_type[kind] = entry + out["by_event_type"] = by_type + # events with no precursor + nop = [[e for e in r["by_event"] if not e["has_precursor"] and e["source"] == "scheduled"] for r in runs] + n_nop = [len(s) for s in nop] + out["no_precursor"] = { + "n_events": int(sum(n_nop)), + "lost_gpu_h": bootstrap_ci([sum(e["lost_gpu_h"] for e in s) for s in nop], B, 30), + "lost_steps": bootstrap_ci([sum(e["lost_steps"] for e in s) for s in nop], B, 31), + "recall": ratio_ci([sum(1 for e in s if e["caught"]) for s in nop], n_nop, B, 32), + "lost_gpu_h_per_event": (float(sum(e["lost_gpu_h"] for s in nop for e in s) / max(sum(n_nop), 1))), + } + prec = [[e for e in r["by_event"] if e["has_precursor"] and e["source"] == "scheduled"] for r in runs] + n_p = [len(s) for s in prec] + out["with_precursor"] = { + "n_events": int(sum(n_p)), + "lost_gpu_h": bootstrap_ci([sum(e["lost_gpu_h"] for e in s) for s in prec], B, 33), + "recall": ratio_ci([sum(1 for e in s if e["caught"]) for s in prec], n_p, B, 34), + "lost_gpu_h_per_event": (float(sum(e["lost_gpu_h"] for s in prec for e in s) / max(sum(n_p), 1))), + } + out["unattributed_lost_gpu_h"] = bootstrap_ci([r["unattributed"]["lost_gpu_h"] for r in runs], B, 35) + if not has_warnings: + for key in ("recall", "recall_in_time", "lead_minutes", "alarm_precision", "false_alarms_per_day", "false_alarm_gpu_h", "n_alarms"): + out["overall"][key] = None + for entry in by_type.values(): + for key in ("recall", "recall_in_time", "lead_minutes_median"): + entry[key] = None + out["no_precursor"]["recall"] = None + out["with_precursor"]["recall"] = None + return out + + +def paired_difference(a: Sequence[float], b: Sequence[float], n_resamples: int, seed: int = 0) -> dict[str, Any]: + """Mean of ``a - b`` over seeds (paired) with a bootstrap interval.""" + d = np.asarray(a, dtype=float) - np.asarray(b, dtype=float) + return bootstrap_ci(list(d), n_resamples, seed) + + +def file_sha256(path: Path | str) -> str: + h = hashlib.sha256() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + return h.hexdigest() + + +__all__ = [ + "Alarm", "Calibration", "ChannelBank", "EpisodeTracker", "ForecastWarningController", "OracleAlarm", "OracleController", + "OutageRun", "OutageSettings", "PersistenceForecaster", "PlacementView", "ResponsePlanner", "SeriesIndex", "TraceRecorder", + "aggregate_runs", "attribute_faults", "bootstrap_ci", "build_event_table", "calibrate", "classify_alarms", + "curtailment_violation", "deep_merge", "detection_stats", "episodes_from_pmax", "exceedance_probability", "job_costs", + "load_outage_scenario", "match_events", "oracle_scenario", "oracle_settings", "merge_alarms", "openloop_pmax", "oracle_alarms_from_reference", + "paired_difference", "periodic_local_jobs", "replay_objective", "response_costs", "run_policy", "score_run", + "seed_scenario", "series_limits", "throttle_episodes", "throttle_slowdown_gpu_h", "window_probabilities", +] diff --git a/tests/test_research_heatwave_outage.py b/tests/test_research_heatwave_outage.py new file mode 100644 index 0000000..a0c4c6e --- /dev/null +++ b/tests/test_research_heatwave_outage.py @@ -0,0 +1,286 @@ +"""Tests for the Heatwave outage-response module. + +Every run here is simulated. Horizons are short and job lists tiny; the full +experiment lives in ``experiments/e003-heatwave/run_outage.py``. Expected +values are computed from the configuration the simulator builds, never typed +in as power or checkpoint numbers. +""" + +from __future__ import annotations + +import copy +from dataclasses import replace +from pathlib import Path + +import numpy as np +import pytest + +from gpu_stack.research.heatwave_events import GridCurtailment, HardwareFault, Precursor +from gpu_stack.research.heatwave_outage import ( + EpisodeTracker, + ForecastWarningController, + OracleAlarm, + OracleController, + OutageSettings, + PersistenceForecaster, + TraceRecorder, + aggregate_runs, + bootstrap_ci, + build_event_table, + episodes_from_pmax, + exceedance_probability, + job_costs, + load_outage_scenario, + oracle_alarms_from_reference, + oracle_scenario, + oracle_settings, + periodic_local_jobs, + ratio_ci, + run_policy, + score_run, + seed_scenario, +) +from gpu_stack.research.heatwave_sim import Job, build_config + +ROOT = Path(__file__).resolve().parents[1] +SCENARIO = ROOT / "experiments" / "e003-heatwave" / "outage-scenario-v1.json" + +LEVELS = np.linspace(0.05, 0.95, 21) + + +def outage_and_settings(): + outage = load_outage_scenario(SCENARIO) + return outage, OutageSettings.from_scenario(outage) + + +def make_job(job_id="j1", n=8, start=0.0, duration=7200.0, step=4.0, ckpt_every=3600.0, ckpt_dur=30.0, **kw) -> Job: + values = dict( + job_id=job_id, n_gpus=n, submit_s=0.0, start_s=start, duration_s=duration, step_time_s=step, + checkpoint_every_s=ckpt_every, checkpoint_duration_s=ckpt_dur, value_per_gpu_hour=2.0, flexible=False, site_id="site-a", + ) + values.update(kw) + return Job(**values) + + +def tiny_scenario(outage, horizon_s, jobs, events=()): + sc = copy.deepcopy(outage["sim"]) + sc["horizon_s"] = horizon_s + sc["events"] = [e.to_dict() for e in events] + sc["jobs"] = {"list": [j.to_dict() for j in jobs]} + sc["controller_interval_s"] = 60.0 + return sc + + +def fast_settings(S: OutageSettings) -> OutageSettings: + return replace(S, decision_interval_s=60.0, context_steps=12, horizon_steps=6, warmup_s=0.0, recheckpoint_s=300.0) + + +# --------------------------------------------------------------------------- +# scenario and seeds +# --------------------------------------------------------------------------- + + +def test_scenario_is_labeled_simulated_and_loads(): + outage, S = outage_and_settings() + assert outage["data_kind"] == "simulated" + assert outage["sim"]["data_kind"] == "simulated" + assert set(outage["seeds"]["train"]).isdisjoint(outage["seeds"]["test"]) + assert S.horizon_s == S.horizon_steps * S.decision_interval_s + assert outage["sim"]["controller_interval_s"] == S.decision_interval_s + + +def test_seed_scenario_is_deterministic_and_event_free_variant_is_empty(): + outage, S = outage_and_settings() + a, b = seed_scenario(outage, 201), seed_scenario(outage, 201) + assert a == b + assert seed_scenario(outage, 202)["events"] != a["events"] + assert a["events"], "the generator draws events" + assert all(e["onset_s"] >= S.warmup_s for e in a["events"]) + assert seed_scenario(outage, 201, with_events=False)["events"] == [] + assert a["jobs"]["generate"]["seed"] != seed_scenario(outage, 202)["jobs"]["generate"]["seed"] + + +def test_periodic_local_jobs_splits_even_single_site_jobs_only(): + even, odd = make_job("e", n=8), make_job("o", n=7) + already = make_job("m", n=8, multisite=True, site_id=None, average_every_steps=4) + out = periodic_local_jobs([even, odd, already]) + assert out[0].multisite and out[0].average_every_steps == 8 and out[0].site_id is None + assert out[1] == odd + assert out[2] == already + + +# --------------------------------------------------------------------------- +# probabilities and warning episodes +# --------------------------------------------------------------------------- + + +def test_exceedance_probability_from_quantiles(): + values = np.broadcast_to(np.linspace(10.0, 20.0, 21), (3, 4, 21)).copy() + hi = exceedance_probability(values, LEVELS, np.full((3, 4), 25.0)) + lo = exceedance_probability(values, LEVELS, np.full((3, 4), 5.0)) + assert np.allclose(hi, 1.0 - LEVELS[-1]) + assert np.allclose(lo, 1.0 - LEVELS[0]) + mid = np.stack([exceedance_probability(values, LEVELS, np.full((3, 4), x))[0, 0] for x in (11.0, 14.0, 17.0, 19.0)]) + assert np.all(np.diff(mid) < 0) + assert np.isclose(exceedance_probability(values, LEVELS, np.full((3, 4), 15.0))[0, 0], 0.5) + + +def test_exceedance_probability_zero_spread_and_missing_limit(): + flat = np.full((2, 3, 21), 7.0) + assert np.all(exceedance_probability(flat, LEVELS, np.full((2, 3), 6.0)) == 1.0) + assert np.all(exceedance_probability(flat, LEVELS, np.full((2, 3), 8.0)) == 0.0) + assert np.all(exceedance_probability(flat, LEVELS, np.full((2, 3), np.nan)) == 0.0) + with pytest.raises(ValueError): + exceedance_probability(flat, LEVELS[:-1], np.full((2, 3), 1.0)) + + +def test_episode_tracker_hysteresis_and_end_time(): + tr = EpisodeTracker(2, threshold=0.5, clear_decisions=2) + series = [(0, 0.1), (1, 0.6), (2, 0.2), (3, 0.7), (4, 0.2), (5, 0.2), (6, 0.9)] + for t, p in series: + tr.update(float(t), np.array([p, 0.0])) + eps = tr.finish() + assert [(e["k"], e["start_s"], e["end_s"]) for e in eps] == [(0, 1.0, 3.0), (0, 6.0, 6.0)] + assert eps[0]["peak_p"] == pytest.approx(0.7) + assert eps[0]["n_decisions"] == 2 + + +def test_episodes_from_pmax_matches_tracker_and_labels_with_index(): + pm = np.zeros((6, 2)) + pm[2:4, 1] = 0.8 + eps = episodes_from_pmax(np.arange(6) * 60.0, pm, 0.5, 1) + assert len(eps) == 1 and eps[0]["k"] == 1 and eps[0]["start_s"] == 120.0 and eps[0]["end_s"] == 180.0 + + +# --------------------------------------------------------------------------- +# simulator interface +# --------------------------------------------------------------------------- + + +def test_hardware_precursor_is_visible_in_the_faulty_gpu_temperature(): + outage, _ = outage_and_settings() + strength, lead, onset, slot = 6.0, 1200.0, 2400.0, 2 + + def event(with_precursor: bool): + pre = (Precursor("gpu_temp_c", lead, strength),) if with_precursor else () + return HardwareFault("h1", "site-a", "a1", slot, onset, 120.0, precursors=pre) + + class Watch: + def __init__(self): + self.rows = [] + + def decide(self, t_s, vs): + self.rows.append((t_s, np.array(vs.now["gpu_temp_c"], dtype=float))) + return () + + peaks = [] + for flag in (True, False): + sc = tiny_scenario(outage, 3600, [make_job(duration=3600.0)], [event(flag)]) + w = Watch() + _, cfg = run_policy(sc, w, 0) + racks = [r for lay in cfg.layouts for r in lay.racks] + first = sum(r.n_gpus for r in racks[: [r.rack_id for r in racks].index("a1")]) + peaks.append(max(row[first + slot] for t, row in w.rows if t < onset)) + assert peaks[0] - peaks[1] > 0.5 * strength + + +def test_job_costs_follow_the_simulator_configuration(): + outage, _ = outage_and_settings() + cfg = build_config(tiny_scenario(outage, 3600, [make_job(n=8, ckpt_dur=30.0)]), seed=0) + job = cfg.jobs[0] + c = job_costs(job, cfg.transfer) + assert c["checkpoint"] == pytest.approx(job.n_gpus * job.checkpoint_duration_s / 3600.0) + assert c["move"] > c["checkpoint"] + twice = job_costs(replace(job, checkpoint_duration_s=2 * job.checkpoint_duration_s), cfg.transfer) + assert twice["checkpoint"] == pytest.approx(2 * c["checkpoint"]) + + +# --------------------------------------------------------------------------- +# scoring and policies on a tiny simulated window +# --------------------------------------------------------------------------- + + +@pytest.fixture(scope="module") +def faulted(): + """One non-flexible job, a hardware fault without precursor, and a long checkpoint interval.""" + outage, S = outage_and_settings() + S = fast_settings(S) + sc = tiny_scenario(outage, 7200, [make_job(duration=7200.0, ckpt_every=3000.0)], + [HardwareFault("h1", "site-a", "a1", 2, 2900.0, 600.0)]) + rec = TraceRecorder(S) + res, cfg = run_policy(sc, rec, 0) + table = build_event_table(cfg, res.events, S) + ex = rec.export() + ex["episodes"] = [] + ref = float(run_policy(tiny_scenario(outage, 7200, [make_job(duration=7200.0, ckpt_every=3000.0)]), None, 0)[0].summary()["useful_gpu_hours"]) + score = score_run(res, cfg, table, ex, S, ref) + return {"outage": outage, "S": S, "sc": sc, "res": res, "cfg": cfg, "table": table, "score": score, "ref": ref} + + +def test_reactive_loses_work_and_attribution_conserves_it(faulted): + res, score = faulted["res"], faulted["score"] + lost = res.summary()["lost_gpu_hours"] + assert lost > 0.0 + attributed = sum(e["lost_gpu_h"] for e in score["by_event"]) + score["unattributed"]["lost_gpu_h"] + assert attributed == pytest.approx(lost, rel=1e-6) + fault_rows = [e for e in score["by_event"] if e["kind"] == "hardware_fault"] + assert len(fault_rows) == 1 and fault_rows[0]["lost_gpu_h"] > 0.0 and not fault_rows[0]["caught"] + assert score["response"]["n_alarms"] == 0 and score["response"]["total_gpu_h"] == 0.0 + assert score["data_kind"] == "simulated" + + +def test_perfect_foresight_loses_less_than_reactive_and_catches_the_event(faulted): + S, sc, cfg, res, table = faulted["S"], faulted["sc"], faulted["cfg"], faulted["res"], faulted["table"] + So = oracle_settings(S, cfg.control_dt_s) + alarms = oracle_alarms_from_reference(cfg, res.events, [], So) + assert alarms and all(isinstance(a, OracleAlarm) for a in alarms) + ctrl = OracleController(So, alarms) + res_o, cfg_o = run_policy(oracle_scenario(sc), ctrl, 0) + score_o = score_run(res_o, cfg_o, table, ctrl.export(), So, faulted["ref"]) + assert res_o.summary()["lost_gpu_hours"] < 0.5 * res.summary()["lost_gpu_hours"] + assert all(e["caught_in_time"] for e in score_o["by_event"] if e["kind"] == "hardware_fault") + assert score_o["response"]["total_gpu_h"] > 0.0 + + +def test_persistence_warning_on_announced_curtailment_checkpoints_then_resumes(): + outage, S = outage_and_settings() + S = fast_settings(S) + flex = make_job("f", n=16, duration=7200.0, flexible=True, ckpt_every=3000.0) + sc = tiny_scenario(outage, 7200, [flex], [GridCurtailment("g1", "site-a", 1800.0, 900.0, 0.2, announce_lead_s=1500.0)]) + ctrl = ForecastWarningController(S, PersistenceForecaster(), 0.5, None, "persistence") + res, cfg = run_policy(sc, ctrl, 0) + ex = ctrl.export() + site_eps = [e for e in ex["episodes"] if e["class"] == "curtail"] + assert site_eps and site_eps[0]["start_s"] < 1800.0 + applied = [a for a in res.actions if a["status"] == "applied"] + assert applied, "the response ran at least one action" + table = build_event_table(cfg, res.events, S) + score = score_run(res, cfg, table, ex, S, 0.0) + row = [e for e in score["by_event"] if e["kind"] == "grid_curtailment"][0] + assert row["caught"] and row["lead_s"] is not None and row["lead_s"] > 0.0 + + +# --------------------------------------------------------------------------- +# aggregation +# --------------------------------------------------------------------------- + + +def test_bootstrap_helpers_are_deterministic_and_pooled(): + vals = [1.0, 2.0, 3.0, 4.0] + a, b = bootstrap_ci(vals, 200, 3), bootstrap_ci(vals, 200, 3) + assert a == b and a["mean"] == pytest.approx(2.5) and a["lo"] <= a["mean"] <= a["hi"] + r = ratio_ci([1, 2, 3], [2, 4, 6], 200, 1) + assert r["value"] == pytest.approx(0.5) and r["lo"] == pytest.approx(0.5) and r["hi"] == pytest.approx(0.5) + assert bootstrap_ci([], 50)["n"] == 0 + + +def test_aggregate_hides_detection_fields_without_a_warning_rule(faulted): + S = faulted["S"] + runs = [faulted["score"]] * 3 + with_w = aggregate_runs(runs, S, has_warnings=True) + without = aggregate_runs(runs, S, has_warnings=False) + assert with_w["overall"]["recall"]["value"] == 0.0 + assert without["overall"]["recall"] is None and without["has_warnings"] is False + assert without["no_precursor"]["n_events"] == 3 + assert without["no_precursor"]["lost_gpu_h"]["mean"] == pytest.approx(with_w["no_precursor"]["lost_gpu_h"]["mean"]) + assert with_w["data_kind"] == "simulated" From 53ca2964ddcdbb45a0165706f12ffb76546063b3 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:39:36 -0700 Subject: [PATCH 10/52] Packing: build on sim scenario v2, 100 s controller calls, 3 h planning horizon Co-Authored-By: Claude Sonnet --- experiments/e003-heatwave/packing-scenario-v1.json | 10 +++++----- experiments/e003-heatwave/run_packing.py | 2 +- tests/test_research_heatwave_packing.py | 6 +++--- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/experiments/e003-heatwave/packing-scenario-v1.json b/experiments/e003-heatwave/packing-scenario-v1.json index 3319a4e..73412f3 100644 --- a/experiments/e003-heatwave/packing-scenario-v1.json +++ b/experiments/e003-heatwave/packing-scenario-v1.json @@ -2,8 +2,8 @@ "schema": "gpu-stack.e003-heatwave-packing-scenario.v1", "data_kind": "simulated", "scenario_id": "e003-heatwave-packing-v1", - "description": "Power-cap packing episodes on the two-site e003 simulator. Every quantity is simulated; every number below is a scenario assumption (nothing is measured data). Power levels, cooling and thermal parameters are read from sim-scenario-v1.json and the measured e002 ratios it points to; the site caps are computed at run time from a commissioning calibration run.", - "base_scenario": "sim-scenario-v1.json", + "description": "Power-cap packing episodes on the two-site e003 simulator. Every quantity is simulated; every number below is a scenario assumption (nothing is measured data). Power levels, cooling and thermal parameters are read from sim-scenario-v2.json and the measured e002 ratios it points to; the site caps are computed at run time from a commissioning calibration run.", + "base_scenario": "sim-scenario-v2.json", "episode": { "horizon_s": 147600, "warmup_s": 90000, @@ -19,7 +19,7 @@ "start_hour_utc": 13.0 }, "cap": { - "load_fraction": 0.8, + "load_fraction": 0.55, "design_outdoor_c": 35.0, "calibration": { "idle_hours": 1, @@ -30,10 +30,10 @@ "generate": { "utilization_goal": 1.5, "gpu_choices": [ + 4, 8, 16, - 32, - 64 + 32 ], "gpu_weights": [ 3, diff --git a/experiments/e003-heatwave/run_packing.py b/experiments/e003-heatwave/run_packing.py index 755532b..0929988 100644 --- a/experiments/e003-heatwave/run_packing.py +++ b/experiments/e003-heatwave/run_packing.py @@ -40,7 +40,7 @@ from gpu_stack.research.heatwave_sim import load_scenario # noqa: E402 SCENARIO = HERE / "packing-scenario-v1.json" -SIM_SCENARIO = HERE / "sim-scenario-v1.json" +SIM_SCENARIO = HERE / json.loads(SCENARIO.read_text())["base_scenario"] E002_RAW = ROOT / "docs" / "data" / "e002-checkpoint-power-raw-v1.json" PARTS = HERE / "results" / "packing-v1-parts" OUT = HERE / "results" / "packing-v1.json" diff --git a/tests/test_research_heatwave_packing.py b/tests/test_research_heatwave_packing.py index f8d8850..5e48a56 100644 --- a/tests/test_research_heatwave_packing.py +++ b/tests/test_research_heatwave_packing.py @@ -39,7 +39,7 @@ def tiny(): packing["episode"].update({"horizon_s": 10800, "warmup_s": 3600, "min_context_bins": 6, "warm_ratio_bins": 6}) packing["cap"]["calibration"] = {"idle_hours": 1, "full_hours": 1} packing["jobs"]["generate"].update({"duration_range_s": [1800, 7200], "utilization_goal": 1.5}) - sim = load_scenario(E003 / "sim-scenario-v1.json") + sim = load_scenario(E003 / "sim-scenario-v2.json") cal = hp.calibrate_sites(sim, packing) return packing, sim, cal @@ -206,8 +206,8 @@ def test_ratio_bins_match_the_definition(tiny): i = 3 expect = p[i * k:(i + 1) * k, 0].sum() / (ctrl.idle[0] + ctrl.beta[0] * b[i * k:(i + 1) * k, 0]).sum() assert bins[0, i] == pytest.approx(expect) - # the recorded minute means equal the simulator's 10 s samples averaged over the minute - m0 = res.site_power_w[:6, 0].mean() + # the recorded call-interval means equal the simulator's control-step samples averaged over the interval + m0 = res.site_power_w[: int(st.call_s / res.dt), 0].mean() assert p[0, 0] == pytest.approx(m0) From 4b62e940d5afc38d00d1b0baa73d22888c8950dc Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:41:38 -0700 Subject: [PATCH 11/52] Packing: forecast refresh for slow baselines, reduced-run options in the runner Co-Authored-By: Claude Sonnet --- .../e003-heatwave/packing-scenario-v1.json | 9 ++- .../results/packing-v1-parts/calibration.json | 58 +++++++++++++++++++ experiments/e003-heatwave/run_packing.py | 53 ++++++++++++----- gpu_stack/research/heatwave_packing.py | 30 +++++++--- tests/test_research_heatwave_packing.py | 27 +++++++++ 5 files changed, 153 insertions(+), 24 deletions(-) create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/calibration.json diff --git a/experiments/e003-heatwave/packing-scenario-v1.json b/experiments/e003-heatwave/packing-scenario-v1.json index 73412f3..d6de48b 100644 --- a/experiments/e003-heatwave/packing-scenario-v1.json +++ b/experiments/e003-heatwave/packing-scenario-v1.json @@ -191,7 +191,11 @@ "use_covariates": [ "timestral-5M", "timestral-28M" - ] + ], + "forecast_refresh_bins": { + "ets": 12, + "arima": 12 + } }, "learning": { "bandwidths": [ @@ -219,6 +223,7 @@ "Heat wave: each episode draws a heat wave (centre, width, size) added to the sim scenario's diurnal outdoor temperature; episode time 0 is 06:00 local at the first site (start_hour_utc). The event draw rates, precursor probability and ranges are assumptions of the simulation.", "Over-cap penalty (USD per kWh above the site cap) is a scenario parameter; the default and the sweep are assumptions.", "Planning horizon (3 h in 5-minute steps) and the learned policy's surrogate reward window (3 h) are scenario choices.", - "Warm-up (first 25 h of each 41 h episode) uses one shared rule for all forecast policies and is excluded from every reported metric; the evaluation window is the last 16 h." + "Warm-up (first 25 h of each 41 h episode) uses one shared rule for all forecast policies and is excluded from every reported metric; the evaluation window is the last 16 h.", + "ETS and ARIMA refit once per hour of decisions (forecast_refresh_bins = 12) and serve the 11 decisions in between from the same paths shifted by the elapsed steps, to keep their run time down; every other forecaster is called at every decision." ] } diff --git a/experiments/e003-heatwave/results/packing-v1-parts/calibration.json b/experiments/e003-heatwave/results/packing-v1-parts/calibration.json new file mode 100644 index 0000000..0fab626 --- /dev/null +++ b/experiments/e003-heatwave/results/packing-v1-parts/calibration.json @@ -0,0 +1,58 @@ +{ + "provenance": { + "script": "experiments/e003-heatwave/run_packing.py", + "script_sha256": "b2dd63f48e4a9d63a7312b4f2f9a081795a3c933b0fb296883eb8b1ecf59a88e", + "module_sha256": "807a351a876b6c6e7db0acb938eb9b4878e9655efc97a35167564697948ed0f6", + "scenario_sha256": "9dc5763b8bf38a5f375050f6924983c0e8f18ceeb6494ace6eab6d27848a4429", + "git": { + "commit": "53ca2964ddcdbb45a0165706f12ffb76546063b3", + "branch": "hw-pack", + "dirty": "false" + }, + "data_kind": "simulated", + "seed": 9000, + "sim_scenario_sha256": "d7662b1c13bac5870c6c48ea6d1b7e477d906a7655989d02ef6a94be1f8f3e09", + "e002_raw_sha256": "d4095c17409c6cea9de0fa1fe15b85c0957b3619f20c80b6efb508f979a7912e" + }, + "result": { + "data_kind": "simulated", + "design_outdoor_c": 35.0, + "cap_load_fraction": 0.55, + "sites": { + "site-a": { + "site_id": "site-a", + "n_gpus": 104, + "idle_w": 39585.16332307179, + "full_w": 97683.61754063182, + "cap_w": 71539.3131427298, + "beta_w_per_gpu": 558.6389828611541, + "utc_offset_h": -7.0 + }, + "site-b": { + "site_id": "site-b", + "n_gpus": 80, + "idle_w": 30450.125633132066, + "full_w": 75150.40157972567, + "cap_w": 55035.277403758555, + "beta_w_per_gpu": 558.7534493324201, + "utc_offset_h": -5.0 + } + }, + "half_load_check": [ + { + "site_id": "site-a", + "busy_gpus": 52, + "measured_w": 68664.69082728782, + "affine_prediction_w": 68634.3904318518, + "relative_error": -0.0004412805922658555 + }, + { + "site_id": "site-b", + "busy_gpus": 40, + "measured_w": 52802.98472436753, + "affine_prediction_w": 52800.26360642887, + "relative_error": -5.153341147037911e-05 + } + ] + } +} diff --git a/experiments/e003-heatwave/run_packing.py b/experiments/e003-heatwave/run_packing.py index 0929988..7bf574d 100644 --- a/experiments/e003-heatwave/run_packing.py +++ b/experiments/e003-heatwave/run_packing.py @@ -13,7 +13,10 @@ ``t5m-fixed`` runs at q=0.9 for its reference ratio path). Part files go to ``results/packing-v1-parts/`` (each with script hash, git commit and seeds); ``merge`` writes ``results/packing-v1.json`` through ``heatwave.provenance``. -Existing part files are kept, so a stage can be resumed. +Existing part files are kept, so a stage can be resumed. Run heavy stages +under ``nice -n 10``. ETS and ARIMA refit once per hour of decisions (see +``policies.forecast_refresh_bins`` in the scenario); the setting is recorded in +every run's metrics. Every quantity is simulated. The scenario is ``packing-scenario-v1.json``; power levels, cooling and thermal parameters are read from the simulator @@ -151,12 +154,24 @@ def policy_id(forecaster: str, label: str) -> str: return f"{forecaster}/{label}" -def fixed_levels(packing: dict, forecaster: str) -> list: +def fixed_levels(packing: dict, forecaster: str, opts: argparse.Namespace) -> list: if forecaster in packing["policies"]["full_grid_forecasters"]: - return list(hp.QUANTILE_LEVELS) + return list(hp.QUANTILE_LEVELS)[:: max(1, opts.grid_stride)] + if opts.subset_q: + return [float(x) for x in opts.subset_q.split(",")] return list(packing["policies"]["subset_q"]) +def used_seeds(packing: dict, key: str, opts: argparse.Namespace) -> list: + n = {"train": opts.n_train, "test": opts.n_test}[key] + seeds = list(packing["seeds"][key]) + return seeds[:n] if n > 0 else seeds + + +def option_record(opts: argparse.Namespace) -> dict: + return {"n_train": opts.n_train, "n_test": opts.n_test, "grid_stride": opts.grid_stride, "subset_q": opts.subset_q} + + # --------------------------------------------------------------------------- # stages # --------------------------------------------------------------------------- @@ -189,7 +204,7 @@ def run_one(packing, sim, settings, models, seed: int, controller_factory): def stage_collect(args) -> None: packing, sim, settings = setup() models = load_models() - seeds = shard_filter(list(packing["seeds"]["train"]), args.shard) + seeds = shard_filter(used_seeds(packing, "train", args), args.shard) fc = make_forecaster("timestral-5M", packing, args.threads) for seed in seeds: out = PARTS / f"collect-seed{seed}.json" @@ -209,7 +224,7 @@ def factory(ep, site_ids): p_min, b_min = ctrl.minute_records() np.savez_compressed(PARTS / f"collect-seed{seed}.npz", p_min=p_min, b_min=b_min, **sit) write_json(out, {"provenance": part_provenance(seed=seed, role="train-exploration", forecaster="timestral-5M", - policy="random-q"), + policy="random-q", options=option_record(args)), "result": {"metrics": metrics, "wall_s": wall, "n_situations": int(sit["t"].shape[0]), "draws": ep.draws}}) print(f"collect seed {seed}: {sit['t'].shape[0]} situations, wall {wall:.0f}s", flush=True) @@ -220,8 +235,8 @@ def stage_train(args) -> None: models_d = load_models() models = list(models_d.values()) penalties = list(packing["penalty"]["sweep_usd_per_kwh"]) - train_seeds = list(packing["seeds"]["train"]) - val_seeds = set(packing["seeds"]["validation"]) + train_seeds = used_seeds(packing, "train", args) + val_seeds = set(train_seeds[-2:]) parts = {"train": [], "val": []} for seed in train_seeds: z = np.load(PARTS / f"collect-seed{seed}.npz") @@ -264,19 +279,19 @@ def stack(items): "refit_regret_per_value_on_all": final.regret(Xall, Call, Vall), } write_json(MODEL_PATH, {"provenance": part_provenance(train_seeds=train_seeds, validation_seeds=sorted(val_seeds), - penalties=penalties), + penalties=penalties, options=option_record(args)), "result": {"selected": row, "grid": grid, "reference_regrets": ref, "n_rows_train": int(Xtr.shape[0]), "n_rows_validation": int(Xva.shape[0]), "feature_names": hp.FEATURE_NAMES, "model": final.to_dict()}}) print(f"wrote {MODEL_PATH}: selected {row}") -def eval_jobs(packing: dict, group: str) -> list: +def eval_jobs(packing: dict, group: str, opts: argparse.Namespace) -> list: """List of (policy_id, kind, forecaster, level_or_penalty).""" jobs = [] if group in GROUPS: fcn = GROUPS[group] - for q in fixed_levels(packing, fcn): + for q in fixed_levels(packing, fcn, opts): jobs.append((policy_id(fcn, f"q{q:g}"), "fixed", fcn, q)) elif group == "reactive": jobs.append(("reactive", "reactive", None, None)) @@ -293,8 +308,8 @@ def eval_jobs(packing: dict, group: str) -> list: def stage_eval(args) -> None: packing, sim, settings = setup() models = load_models() - seeds = list(packing["seeds"]["test"]) - jobs = [(j, s) for j in eval_jobs(packing, args.group) for s in seeds] + seeds = used_seeds(packing, "test", args) + jobs = [(j, s) for j in eval_jobs(packing, args.group, args) for s in seeds] jobs = shard_filter(jobs, args.shard) cache = {} learned = None @@ -320,13 +335,15 @@ def factory(ep, site_ids): return hp.PackingController( ep.jobs, models, site_ids, settings, ep.hour0_local, mode="forecast", forecaster=cache[fcn], policy=policy, penalty_usd_per_kwh=penalty, use_covariates=use_cov, + forecast_refresh_bins=int(packing["policies"]["forecast_refresh_bins"].get(fcn, 1)), ) ep, ctrl, res, wall = run_one(packing, sim, settings, models, seed, factory) metrics = hp.episode_metrics(res, ctrl, ep, settings) if pid == "timestral-5M/q0.9": np.savez_compressed(PARTS / f"trace-seed{seed}.npz", ratio_bins=ctrl.ratio_bins()) - write_json(out, {"provenance": part_provenance(seed=seed, policy=pid, forecaster=fcn, use_covariates=use_cov), + write_json(out, {"provenance": part_provenance(seed=seed, policy=pid, forecaster=fcn, use_covariates=use_cov, + options=option_record(args)), "result": {"policy": pid, "kind": kind, "seed": seed, "metrics": metrics, "wall_s": wall, "draws": ep.draws}}) print(f"{pid} seed {seed}: gpu_h {metrics['gpu_hours']:.0f} viol {metrics['violation_rate']:.3f} " @@ -350,7 +367,7 @@ def stage_merge(args) -> None: for f in files: rec = json.loads(f.read_text())["result"] runs.setdefault(rec["policy"], {})[rec["seed"]] = rec["metrics"] - seeds = list(packing["seeds"]["test"]) + seeds = sorted({s for by in runs.values() for s in by}) cal = json.loads(CAL_PATH.read_text())["result"] summary = {} for pid, by_seed in sorted(runs.items()): @@ -415,6 +432,8 @@ def stage_merge(args) -> None: "scenario": {k: packing[k] for k in ("episode", "cap", "jobs", "heatwave_draw", "events", "penalty", "reward_window_s", "queue_order", "reactive", "policies", "learning", "bootstrap", "seeds")}, "assumptions": packing["assumptions"], + "run_options": sorted({json.JSONEncoder(sort_keys=True).encode(json.loads(f.read_text())["provenance"].get("options")) for f in files}), + "test_seeds_run": seeds, "calibration": cal, "metric_definitions": { "window": "evaluation window = last (horizon_s - warmup_s) seconds of each episode; warm-up excluded", @@ -433,7 +452,7 @@ def stage_merge(args) -> None: "paired_net_value_difference": paired, "best_fixed_by_penalty": selection_by_penalty, "learned_model": None if model_rec is None else {k: v for k, v in model_rec.items() if k != "model"}, - "training_episodes": [{"seed": c["draws"]["sim_seed"], "n_situations": c["n_situations"]} for c in collect], + "training_episodes": [{"sim_seed": c["draws"]["sim_seed"], "n_situations": c["n_situations"]} for c in collect], "per_seed_metrics": {pid: {str(s): m for s, m in by.items()} for pid, by in sorted(runs.items())}, } from heatwave.provenance import write_result @@ -454,6 +473,10 @@ def main() -> None: ap.add_argument("--group", default="") ap.add_argument("--shard", default="0/1") ap.add_argument("--threads", type=int, default=1) + ap.add_argument("--n-train", type=int, default=0, help="use only the first N training seeds (0 = all)") + ap.add_argument("--n-test", type=int, default=0, help="use only the first N test seeds (0 = all)") + ap.add_argument("--grid-stride", type=int, default=1, help="use every S-th of the 21 levels for full-grid forecasters") + ap.add_argument("--subset-q", default="", help="comma list replacing policies.subset_q") args = ap.parse_args() if args.stage == "calibrate": stage_calibrate() diff --git a/gpu_stack/research/heatwave_packing.py b/gpu_stack/research/heatwave_packing.py index f38d96c..fc23528 100644 --- a/gpu_stack/research/heatwave_packing.py +++ b/gpu_stack/research/heatwave_packing.py @@ -425,7 +425,10 @@ class PackingController: ``mode`` is ``'forecast'`` (a forecaster plus a q policy), ``'oracle'`` (``oracle_ratio`` supplies the realized ratio path, shape ``(S, n_bins)``), - or ``'reactive'`` (measured power, throttling). All jobs are held back + or ``'reactive'`` (measured power, throttling). ``forecast_refresh_bins = k > 1`` + calls the forecaster only every ``k``-th decision (with horizon ``H + k - 1``) + and serves the decisions in between from the same paths, shifted by the + elapsed bins (used for the slow classical baselines). All jobs are held back with ``DeferJob`` the first time they are seen and released with ``AdmitJob``. Every candidate evaluation before ``warmup_s`` uses the same warm-up rule in every forecast mode: a flat ratio equal to the largest of @@ -446,6 +449,7 @@ def __init__( use_covariates: bool = False, oracle_ratio: Optional[np.ndarray] = None, log_situations: bool = False, + forecast_refresh_bins: int = 1, ) -> None: if mode not in ("forecast", "oracle", "reactive"): raise ValueError("mode must be 'forecast', 'oracle' or 'reactive'") @@ -461,6 +465,10 @@ def __init__( self.use_covariates = bool(use_covariates) self.oracle_ratio = None if oracle_ratio is None else np.asarray(oracle_ratio, dtype=float) self.log_situations = bool(log_situations) + if int(forecast_refresh_bins) < 1: + raise ValueError("forecast_refresh_bins must be >= 1") + self.refresh = int(forecast_refresh_bins) + self._cache: Optional[tuple[float, np.ndarray]] = None self.site_ids = tuple(site_ids) self.S = len(self.site_ids) self.models = [models[s] for s in self.site_ids] @@ -561,23 +569,30 @@ def _ratio_paths(self, t: float, vs: VisibleState, bins: np.ndarray) -> np.ndarr return np.repeat(tr[:, idx][:, :, None], N_LEVELS, axis=2) if in_warmup or bins.shape[1] < self.cfg.min_context_bins or self.mode == "oracle": return np.broadcast_to(self._warm_ratio(bins)[:, None, None], (self.S, H, N_LEVELS)).copy() + if self.refresh > 1 and self._cache is not None: + off = int(round((t - self._cache[0]) / self.cfg.bin_s)) + if 0 <= off < self.refresh: + return self._cache[1][:, off:off + H] ctx = bins[:, -self.cfg.context_bins:] + h_call = H + self.refresh - 1 kwargs: dict[str, Any] = {} if self.use_covariates: - kwargs["known_covariates"] = self._covariates(t, vs, ctx.shape[1]) - fc = self.forecaster.forecast(ctx[:, None, :], H, **kwargs) + kwargs["known_covariates"] = self._covariates(t, vs, ctx.shape[1], h_call) + fc = self.forecaster.forecast(ctx[:, None, :], h_call, **kwargs) self.forecast_calls += 1 - vals = np.asarray(fc.values, dtype=float)[:, 0, :, :] - return np.maximum(vals, 0.05) + vals = np.maximum(np.asarray(fc.values, dtype=float)[:, 0, :, :], 0.05) + if self.refresh > 1: + self._cache = (t, vals) + return vals[:, :H] - def _covariates(self, t: float, vs: VisibleState, n_ctx: int) -> np.ndarray: + def _covariates(self, t: float, vs: VisibleState, n_ctx: int, horizon: int) -> np.ndarray: k = int(round(self.cfg.bin_s / self.cfg.call_s)) o = np.asarray(self._o_min) m = o.shape[0] // k past = o[: m * k].reshape(m, k, self.S).mean(axis=1).T[:, -n_ctx:] hs = vs.known_future["hour_start_s"] fc = vs.known_future["outdoor_forecast_c"] - mids = t + (np.arange(self.cfg.horizon_bins) + 0.5) * self.cfg.bin_s + mids = t + (np.arange(horizon) + 0.5) * self.cfg.bin_s future = np.stack([np.interp(mids, hs, fc[:, s]) for s in range(self.S)]) return np.concatenate([past, future], axis=1)[:, None, :] @@ -814,6 +829,7 @@ def episode_metrics(result: Any, controller: PackingController, episode: Episode "mean_power_over_cap": float((w / cap[None, :]).mean()), "energy_kwh": float(w.sum() * dt / 3.6e6), "forecast_calls": int(controller.forecast_calls), + "forecast_refresh_bins": int(controller.refresh), "job_faults": int(sum(r["faults"] for r in rows)), } return out diff --git a/tests/test_research_heatwave_packing.py b/tests/test_research_heatwave_packing.py index 5e48a56..b7519b9 100644 --- a/tests/test_research_heatwave_packing.py +++ b/tests/test_research_heatwave_packing.py @@ -264,3 +264,30 @@ def test_bootstrap_ci(): assert one["lo"] == one["hi"] == 3.0 with pytest.raises(ValueError): hp.bootstrap_ci([]) + + +def test_forecast_refresh_reuses_paths(tiny): + packing, sim, cal = tiny + ep = hp.build_episode(packing, sim, cal["models"], 11) + + class Counting(StubForecaster): + calls = 0 + horizons = [] + + def forecast(self, context, horizon, known_covariates=None, past_covariates=None): + Counting.calls += 1 + Counting.horizons.append(horizon) + return super().forecast(context, horizon) + + runs = {} + for k in (1, 4): + Counting.calls, Counting.horizons = 0, [] + ctrl, st = make_controller(tiny, ep, q=0.9, forecaster=Counting(), forecast_refresh_bins=k) + res = hp.run_episode(ep, ctrl) + m = hp.episode_metrics(res, ctrl, ep, st) + runs[k] = (Counting.calls, list(Counting.horizons), m) + assert runs[4][0] < runs[1][0] + assert set(runs[1][1]) == {st.horizon_bins} and set(runs[4][1]) == {st.horizon_bins + 3} + assert runs[4][2]["forecast_refresh_bins"] == 4 and runs[1][2]["forecast_refresh_bins"] == 1 + with pytest.raises(ValueError): + make_controller(tiny, ep, q=0.9, forecast_refresh_bins=0) From d528ea06f7835ab4445e323b0d163b3c44559b49 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:52:55 -0700 Subject: [PATCH 12/52] Outage runner: Timestral import path, shared open-loop pass, forecast-compute notes Co-Authored-By: Claude Sonnet 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- experiments/e003-heatwave/run_outage.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/experiments/e003-heatwave/run_outage.py b/experiments/e003-heatwave/run_outage.py index 36a7ee3..155adc8 100644 --- a/experiments/e003-heatwave/run_outage.py +++ b/experiments/e003-heatwave/run_outage.py @@ -126,7 +126,7 @@ def make_forecaster(name: str, outage: dict, args: argparse.Namespace): return ETSForecaster(int(cl["ets_season_length"])) if name == "arima": return ARIMAForecaster(tuple(cl["arima_order"])) - from heatwave.forecast import TimestralForecaster + from heatwave.forecast.timestral_fc import TimestralForecaster if name == "ts5m": return TimestralForecaster("5M", threads=args.threads) @@ -214,7 +214,8 @@ def stage_openloop(outage, S, args, work) -> None: names = [n for n in FORECASTERS if n != "finetuned" or finetuned_available(args)] + ["perfect"] for name in names: fc = None - for seed in seeds_for(outage, "all", args): + order = seeds_for(outage, "all", args) + for seed in (order[::-1] if args.reverse else order): path = work / f"pmax_{name}_{seed}.npy" if path.exists(): continue @@ -464,6 +465,8 @@ def stage_report(outage, S, args, work) -> None: "The sweep is open loop on the recorded reactive traces; closed-loop numbers are in policies.", "The perfect-foresight policy reads event times of the reactive run and is an upper bound, not a deployable policy.", "deficit_gpu_h is the useful GPU-hours of the event-free run minus the policy run on the same seed.", + "Forecast compute: every forecaster's open-loop probabilities are computed once per seed on all decision points (no subsampling) and cached; tuning and the threshold sweep reuse that cache.", + "Each forecast-driven policy then runs closed loop once per held-out seed at its tuned threshold; those runs forecast at every decision point because the response changes the trajectory the forecaster sees.", ], "assumptions": outage["assumptions"], } @@ -490,6 +493,7 @@ def main() -> None: ap.add_argument("--policies", nargs="*", default=None, choices=POLICIES) ap.add_argument("--forecasters", nargs="*", default=None, choices=FORECASTERS + ("perfect",)) ap.add_argument("--n-test", type=int, default=0, help="use only the first N held-out seeds (0 = all)") + ap.add_argument("--reverse", action="store_true", help="open-loop pass visits seeds last to first (a second process can share the work)") ap.add_argument("--threads", type=int, default=None) ap.add_argument("--batch-size", type=int, default=512) ap.add_argument("--finetuned-weights", default=None) From a439caf29bad9bf8310c61024c21405d9005b8b8 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:55:47 -0700 Subject: [PATCH 13/52] Packing: train only on situations where the level matters Co-Authored-By: Claude Sonnet --- .../results/packing-v1-parts/calibration.json | 8 +- .../packing-v1-parts/collect-seed100.json | 75 ++++++++++++++++++ .../packing-v1-parts/collect-seed100.npz | Bin 0 -> 424172 bytes .../packing-v1-parts/collect-seed101.json | 75 ++++++++++++++++++ .../packing-v1-parts/collect-seed101.npz | Bin 0 -> 445193 bytes .../packing-v1-parts/collect-seed102.json | 75 ++++++++++++++++++ .../packing-v1-parts/collect-seed102.npz | Bin 0 -> 444290 bytes .../packing-v1-parts/collect-seed103.json | 75 ++++++++++++++++++ .../packing-v1-parts/collect-seed103.npz | Bin 0 -> 418707 bytes .../packing-v1-parts/collect-seed104.json | 75 ++++++++++++++++++ .../packing-v1-parts/collect-seed104.npz | Bin 0 -> 424478 bytes .../packing-v1-parts/collect-seed105.json | 75 ++++++++++++++++++ .../packing-v1-parts/collect-seed105.npz | Bin 0 -> 441688 bytes experiments/e003-heatwave/run_packing.py | 7 +- 14 files changed, 458 insertions(+), 7 deletions(-) create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed100.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed100.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed101.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed101.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed102.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed102.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed103.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed103.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed104.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed104.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed105.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed105.npz diff --git a/experiments/e003-heatwave/results/packing-v1-parts/calibration.json b/experiments/e003-heatwave/results/packing-v1-parts/calibration.json index 0fab626..50d0521 100644 --- a/experiments/e003-heatwave/results/packing-v1-parts/calibration.json +++ b/experiments/e003-heatwave/results/packing-v1-parts/calibration.json @@ -1,11 +1,11 @@ { "provenance": { "script": "experiments/e003-heatwave/run_packing.py", - "script_sha256": "b2dd63f48e4a9d63a7312b4f2f9a081795a3c933b0fb296883eb8b1ecf59a88e", - "module_sha256": "807a351a876b6c6e7db0acb938eb9b4878e9655efc97a35167564697948ed0f6", - "scenario_sha256": "9dc5763b8bf38a5f375050f6924983c0e8f18ceeb6494ace6eab6d27848a4429", + "script_sha256": "5290bbc8759befafe8fa63f93c10a9566b87fa53b7ba79a1d6137a51e643c94c", + "module_sha256": "d4e21491b179416e52d04f620cfaffdd931c658ad702b0e348eb05747de48a79", + "scenario_sha256": "5b7f96ac79b8caa7eb84b4d93849c9046c8b8737f9ac871ecc3ccd1c9b0cee73", "git": { - "commit": "53ca2964ddcdbb45a0165706f12ffb76546063b3", + "commit": "4b62e940d5afc38d00d1b0baa73d22888c8950dc", "branch": "hw-pack", "dirty": "false" }, diff --git 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b/experiments/e003-heatwave/run_packing.py index 7bf574d..dfe4d3a 100644 --- a/experiments/e003-heatwave/run_packing.py +++ b/experiments/e003-heatwave/run_packing.py @@ -247,9 +247,10 @@ def stage_train(args) -> None: for pi, lam in enumerate(penalties): X = sit["x"].copy() X[:, hp.FEATURE_NAMES.index("log_penalty")] = np.log(lam) - X_all.append(X[usable]) - C_all.append(costs[pi][usable]) - V_all.append(value[usable]) + keep = usable & (costs[pi].max(axis=1) > 0.0) # rows where every level has equal reward carry no signal + X_all.append(X[keep]) + C_all.append(costs[pi][keep]) + V_all.append(value[keep]) parts["val" if seed in val_seeds else "train"].append((np.concatenate(X_all), np.concatenate(C_all), np.concatenate(V_all))) def stack(items): From 3ea9085a46bc4265a3056fa69271ad8735245992 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 14:56:20 -0700 Subject: [PATCH 14/52] Packing: wider learning grid, validation seeds are the last two training seeds used Co-Authored-By: Claude Sonnet --- .../e003-heatwave/packing-scenario-v1.json | 14 +- .../results/packing-v1-parts/model.json | 6399 +++++++++++++++++ 2 files changed, 6405 insertions(+), 8 deletions(-) create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/model.json diff --git a/experiments/e003-heatwave/packing-scenario-v1.json b/experiments/e003-heatwave/packing-scenario-v1.json index d6de48b..3623cc3 100644 --- a/experiments/e003-heatwave/packing-scenario-v1.json +++ b/experiments/e003-heatwave/packing-scenario-v1.json @@ -158,10 +158,6 @@ 107, 108 ], - "validation": [ - 107, - 108 - ], "test": [ 500, 501, @@ -199,14 +195,16 @@ }, "learning": { "bandwidths": [ - 0.5, 1.0, - 2.0 + 2.0, + 4.0, + 8.0 ], "ridges": [ - 1.0, 10.0, - 100.0 + 100.0, + 1000.0, + 10000.0 ], "n_rff": 256, "model_seed": 0 diff --git a/experiments/e003-heatwave/results/packing-v1-parts/model.json b/experiments/e003-heatwave/results/packing-v1-parts/model.json new file mode 100644 index 0000000..478eae6 --- /dev/null +++ b/experiments/e003-heatwave/results/packing-v1-parts/model.json @@ -0,0 +1,6399 @@ +{ + "provenance": { + "script": "experiments/e003-heatwave/run_packing.py", + "script_sha256": "775824294538dd7090bef77219f8ffc4235574ac1dc67d03c379609abd423248", + "module_sha256": "d4e21491b179416e52d04f620cfaffdd931c658ad702b0e348eb05747de48a79", + "scenario_sha256": "5b7f96ac79b8caa7eb84b4d93849c9046c8b8737f9ac871ecc3ccd1c9b0cee73", + "git": { + "commit": "a439caf29bad9bf8310c61024c21405d9005b8b8", + "branch": "hw-pack", + "dirty": "false" + }, + "data_kind": "simulated", + "train_seeds": [ + 100, + 101, + 102, + 103, + 104, + 105 + ], + "validation_seeds": [ + 104, + 105 + ], + "penalties": [ + 2.5, + 10.0, + 40.0, + 160.0 + ], + "options": { + "n_train": 6, + "n_test": 3, + "grid_stride": 1, + "subset_q": "" + } + }, + "result": { + "selected": { + "bandwidth": 2.0, + "ridge": 100.0, + "val_regret_per_value": 0.049005610695333296, + "train_regret_per_value": 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+++ b/experiments/e003-heatwave/results/outage-v1.json @@ -0,0 +1,10570 @@ +{ + "provenance": { + "script": "experiments/e003-heatwave/run_outage.py", + "script_sha256": "9818fe26aca171c6560032687b019a6747337e3440ef37defb2d34a966f98e5a", + "git": { + "commit": "d528ea06f7835ab4445e323b0d163b3c44559b49", + "branch": "hw-outage", + "dirty": "false" + }, + "inputs": { + "experiments/e003-heatwave/outage-scenario-v1.json": "c22ef070df0265032689f2641c7639dfce9641bc034c833636eead8faf5bada9", + "experiments/e003-heatwave/sim-scenario-v2.json": "d7662b1c13bac5870c6c48ea6d1b7e477d906a7655989d02ef6a94be1f8f3e09" + }, + "data_kind": "simulated", + "created_utc": "2026-10-26T22:20:30+00:00", + "notes": "Held-out seeds; thresholds tuned on training seeds; all outputs simulated.", + "module_sha256": "ffbac7097d79db87b4a3f38acc79f6d51175f6671556958121c3cefe0051a576", + "seeds": { + "train": [ + 101, + 102, + 103, + 104 + ], + "test": [ + 201, + 202, + 203, + 204, + 205, + 206, + 207, + 208, + 209, + 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10 + } + }, + "ts28m": { + "lost_gpu_h_minus_reactive": { + "mean": -7.523493705975843, + "lo": -19.980575117133135, + "hi": 3.1516455143283393, + "n": 10 + }, + "lost_steps_minus_reactive": { + "mean": -360.2919033827528, + "lo": -938.4724060211199, + "hi": 61.67158746171825, + "n": 10 + }, + "deficit_gpu_h_minus_reactive": { + "mean": -4.451214533094822, + "lo": -18.160381600437194, + "hi": 3.749003261889198, + "n": 10 + }, + "import_energy_above_kwh_minus_reactive": { + "mean": -5.224251464209499, + "lo": -10.100555181155746, + "hi": -1.1457544836015205, + "n": 10 + } + }, + "perfect_foresight": { + "lost_gpu_h_minus_reactive": { + "mean": -31.544860277220387, + "lo": -48.202983611723816, + "hi": -16.502871315007123, + "n": 10 + }, + "lost_steps_minus_reactive": { + "mean": -1125.903253875988, + "lo": -1743.0147279910652, + "hi": -558.6583378491581, + "n": 10 + }, + "deficit_gpu_h_minus_reactive": { + "mean": 0.7903585921055537, + "lo": -32.95056053538265, + "hi": 39.01327883849278, + "n": 10 + }, + "import_energy_above_kwh_minus_reactive": { + "mean": -6.663036640304367, + "lo": -13.800755120102421, + "hi": -1.439003034866696, + "n": 10 + } + } + }, + "not_run": { + "finetuned": "no fine-tuned weights were found (pass --finetuned-weights); the hook is wired but was not run" + }, + "finetuned_weights": null, + "notes": [ + "All data are simulated; no measured cluster data are used.", + "Thresholds were chosen on training seeds with a replay objective, then used unchanged on held-out seeds.", + "The sweep is open loop on the recorded reactive traces; closed-loop numbers are in policies.", + "The perfect-foresight policy reads event times of the reactive run and is an upper bound, not a deployable policy.", + "deficit_gpu_h is the useful GPU-hours of the event-free run minus the policy run on the same seed.", + "Forecast compute: every forecaster's open-loop probabilities are computed once per seed on all decision points (no subsampling) and cached; tuning and the threshold sweep reuse that cache.", + "Each forecast-driven policy then runs closed loop once per held-out seed at its tuned threshold; those runs forecast at every decision point because the response changes the trajectory the forecaster sees." + ], + "assumptions": [ + "Everything in this file is a simulation setting. No number here is a measurement of a real cluster.", + "The two-site cluster, job generator, thermal, breaker and power parameters come from sim-scenario-v2.json (not edited here). Power levels are read through the simulator at run time, so a recalibration of the simulator scenario flows through.", + "Job durations are drawn log-uniform between 1800 s and 21600 s (the base scenario uses 3600 s to 172800 s) so that a 15-hour window holds dozens of jobs; the job mix is otherwise the base scenario's, including its 10 percent of two-shard jobs that already follow the averaging rule.", + "The simulated clock starts at 17:00 UTC (late morning to midday local time); heat-wave centers and amplitudes are chosen to fall inside the 15-hour window and are simulation assumptions.", + "Event rates, precursor probability, precursor lead times and strengths, durations and severities are simulation assumptions chosen to produce several events per window; they are not rates of real facilities.", + "Controller decisions, forecasts and warnings use a 600 s interval; the simulator control step is 60 s and the base step is 60 s (the sim-scenario-v2.json steps are 10 s and 1 s).", + "Warning horizon 6 steps x 600 s = 60 min; forecast context 18 steps x 600 s = 3 h, equal to the 3 h warm-up before the first scheduled event.", + "Probability levels for the supply-temperature and GPU-temperature anomaly channels are the largest value (calibration_quantile 1.0) seen in event-free runs on training seeds. Level of the GPU temperature channel is the throttle threshold of the simulator scenario; level of the breaker load channel is 1.0 (rack power above rating); level of the site channel is the grid import limit including announced curtailments.", + "recheckpoint_s, clear_decisions, max_pause_s and oracle_extra_lead_steps are response-policy design choices that are not tuned." + ] + } +} From bb28b36914b3ec1ca74948dc172073dd6905b38b Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 15:31:19 -0700 Subject: [PATCH 16/52] Runner: worker pid in temp names, --only filter for the forecast stage Co-Authored-By: Claude Sonnet --- experiments/e003-heatwave/run_cost.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/experiments/e003-heatwave/run_cost.py b/experiments/e003-heatwave/run_cost.py index 9a17a7e..0ba7bb1 100644 --- a/experiments/e003-heatwave/run_cost.py +++ b/experiments/e003-heatwave/run_cost.py @@ -15,6 +15,7 @@ import argparse import json +import os import subprocess import sys import time @@ -82,12 +83,12 @@ def forecast_tasks(scenario: dict) -> list: return tasks -def forecast_stage(scenario: dict, worker: int, of: int) -> None: +def forecast_stage(scenario: dict, worker: int, of: int, only: list) -> None: CACHE.mkdir(exist_ok=True) (CACHE / ".gitignore").write_text("*\n") data: dict = {} fc_cache: dict = {} - todo = forecast_tasks(scenario)[worker::of] + todo = [t for t in forecast_tasks(scenario) if not only or t[1] in only][worker::of] ctx_cfg = scenario["context_hours"] horizon = scenario["decision"]["horizon_hours"] for series_key, fk, week in todo: @@ -108,7 +109,7 @@ def forecast_stage(scenario: dict, worker: int, of: int) -> None: data[pair]["known"] = hc.known_covariate_matrix(pair, data[pair]["index"]) known = data[pair]["known"]["matrix"] vals = hc.compute_week(data[pair][what], data[pair]["test_start"], week, fc_cache[fk], ctx, horizon, known=known) - tmp = path.with_suffix(".tmp.npz") + tmp = path.with_suffix(f".{os.getpid()}.tmp.npz") np.savez_compressed(tmp, values=vals, levels=np.array(hc.QUANTILE_LEVELS)) tmp.rename(path) print(f"{series_key} {fk} week {week}: {time.time() - t0:.1f}s", flush=True) @@ -232,7 +233,7 @@ def schedule_stage(scenario: dict, worker: int, of: int) -> None: except FileNotFoundError: print(f"{task_key(t)}: forecasts not cached yet, skipped", flush=True) continue - tmp = path.with_suffix(".tmp.npz") + tmp = path.with_suffix(f".{os.getpid()}.tmp.npz") np.savez_compressed(tmp, **res) tmp.rename(path) print(f"{task_key(t)}: {time.time() - t0:.1f}s fallbacks={res['fallbacks']}", flush=True) @@ -444,6 +445,7 @@ def main() -> None: ap.add_argument("stage", choices=["forecast", "schedule", "evaluate"]) ap.add_argument("--worker", type=int, default=0) ap.add_argument("--of", type=int, default=1) + ap.add_argument("--only", default="", help="comma-separated forecaster keys (forecast stage)") ap.add_argument("--scenario", default=str(SCENARIO)) ap.add_argument("--out", default=str(OUT)) ap.add_argument("--sched-dir", default=str(SCHED)) @@ -451,7 +453,7 @@ def main() -> None: OUT, SCHED, SCENARIO = Path(a.out), Path(a.sched_dir), Path(a.scenario) scenario = hc.load_scenario(SCENARIO) if a.stage == "forecast": - forecast_stage(scenario, a.worker, a.of) + forecast_stage(scenario, a.worker, a.of, [x for x in a.only.split(',') if x]) elif a.stage == "schedule": schedule_stage(scenario, a.worker, a.of) else: From dc7061b2dac5fe28d9126f8035b9961f5e82c82a Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 15:37:35 -0700 Subject: [PATCH 17/52] Evaluate: forecast spread diagnostic and notes on subsampling and bounds Co-Authored-By: Claude Sonnet --- .../e003-heatwave/results/cost-v1.json | 216607 +++++++++++++++ experiments/e003-heatwave/run_cost.py | 9 +- 2 files changed, 216615 insertions(+), 1 deletion(-) create mode 100644 experiments/e003-heatwave/results/cost-v1.json diff --git a/experiments/e003-heatwave/results/cost-v1.json b/experiments/e003-heatwave/results/cost-v1.json new file mode 100644 index 0000000..c558af8 --- /dev/null +++ b/experiments/e003-heatwave/results/cost-v1.json @@ -0,0 +1,216607 @@ +{ + "provenance": { + "script": "/home/claude/wt/hw-cost/experiments/e003-heatwave/run_cost.py", + "script_sha256": "30f5a8d6a392f8a0ff76f77097d7e13aa031f0ed96bb1a5520780c8f7e4b856a", + "git": { + "commit": "47a752f3e18908a1f68460b8e7ba85f643acfe7d", + "dirty": "false" + }, + "inputs": { + "/home/claude/wt/hw-cost/experiments/e003-heatwave/cost-scenario-v1.json": "dde8857fbfe1d962f39662b0f769e6ee43ef947d95287dc993f95458e4ec1eee", + "/home/claude/wt/hw-cost/experiments/e003-heatwave/inputs/ecb_usd_per_eur_2026-04-27_2026-10-25.csv": "1324b85fbe5ae40cad93472dc8a0223a27fe282acf789e431c02e1dd9c791c69", + "/home/claude/heatwave/data/processed/public_caiso_np15_lmp_dam.parquet": "ac9d2ef96fa1b28085c263d399f61a348266ef9633b85dc0730ed163f5895521", + "/home/claude/heatwave/data/processed/public_caiso_np15_lmp_dam.meta.json": "492ff2ae2ff7555c899cc10f3fd8a345b3bee7c4509b02d6104d4a137eaaec8e", + "/home/claude/heatwave/data/processed/public_caiso_carbon.parquet": "c31befeefd5cfaab57a0290c9e8363ef7af66b2df4171e903c057bb0e3dbca2b", + "/home/claude/heatwave/data/processed/public_caiso_carbon.meta.json": "55f21669a503949cacc4b66bfd04f0cf5cc797eb41353a8d8503ca692435de42", + "/home/claude/heatwave/data/processed/public_de_price_dam_1h.parquet": "ffdcf4c37e84c6b127ffb853f5a79812993e5406bce59fa5d3b354463d8975c6", + "/home/claude/heatwave/data/processed/public_de_price_dam_1h.meta.json": "a9440db537689d7ce48641f549b6610ae82dae5566cd05bef931ce91b4649404", + "/home/claude/heatwave/data/processed/public_de_generation.parquet": "005ca3560a3e1ef3f9c94de07b6470f4a183baa3682cad0ff1e0830fdc16840a", + "/home/claude/heatwave/data/processed/public_de_generation.meta.json": "9df3fa3ef551ecd02a7926e92dfa9e83a929822eefdaaeafc157520b365e77d8" + }, + "data_kind": "public", + "created_utc": "2026-10-26T22:36:43+00:00", + "notes": "Public price and carbon series; modeled workload. The git field is the heatwave repository; the gpu_stack repository state is in result.gpu_stack_git." + }, + "result": { + "scenario": { + "schema": "gpu-stack.e003-heatwave-cost-scenario.v1", + "scenario_id": "e003-heatwave-cost-v1", + "data_kind": "public", + "description": "Cost timing: a cluster running a reference training job against real public hourly price and carbon-intensity series (CAISO NP15 day-ahead price with CAISO in-state intensity, and DE-LU day-ahead price with a DE lifecycle-intensity proxy; each pair is from one region). A share f of the work may be delayed by up to a window W; each day the scheduler plans the flexible work from a 48-hour quantile forecast. The series are public data; the workload (utilization, flexible share, windows) is a scenario assumption and nothing here is measured on a real cluster.", + "reference_run": { + "params": 7000000000.0, + "tokens": 2000000000000.0, + "gpu": "H100-SXM", + "n_gpus": 1024, + "overrides": {}, + "note": "The README example of the calculator; every other calculator input is its default." + }, + "test": { + "first_day_utc": "2026-04-27", + "weeks": 26, + "normalization_days": 90, + "note": "26 whole weeks ending 2026-10-25. The 90 days before the first test day fix the z-score constants only. Nothing is tuned on the test period." + }, + "decision": { + "hour_utc": 0, + "horizon_hours": 48, + "run_hours": 24 + }, + "workload": { + "utilization": 0.7, + "power_cap": 1.0, + "flexible_fractions": [ + 0.1, + 0.3, + 0.5 + ], + "windows_hours": [ + 24, + 72 + ], + "headline": { + "flexible_fraction": 0.3, + "window_hours": 24 + }, + "utilization_sensitivity": [ + 0.5, + 0.9 + ], + "note": "utilization is the mean offered load as a fraction of the cluster's full-load power (an assumption, not a measurement); power_cap is the most total load allowed in any hour, as a fraction of full-load power.", + "utilization_sensitivity_policies": [ + "perfect", + "timestral_28M", + "seasonal_naive_24" + ] + }, + "lambdas": [ + 0.0, + 0.1, + 0.2, + 0.3, + 0.4, + 0.5, + 0.6, + 0.7, + 0.8, + 0.9, + 1.0 + ], + "planning_quantiles": { + "default": 0.5, + "risk_sweep": [ + 0.1, + 0.5, + 0.75, + 0.9 + ], + "risk_sweep_flexible_fraction": 0.3 + }, + "pairs": [ + "caiso", + "de" + ], + "context_hours": 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"bb28b36914b3ec1ca74948dc172073dd6905b38b", + "branch": "hw-cost", + "dirty": "false" + }, + "cache_digests": { + "forecast": { + "n_files": 652, + "sha256": "3cd5cf91e28a16fe1c68c1bab4dcc04fd0dfe3053c6252bbdc8785d0e4a98a2e" + }, + "schedule": { + "n_files": 276, + "sha256": "b37e62166b7a6dfebd6d979d5fa351ff59335edd686096329fb38d2fc2988230" + } + }, + "notes": [ + "Electricity price is the wholesale day-ahead price (CAISO NP15 LMP, DE-LU day-ahead); no retail adders, network fees or taxes.", + "Hardware cost per token is the calculator's and does not depend on timing; total = electricity + hardware. DE electricity is converted at the mean ECB rate.", + "CAISO carbon is the in-state intensity computed from CAISO's published CO2 and fuel mix (imports excluded). DE carbon is a production-based lifecycle proxy from IPCC AR5 medians, not an official figure.", + "Workload (utilization, flexible share, windows, no idle power, constant energy per token) is a scenario assumption; nothing is measured on a real cluster.", + "Forecasts are made at 00:00 UTC from history only; no day-ahead publications are used as inputs." + ] + } +} diff --git a/experiments/e003-heatwave/run_cost.py b/experiments/e003-heatwave/run_cost.py index 0ba7bb1..7c7e32e 100644 --- a/experiments/e003-heatwave/run_cost.py +++ b/experiments/e003-heatwave/run_cost.py @@ -329,6 +329,10 @@ def forecast_scores(ctx: PairContext, scenario: dict) -> dict: fc = QuantileForecast(values=vals[:, None, sl, :]) y = fut[:, None, sl] rec[tag] = {"wql": M.wql(fc, y), "mae_median": M.mae_median(fc, y), "coverage_80": M.coverage(fc, y, 0.8)} + if tag == "h1_24": + # how much the median forecast varies across the 24 hours (a flat median gives the planner nothing to shift on) + rec["median_within_day_std"] = float(np.mean(np.std(vals[:, sl, hc.quantile_index(0.5)], axis=1))) + rec["actual_within_day_std"] = float(np.nanmean(np.nanstd(fut[:, sl], axis=1))) out.setdefault(what, {}).setdefault(fk, {})[name] = rec return out @@ -432,7 +436,10 @@ def evaluate_stage(scenario: dict) -> None: "Hardware cost per token is the calculator's and does not depend on timing; total = electricity + hardware. DE electricity is converted at the mean ECB rate.", "CAISO carbon is the in-state intensity computed from CAISO's published CO2 and fuel mix (imports excluded). DE carbon is a production-based lifecycle proxy from IPCC AR5 medians, not an official figure.", "Workload (utilization, flexible share, windows, no idle power, constant energy per token) is a scenario assumption; nothing is measured on a real cluster.", - "Forecasts are made at 00:00 UTC from history only; no day-ahead publications are used as inputs.", + "Forecasts are made at 00:00 UTC from history only; no day-ahead publications are used as inputs, except the policy timestral_28M_cov, which also reads the archived day-ahead demand (CAISO) or load, solar and wind (DE) forecasts as known covariates; the issue time of those archived values relative to 00:00 UTC is not verified for hours 25-48. Open-Meteo weather forecasts were not available (quota), so no weather covariate was used.", + "Classical forecasts (seasonal naive, ETS, ARIMA) are computed once for every decision day of the 26 test weeks (no subsampling), cached, and reused by every policy configuration. Timestral 209M is run on 7 of the 26 weeks only (weeks 0, 4, 8, 12, 16, 20, 24); curves_subsample holds every policy re-run on those same weeks with an empty backlog at the start of each week.", + "perfect is the same daily scheduler fed the realised values for 48 hours; offline_optimum is one linear program over the whole test period with every value known. Shares of the perfect-foresight saving can exceed 1 because perfect plans 48 hours at a time and its goal is the mixed cost, not either axis alone.", + "Great Britain carbon (forecast vintage undocumented) was not used: no GB price series was collected, so there is no same-region pair for it.", ], } write_result(OUT, result, script=__file__, data_kind="public", inputs=inputs, From 514948a752d357885f6727fe47fb7bce1eed6917 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 15:43:15 -0700 Subject: [PATCH 18/52] Add cost-v1 result: dollars per token vs carbon per token, CAISO and DE-LU pairs (public price and carbon series, modeled workload) Co-Authored-By: Claude Sonnet --- .../e003-heatwave/results/cost-v1.json | 115 +++++++++++++++++- 1 file changed, 111 insertions(+), 4 deletions(-) diff --git a/experiments/e003-heatwave/results/cost-v1.json b/experiments/e003-heatwave/results/cost-v1.json index c558af8..fe791b3 100644 --- a/experiments/e003-heatwave/results/cost-v1.json +++ b/experiments/e003-heatwave/results/cost-v1.json @@ -1,7 +1,7 @@ { "provenance": { "script": "/home/claude/wt/hw-cost/experiments/e003-heatwave/run_cost.py", - "script_sha256": "30f5a8d6a392f8a0ff76f77097d7e13aa031f0ed96bb1a5520780c8f7e4b856a", + "script_sha256": "9eafa5e1240eb00360309f7de6ac7d04ec9d0b70ba3228de29e85d7d89604918", "git": { "commit": "47a752f3e18908a1f68460b8e7ba85f643acfe7d", "dirty": "false" @@ -19,7 +19,7 @@ "/home/claude/heatwave/data/processed/public_de_generation.meta.json": "9df3fa3ef551ecd02a7926e92dfa9e83a929822eefdaaeafc157520b365e77d8" }, "data_kind": "public", - "created_utc": "2026-10-26T22:36:43+00:00", + "created_utc": "2026-10-26T22:42:42+00:00", "notes": "Public price and carbon series; modeled workload. The git field is the heatwave repository; the gpu_stack repository state is in result.gpu_stack_git." }, "result": { @@ -255,6 +255,8 @@ "mae_median": 5.579892924825678, "coverage_80": 0.7933673469387755 }, + "median_within_day_std": 10.13398432883013, + "actual_within_day_std": 10.271132589921471, "h25_48": { "wql": 0.16072116610676485, "mae_median": 7.687026825178078, @@ -267,6 +269,8 @@ "mae_median": 5.955052110203337, "coverage_80": 0.7898351648351648 }, + "median_within_day_std": 11.261005803230614, + "actual_within_day_std": 11.25879121119283, "h25_48": { "wql": 0.16851571701002774, "mae_median": 8.381532525672649, @@ -281,6 +285,8 @@ "mae_median": 11.357528980966684, "coverage_80": 0.7372448979591837 }, + "median_within_day_std": 1.8470250180425267, + "actual_within_day_std": 10.271132589921471, "h25_48": { "wql": 0.28964283752019454, "mae_median": 13.13112940674334, @@ -293,6 +299,8 @@ "mae_median": 11.616123297679044, "coverage_80": 0.7598443223443223 }, + "median_within_day_std": 2.460764480821013, + "actual_within_day_std": 11.25879121119283, "h25_48": { "wql": 0.28865513214954597, "mae_median": 13.85362317826206, @@ -307,6 +315,8 @@ "mae_median": 5.08321894403007, "coverage_80": 0.8316326530612245 }, + "median_within_day_std": 9.555598117293528, + "actual_within_day_std": 10.271132589921471, "h25_48": { "wql": 0.15914230099258206, "mae_median": 7.241257854685346, @@ -319,6 +329,8 @@ "mae_median": 5.530222183314335, "coverage_80": 0.8303571428571429 }, + "median_within_day_std": 10.970503054751273, + "actual_within_day_std": 11.25879121119283, "h25_48": { "wql": 0.16708386818565144, "mae_median": 8.185633226361436, @@ -333,6 +345,8 @@ "mae_median": 4.14754279605619, "coverage_80": 0.798469387755102 }, + "median_within_day_std": 9.532647515398592, + "actual_within_day_std": 10.271132589921471, "h25_48": { "wql": 0.11245611749329712, "mae_median": 5.425661028028605, @@ -345,6 +359,8 @@ "mae_median": 4.604332199478394, "coverage_80": 0.7847985347985348 }, + "median_within_day_std": 10.13579248346092, + "actual_within_day_std": 11.25879121119283, "h25_48": { "wql": 0.12950599399368262, "mae_median": 6.6348416165902995, @@ -359,6 +375,8 @@ "mae_median": 4.155987529545478, "coverage_80": 0.8010204081632653 }, + "median_within_day_std": 9.877806284447216, + "actual_within_day_std": 10.271132589921471, "h25_48": { "wql": 0.11137560763291193, "mae_median": 5.382144324415169, @@ -371,6 +389,8 @@ "mae_median": 4.459179602304361, "coverage_80": 0.7944139194139194 }, + "median_within_day_std": 10.4526491024425, + "actual_within_day_std": 11.25879121119283, "h25_48": { "wql": 0.1253607441661933, "mae_median": 6.428806972607705, @@ -385,6 +405,8 @@ "mae_median": 3.4783841771677237, "coverage_80": 0.8231292517006803 }, + "median_within_day_std": 9.653193109187237, + "actual_within_day_std": 10.271132589921471, "h25_48": { "wql": 0.08320458313232275, "mae_median": 4.067852567861648, @@ -397,6 +419,8 @@ "mae_median": 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0.1521149825329043, "mae_median": 26.62876640539509, @@ -557,6 +603,8 @@ "mae_median": 16.23021166387715, "coverage_80": 0.7585034013605442 }, + "median_within_day_std": 39.92460712557198, + "actual_within_day_std": 43.141870801655045, "h25_48": { "wql": 0.13093778729374492, "mae_median": 21.299355125600716, @@ -569,6 +617,8 @@ "mae_median": 15.646805524793734, "coverage_80": 0.788003663003663 }, + "median_within_day_std": 41.91169536211062, + "actual_within_day_std": 43.95061290053661, "h25_48": { "wql": 0.1308307351384317, "mae_median": 22.906210401615244, @@ -583,6 +633,8 @@ "mae_median": 16.801618336012332, "coverage_80": 0.7729591836734694 }, + "median_within_day_std": 41.39842345475925, + "actual_within_day_std": 43.141870801655045, "h25_48": { "wql": 0.13485776701204516, "mae_median": 21.579757002608453, @@ -108365,6 +108417,8 @@ "mae_median": 34.33794858691207, "coverage_80": 0.8061224489795918 }, + "median_within_day_std": 66.27707548355673, + "actual_within_day_std": 62.753530774497925, "h25_48": { "wql": 0.27105652898157395, "mae_median": 45.238018600989236, @@ -108377,6 +108431,8 @@ "mae_median": 34.31215481058682, "coverage_80": 0.7777014652014652 }, + "median_within_day_std": 56.316075760825036, + "actual_within_day_std": 55.51275224488313, "h25_48": { "wql": 0.27091438156489933, "mae_median": 45.249836526373876, @@ -108391,6 +108447,8 @@ "mae_median": 51.1474046155709, "coverage_80": 0.8715986394557823 }, + "median_within_day_std": 2.324839597820278, + "actual_within_day_std": 62.753530774497925, "h25_48": { "wql": 0.41279508655142916, "mae_median": 55.36295596064354, @@ -108403,6 +108461,8 @@ "mae_median": 45.99910377128657, "coverage_80": 0.9107142857142857 }, + "median_within_day_std": 1.2811042424944963, + "actual_within_day_std": 55.51275224488313, "h25_48": { "wql": 0.4108988531398566, "mae_median": 52.52445337204664, @@ -108417,6 +108477,8 @@ "mae_median": 34.16256338623612, "coverage_80": 0.8520408163265306 }, + "median_within_day_std": 66.25082663791949, + "actual_within_day_std": 62.753530774497925, "h25_48": { "wql": 0.26301671109964536, "mae_median": 42.96288288228819, @@ -108429,6 +108491,8 @@ "mae_median": 32.758985995087556, "coverage_80": 0.839514652014652 }, + "median_within_day_std": 54.98488171995619, + "actual_within_day_std": 55.51275224488313, "h25_48": { "wql": 0.2591491921110029, "mae_median": 42.91016480821782, @@ -108443,6 +108507,8 @@ "mae_median": 26.039432887122747, "coverage_80": 0.7772108843537415 }, + "median_within_day_std": 51.77068504240099, + "actual_within_day_std": 62.753530774497925, "h25_48": { "wql": 0.18125272249958513, "mae_median": 31.285389380357703, @@ -108455,6 +108521,8 @@ "mae_median": 24.227808006272646, "coverage_80": 0.7706043956043956 }, + "median_within_day_std": 48.8227778371216, + "actual_within_day_std": 55.51275224488313, "h25_48": { "wql": 0.1833158145686165, "mae_median": 32.111891663458046, @@ -108469,6 +108537,8 @@ "mae_median": 23.992326346481736, "coverage_80": 0.8214285714285714 }, + "median_within_day_std": 53.53905281030716, + "actual_within_day_std": 62.753530774497925, "h25_48": { "wql": 0.1719981492729731, "mae_median": 29.738424074990412, @@ -108481,6 +108551,8 @@ "mae_median": 23.109387728041344, "coverage_80": 0.8122710622710623 }, + "median_within_day_std": 50.20424922405365, + "actual_within_day_std": 55.51275224488313, "h25_48": { "wql": 0.17515550504013372, "mae_median": 30.859214276892132, @@ -108495,6 +108567,8 @@ "mae_median": 19.642239004641162, "coverage_80": 0.826530612244898 }, + "median_within_day_std": 54.90276797843477, + "actual_within_day_std": 62.753530774497925, "h25_48": { "wql": 0.14521066275197522, "mae_median": 25.90231599411997, @@ -108507,6 +108581,8 @@ "mae_median": 16.96669774858943, "coverage_80": 0.8436355311355311 }, + "median_within_day_std": 51.284868368081426, + "actual_within_day_std": 55.51275224488313, "h25_48": { "wql": 0.1215712942364276, "mae_median": 21.418149863538314, @@ -108521,6 +108597,8 @@ "mae_median": 23.923076500016816, "coverage_80": 0.7976190476190477 }, + "median_within_day_std": 55.75750074521022, + "actual_within_day_std": 62.753530774497925, "h25_48": { "wql": 0.16366902439789346, "mae_median": 28.704204985819704, @@ -108537,6 +108615,8 @@ "mae_median": 60.98905667136657, "coverage_80": 0.8358843537414966 }, + "median_within_day_std": 106.07934787365076, + "actual_within_day_std": 101.08670270143232, "h25_48": { "wql": 0.18975786969226957, "mae_median": 75.92511466363344, @@ -108549,6 +108629,8 @@ "mae_median": 68.60681017890845, "coverage_80": 0.7838827838827839 }, + "median_within_day_std": 102.378685962809, + "actual_within_day_std": 101.6263037183972, "h25_48": { "wql": 0.21041212450813418, "mae_median": 85.72782471585509, @@ -108563,6 +108645,8 @@ "mae_median": 116.44344199120367, "coverage_80": 0.7295918367346939 }, + "median_within_day_std": 0.000326917040880723, + "actual_within_day_std": 101.08670270143232, "h25_48": { "wql": 0.3497875834227756, "mae_median": 139.31265779178463, @@ -108575,6 +108659,8 @@ "mae_median": 116.61702409881933, "coverage_80": 0.7426739926739927 }, + "median_within_day_std": 1.3677762353219303, + "actual_within_day_std": 101.6263037183972, "h25_48": { "wql": 0.3582030810536273, "mae_median": 147.5166698248711, @@ -108589,6 +108675,8 @@ "mae_median": 46.41570882231082, "coverage_80": 0.8860544217687075 }, + "median_within_day_std": 102.0512485285594, + "actual_within_day_std": 101.08670270143232, "h25_48": { "wql": 0.17112187910696824, "mae_median": 69.54480183485367, @@ -108601,6 +108689,8 @@ "mae_median": 51.184843107986964, "coverage_80": 0.8523351648351648 }, + "median_within_day_std": 97.37067081089864, + "actual_within_day_std": 101.6263037183972, "h25_48": { "wql": 0.19428891540039253, "mae_median": 80.02718141348582, @@ -108615,6 +108705,8 @@ "mae_median": 39.321228954943166, "coverage_80": 0.8214285714285714 }, + "median_within_day_std": 100.40671175603852, + "actual_within_day_std": 101.08670270143232, "h25_48": { "wql": 0.1397524389125449, "mae_median": 58.32950943394471, @@ -108627,6 +108719,8 @@ "mae_median": 43.67360509533165, "coverage_80": 0.8040293040293041 }, + "median_within_day_std": 97.17745795066598, + "actual_within_day_std": 101.6263037183972, "h25_48": { "wql": 0.16246793886693364, "mae_median": 69.64921923284994, @@ -108641,6 +108735,8 @@ "mae_median": 36.912384051871435, "coverage_80": 0.8154761904761905 }, + "median_within_day_std": 101.73062863012328, + "actual_within_day_std": 101.08670270143232, "h25_48": { "wql": 0.13829305743959097, "mae_median": 57.991664472832795, @@ -108653,6 +108749,8 @@ "mae_median": 42.898372806538035, "coverage_80": 0.7861721611721612 }, + "median_within_day_std": 97.83846056016253, + "actual_within_day_std": 101.6263037183972, "h25_48": { "wql": 0.1620190066518833, "mae_median": 69.03759755259446, @@ -108667,6 +108765,8 @@ "mae_median": 22.191941521475115, "coverage_80": 0.8052721088435374 }, + "median_within_day_std": 103.0182871899902, + "actual_within_day_std": 101.08670270143232, "h25_48": { "wql": 0.08176602486244203, "mae_median": 33.722880038447016, @@ -108679,6 +108779,8 @@ "mae_median": 23.03848241300923, "coverage_80": 0.8111263736263736 }, + "median_within_day_std": 100.3719320990205, + "actual_within_day_std": 101.6263037183972, "h25_48": { "wql": 0.07864975235379938, "mae_median": 32.79793459081299, @@ -108693,6 +108795,8 @@ "mae_median": 35.81792431825822, "coverage_80": 0.83078231292517 }, + "median_within_day_std": 102.4155173290638, + "actual_within_day_std": 101.08670270143232, "h25_48": { "wql": 0.13549896049282095, "mae_median": 56.96297368437384, @@ -216582,7 +216686,7 @@ }, "gpu_stack_git": { "repo": "gpu_stack", - "commit": "bb28b36914b3ec1ca74948dc172073dd6905b38b", + "commit": "dc7061b2dac5fe28d9126f8035b9961f5e82c82a", "branch": "hw-cost", "dirty": "false" }, @@ -216601,7 +216705,10 @@ "Hardware cost per token is the calculator's and does not depend on timing; total = electricity + hardware. DE electricity is converted at the mean ECB rate.", "CAISO carbon is the in-state intensity computed from CAISO's published CO2 and fuel mix (imports excluded). DE carbon is a production-based lifecycle proxy from IPCC AR5 medians, not an official figure.", "Workload (utilization, flexible share, windows, no idle power, constant energy per token) is a scenario assumption; nothing is measured on a real cluster.", - "Forecasts are made at 00:00 UTC from history only; no day-ahead publications are used as inputs." + "Forecasts are made at 00:00 UTC from history only; no day-ahead publications are used as inputs, except the policy timestral_28M_cov, which also reads the archived day-ahead demand (CAISO) or load, solar and wind (DE) forecasts as known covariates; the issue time of those archived values relative to 00:00 UTC is not verified for hours 25-48. Open-Meteo weather forecasts were not available (quota), so no weather covariate was used.", + "Classical forecasts (seasonal naive, ETS, ARIMA) are computed once for every decision day of the 26 test weeks (no subsampling), cached, and reused by every policy configuration. Timestral 209M is run on 7 of the 26 weeks only (weeks 0, 4, 8, 12, 16, 20, 24); curves_subsample holds every policy re-run on those same weeks with an empty backlog at the start of each week.", + "perfect is the same daily scheduler fed the realised values for 48 hours; offline_optimum is one linear program over the whole test period with every value known. Shares of the perfect-foresight saving can exceed 1 because perfect plans 48 hours at a time and its goal is the mixed cost, not either axis alone.", + "Great Britain carbon (forecast vintage undocumented) was not used: no GB price series was collected, so there is no same-region pair for it." ] } } From 4b78bf07fd5b60fe194c457cc3ac82be3709d8f4 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 16:00:19 -0700 Subject: [PATCH 19/52] Cost result: re-stamp provenance against heatwave main (no number changed) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- experiments/e003-heatwave/results/cost-v1.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/experiments/e003-heatwave/results/cost-v1.json b/experiments/e003-heatwave/results/cost-v1.json index fe791b3..d972ca3 100644 --- a/experiments/e003-heatwave/results/cost-v1.json +++ b/experiments/e003-heatwave/results/cost-v1.json @@ -3,7 +3,7 @@ "script": "/home/claude/wt/hw-cost/experiments/e003-heatwave/run_cost.py", "script_sha256": "9eafa5e1240eb00360309f7de6ac7d04ec9d0b70ba3228de29e85d7d89604918", "git": { - "commit": "47a752f3e18908a1f68460b8e7ba85f643acfe7d", + "commit": "1048057362084ef5015e16176e52d7391c89cb35", "dirty": "false" }, "inputs": { @@ -19,7 +19,7 @@ "/home/claude/heatwave/data/processed/public_de_generation.meta.json": "9df3fa3ef551ecd02a7926e92dfa9e83a929822eefdaaeafc157520b365e77d8" }, "data_kind": "public", - "created_utc": "2026-10-26T22:42:42+00:00", + "created_utc": "2026-10-26T22:59:50+00:00", "notes": "Public price and carbon series; modeled workload. The git field is the heatwave repository; the gpu_stack repository state is in result.gpu_stack_git." }, "result": { @@ -216686,7 +216686,7 @@ }, "gpu_stack_git": { "repo": "gpu_stack", - "commit": "dc7061b2dac5fe28d9126f8035b9961f5e82c82a", + "commit": "514948a752d357885f6727fe47fb7bce1eed6917", "branch": "hw-cost", "dirty": "false" }, From d4bb718f66a670088e00258ee635ca1c81d27403 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 16:02:18 -0700 Subject: [PATCH 20/52] Packing: plan running jobs over every bin they overlap, stretch walltime by checkpoint overhead Co-Authored-By: Claude Sonnet --- .../e003-heatwave/packing-scenario-v1.json | 6 +- .../eval/oracle__seed500.json | 77 +++++++++++++ .../eval/oracle__seed501.json | 77 +++++++++++++ .../eval/oracle__seed502.json | 77 +++++++++++++ .../eval/reactive__seed500.json | 77 +++++++++++++ .../eval/reactive__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.01__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.01__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.01__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.1__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.1__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.1__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.2__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.2__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.2__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.3__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.3__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.3__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.4__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.4__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.4__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.5__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.5__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.5__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.6__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.6__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.6__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.7__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.7__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.7__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.8__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.8__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.8__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.99__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.99__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.99__seed502.json | 77 +++++++++++++ .../eval/timestral-5M__q0.9__seed500.json | 77 +++++++++++++ .../eval/timestral-5M__q0.9__seed501.json | 77 +++++++++++++ .../eval/timestral-5M__q0.9__seed502.json | 77 +++++++++++++ .../results/packing-v1-parts/model.json | 102 ++++++++++++------ .../packing-v1-parts/trace-seed500.npz | Bin 0 -> 7482 bytes .../packing-v1-parts/trace-seed501.npz | Bin 0 -> 7478 bytes .../packing-v1-parts/trace-seed502.npz | Bin 0 -> 7475 bytes gpu_stack/research/heatwave_packing.py | 30 +++++- tests/test_research_heatwave_packing.py | 22 ++++ 45 files changed, 3049 insertions(+), 37 deletions(-) create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/eval/oracle__seed500.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/eval/oracle__seed501.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/eval/oracle__seed502.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/eval/reactive__seed500.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/eval/reactive__seed501.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/eval/timestral-5M__q0.01__seed500.json 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b/experiments/e003-heatwave/packing-scenario-v1.json @@ -16,7 +16,8 @@ "context_bins": 576, "min_context_bins": 24, "warm_ratio_bins": 24, - "start_hour_utc": 13.0 + "start_hour_utc": 13.0, + "end_rule": "overlap" }, "cap": { "load_fraction": 0.55, @@ -222,6 +223,7 @@ "Over-cap penalty (USD per kWh above the site cap) is a scenario parameter; the default and the sweep are assumptions.", "Planning horizon (3 h in 5-minute steps) and the learned policy's surrogate reward window (3 h) are scenario choices.", "Warm-up (first 25 h of each 41 h episode) uses one shared rule for all forecast policies and is excluded from every reported metric; the evaluation window is the last 16 h.", - "ETS and ARIMA refit once per hour of decisions (forecast_refresh_bins = 12) and serve the 11 decisions in between from the same paths shifted by the elapsed steps, to keep their run time down; every other forecaster is called at every decision." + "ETS and ARIMA refit once per hour of decisions (forecast_refresh_bins = 12) and serve the 11 decisions in between from the same paths shifted by the elapsed steps, to keep their run time down; every other forecaster is called at every decision.", + "Running jobs are planned to end at their declared walltime stretched by the checkpoint overhead implied by the job generator's checkpoint ranges, and count in every 5-minute bin they overlap (end_rule 'overlap')." ] } diff --git a/experiments/e003-heatwave/results/packing-v1-parts/eval/oracle__seed500.json b/experiments/e003-heatwave/results/packing-v1-parts/eval/oracle__seed500.json new file mode 100644 index 0000000..40db29b --- /dev/null +++ b/experiments/e003-heatwave/results/packing-v1-parts/eval/oracle__seed500.json @@ -0,0 +1,77 @@ +{ + "provenance": { + "script": "experiments/e003-heatwave/run_packing.py", + "script_sha256": "775824294538dd7090bef77219f8ffc4235574ac1dc67d03c379609abd423248", + "module_sha256": "d4e21491b179416e52d04f620cfaffdd931c658ad702b0e348eb05747de48a79", + 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cluster operator knows the checkpoint cadence policy of its users; the + controller uses this single number to stretch a job's declared walltime + (mean checkpoint duration over mean checkpoint interval). + """ + g = packing["jobs"]["generate"] + return float(np.mean(g["checkpoint_duration_range_s"]) / np.mean(g["checkpoint_every_range_s"])) + + @dataclass(frozen=True) class PackingSettings: decision_interval_s: float @@ -406,6 +419,8 @@ class PackingSettings: horizon_s: float reactive_margin: float warm_ratio_bins: int + walltime_overhead: float = 0.0 + end_rule: str = "overlap" @classmethod def from_scenario(cls, packing: Mapping[str, Any]) -> "PackingSettings": @@ -417,6 +432,7 @@ def from_scenario(cls, packing: Mapping[str, Any]) -> "PackingSettings": call_s=float(ep["controller_interval_s"]), reward_window_s=float(packing["reward_window_s"]), queue_order=str(packing["queue_order"]), horizon_s=float(ep["horizon_s"]), reactive_margin=float(packing["reactive"]["margin"]), warm_ratio_bins=int(ep["warm_ratio_bins"]), + walltime_overhead=declared_checkpoint_overhead(packing), end_rule=str(ep.get("end_rule", "overlap")), ) @@ -517,7 +533,11 @@ def _planned_busy(self, t: float, vs: VisibleState) -> np.ndarray: continue job = self.job_by_id[jv.job_id] remaining = max(job.duration_s * (1.0 - jv.progress_steps / jv.total_steps), 0.0) - active = (mids < t + remaining).astype(float) + remaining *= 1.0 + self.cfg.walltime_overhead + if self.cfg.end_rule == "overlap": # count the job in every bin it overlaps (peak power, not bin mean) + active = (mids - 0.5 * bs < t + remaining).astype(float) + else: + active = (mids < t + remaining).astype(float) share = job.n_gpus / 2.0 if job.multisite else float(job.n_gpus) for sid in jv.site_ids: out[self.site_ids.index(sid)] += share * active @@ -985,7 +1005,7 @@ def sha256_bytes(data: bytes) -> str: __all__ = [ "CostSensitiveModel", "Episode", "FEATURE_NAMES", "FixedQ", "LearnedQ", "MEDIAN_INDEX", "N_FEATURES", "N_LEVELS", - "PackingController", "PackingSettings", "QUANTILE_LEVELS", "RandomQ", "SiteModel", "Situation", + "PackingController", "PackingSettings", "declared_checkpoint_overhead", "QUANTILE_LEVELS", "RandomQ", "SiteModel", "Situation", "bootstrap_ci", "build_episode", "calibrate_sites", "episode_metrics", "hindsight_costs", "level_index", "load_packing_scenario", "net_value", "run_episode", ] diff --git a/tests/test_research_heatwave_packing.py b/tests/test_research_heatwave_packing.py index b7519b9..0d79192 100644 --- a/tests/test_research_heatwave_packing.py +++ b/tests/test_research_heatwave_packing.py @@ -291,3 +291,25 @@ def forecast(self, context, horizon, known_covariates=None, past_covariates=None assert runs[4][2]["forecast_refresh_bins"] == 4 and runs[1][2]["forecast_refresh_bins"] == 1 with pytest.raises(ValueError): make_controller(tiny, ep, q=0.9, forecast_refresh_bins=0) + + +def test_planned_busy_counts_overlapping_bins_and_stretches_walltime(tiny): + from dataclasses import replace + + packing, sim, cal = tiny + ep = hp.build_episode(packing, sim, cal["models"], 12) + ctrl, st = make_controller(tiny, ep) + job = ep.jobs[0] + jv = SimpleNamespace(job_id=job.job_id, status="running", site_ids=("site-a",), progress_steps=0.0, total_steps=1.0) + vs = SimpleNamespace(jobs=(jv,)) + # remaining walltime of 1.2 bins: the job overlaps bins 0 and 1 under 'overlap', and bin 0 and 1 are + # active under the midpoint rule only when the bin midpoint is before the end + short = replace(job, duration_s=1.2 * st.bin_s / (1.0 + st.walltime_overhead)) + ctrl.job_by_id[job.job_id] = short + n = short.n_gpus + busy = ctrl._planned_busy(0.0, vs) + assert busy[0, 0] == n and busy[0, 1] == n and busy[0, 2] == 0.0 + ctrl.cfg = replace(st, end_rule="midpoint") + busy_mid = ctrl._planned_busy(0.0, vs) + assert busy_mid[0, 0] == n and busy_mid[0, 1] == 0.0 + assert st.walltime_overhead == pytest.approx(hp.declared_checkpoint_overhead(packing)) From cbf6d7e58129fcf5475dab48fe862249b7efdca1 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 16:03:38 -0700 Subject: [PATCH 21/52] e003 sim: GPU-temperature precursors reach visible state and recorded history; JobView lists racks Precursor offsets were applied to the internal GPU temperature (throttle and shutdown) but not to what the controller sees or to the recorded max GPU temperature, so no forecaster could see a hardware-fault precursor. Requested by the outage-response work. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- gpu_stack/research/heatwave_sim.py | 14 ++++++++++---- tests/test_research_heatwave_sim.py | 24 ++++++++++++++++++++++++ 2 files changed, 34 insertions(+), 4 deletions(-) diff --git a/gpu_stack/research/heatwave_sim.py b/gpu_stack/research/heatwave_sim.py index 540e20e..a9942fa 100644 --- a/gpu_stack/research/heatwave_sim.py +++ b/gpu_stack/research/heatwave_sim.py @@ -546,6 +546,7 @@ class JobView: submit_s: float planned_start_s: float paused: bool + rack_ids: Tuple[str, ...] = () @dataclass(frozen=True) @@ -1417,10 +1418,11 @@ def _job_status(self, group: list[_Shard]) -> str: def _visible_state(self, t: float, b: int) -> VisibleState: cfg = self.cfg free_mask = self._free_mask() - gpu_temp = np.where( - self.gpu_alive & self.rack_on[self.gpu_rack], - self.rack_air_now[self.gpu_rack] + self.k_gpu * self.gpu_mean_prev, np.nan, - ) + raw_temp = self.rack_air_now[self.gpu_rack] + self.k_gpu * self.gpu_mean_prev + # Precursor offsets are part of what the GPU's own sensor reports, so they are visible. + for r, slot, off in self.signals.gpu_temp_offsets(t): + raw_temp[self.rack_start[r] + slot] += off + gpu_temp = np.where(self.gpu_alive & self.rack_on[self.gpu_rack], raw_temp, np.nan) eff_import = self.site_import * self.signals.import_limit_factor[b] site_power = self.site_power_prev.copy() now = { @@ -1455,6 +1457,8 @@ def _visible_state(self, t: float, b: int) -> VisibleState: steps_since_durable=max(prog - dur, 0.0), flexible=job.flexible, value_per_gpu_hour=job.value_per_gpu_hour, submit_s=job.submit_s, planned_start_s=job.start_s, paused=any(s.paused for s in group), + rack_ids=tuple(sorted({self.rack_ids[int(r)] for s in group if s.gpus.size + for r in np.unique(self.gpu_rack[s.gpus])})), ) ) horizon = cfg.known_future_horizon_s @@ -1907,6 +1911,8 @@ def run(self) -> "SimResult": self.rack_air_b[b] = T1 self.rack_supply_b[b] = Ts1 gtemp_now = self.rack_air_now[self.gpu_rack] + self.k_gpu * self.gpu_mean_prev + for r, slot, off in sig.gpu_temp_offsets(t + self.cdt): + gtemp_now[self.rack_start[r] + slot] += off self.rack_maxgpu_b[b] = np.maximum.reduceat(np.where(up > 0, gtemp_now, -np.inf), self.rack_start) self.rack_throttled_b[b] = throttled_now / self.rack_n self.rack_ratio_b[b] = ratio diff --git a/tests/test_research_heatwave_sim.py b/tests/test_research_heatwave_sim.py index a93baed..d6c4fa2 100644 --- a/tests/test_research_heatwave_sim.py +++ b/tests/test_research_heatwave_sim.py @@ -655,3 +655,27 @@ def test_scenario_v2_calibration_notes_label_levels(): with _pytest.raises(ValueError): _hs.run_scenario(bad, seed=0) _json.dumps(meta, default=str) + + +def test_hardware_fault_precursor_is_visible_and_jobs_report_racks(): + """A GPU-temperature precursor shows in visible now/history before onset; JobView lists racks.""" + jobs = [job("a", n=32, duration=5000.0)] + pre = {"kind": "hardware_fault", "event_id": "hp", "site_id": "site-a", "rack_id": "a1", "gpu_slot": 1, + "onset_s": 3000.0, "repair_s": 600.0, + "precursors": [{"channel": "gpu_temp_c", "lead_s": 1200.0, "strength": 6.0}]} + ra, rb = Recorder(), Recorder() + run_scenario(base_scenario(3600, jobs=jobs, events=[]), controller=ra, seed=3) + run_scenario(base_scenario(3600, jobs=jobs, events=[pre]), controller=rb, seed=3) + t_early = max(t for t in ra.states if t <= 1700.0) + t_late = max(t for t in ra.states if t <= 2900.0) + sa, sb = ra.states[t_early], rb.states[t_early] + assert np.array_equal(sa.now["gpu_temp_c"], sb.now["gpu_temp_c"], equal_nan=True) # before the ramp + sa, sb = ra.states[t_late], rb.states[t_late] + rack = list(sb.rack_ids).index("a1") + diff = np.nan_to_num(sb.now["gpu_temp_c"]) - np.nan_to_num(sa.now["gpu_temp_c"]) + assert diff.max() > 3.0 # ramp is about 6 K * (2900 - 1800) / 1200 by then + _, ha = sa.history("max_gpu_temp_c", 600.0) + _, hb = sb.history("max_gpu_temp_c", 600.0) + assert hb[-1, rack] > ha[-1, rack] + views = [v for v in sb.jobs if v.job_id == "a"] + assert views and views[0].rack_ids and all(r in sb.rack_ids for r in views[0].rack_ids) From 846258bdfd7c0f529260791073f60092f8981346 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 16:31:14 -0700 Subject: [PATCH 22/52] e003 sim: per-GPU temperature history channel and 1-min per-GPU export A single GPU's precursor can sit under a rack's maximum, so a controller needs the per-GPU sensor history to see it. History channel gpu_temp_c, now key gpu_rack_index, arrays gpu_temp_c_1min and gpu_rack_index. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- gpu_stack/research/heatwave_sim.py | 12 ++++++++++++ tests/test_research_heatwave_sim.py | 22 ++++++++++++++++++++++ 2 files changed, 34 insertions(+) diff --git a/gpu_stack/research/heatwave_sim.py b/gpu_stack/research/heatwave_sim.py index a9942fa..3e508e9 100644 --- a/gpu_stack/research/heatwave_sim.py +++ b/gpu_stack/research/heatwave_sim.py @@ -76,6 +76,7 @@ import heapq import json import math +import warnings from dataclasses import asdict, dataclass, field, is_dataclass, replace from pathlib import Path from typing import Any, Mapping, Optional, Protocol, Sequence, Tuple, Union, runtime_checkable @@ -1272,6 +1273,8 @@ def _allocate_outputs(self) -> None: self.rack_air_b = np.zeros((Nb, R)) self.rack_supply_b = np.zeros((Nb, R)) self.rack_maxgpu_b = np.zeros((Nb, R)) + # Per-GPU sensor temperature (float32 to bound memory; NaN when the GPU is down or its rack is off). + self.gpu_temp_b = np.full((Nb, self.G), np.nan, dtype=np.float32) self.rack_throttled_b = np.zeros((Nb, R)) self.rack_ratio_b = np.zeros((Nb, R)) self.rack_removed_b = np.zeros((Nb, R)) @@ -1438,6 +1441,7 @@ def _visible_state(self, t: float, b: int) -> VisibleState: "site_import_limit_w": eff_import, "free_gpus": np.array([int(free_mask[self.place.site_gpus[s]].sum()) for s in range(self.S)]), "gpu_temp_c": gpu_temp, + "gpu_rack_index": self.gpu_rack.copy(), } jobs = [] for ji, job in enumerate(self.jobs): @@ -1484,6 +1488,7 @@ def history(channel: str, window_s: float) -> Tuple[np.ndarray, np.ndarray]: arrays = { "rack_air_c": self.rack_air_b, "supply_c": self.rack_supply_b, "rack_it_power_w": self.rack_it_b, "max_gpu_temp_c": self.rack_maxgpu_b, "breaker_load_ratio": self.rack_ratio_b, + "gpu_temp_c": self.gpu_temp_b, "site_power_w": self.site_power_b, "rack_cooling_power_w": self.rack_cool_b, } if channel not in arrays: @@ -1914,6 +1919,7 @@ def run(self) -> "SimResult": for r, slot, off in sig.gpu_temp_offsets(t + self.cdt): gtemp_now[self.rack_start[r] + slot] += off self.rack_maxgpu_b[b] = np.maximum.reduceat(np.where(up > 0, gtemp_now, -np.inf), self.rack_start) + self.gpu_temp_b[b] = np.where(self.gpu_alive & self.rack_on[self.gpu_rack], gtemp_now, np.nan) self.rack_throttled_b[b] = throttled_now / self.rack_n self.rack_ratio_b[b] = ratio self.rack_removed_b[b] = q_mean @@ -1978,6 +1984,10 @@ def __init__(self, run: _Run) -> None: self.rack_temp_c_1min = run.rack_air_b[: nm * per_min].reshape(nm, per_min, run.R).mean(axis=1) self.rack_supply_c_1min = run.rack_supply_b[: nm * per_min].reshape(nm, per_min, run.R).mean(axis=1) self.rack_max_gpu_temp_c_1min = run.rack_maxgpu_b[: nm * per_min].reshape(nm, per_min, run.R).max(axis=1) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) # all-NaN minutes of a down GPU + self.gpu_temp_c_1min = np.nanmean(run.gpu_temp_b[: nm * per_min].reshape(nm, per_min, run.G), axis=1) + self.gpu_rack_index = run.gpu_rack.copy() H = int(round(cfg.horizon_s / 3600.0)) per_h = int(round(3600.0 / self.dt)) e = self.site_power_w.reshape(H, per_h, run.S).sum(axis=1) * self.dt / 3.6e6 @@ -2093,6 +2103,8 @@ def to_arrays(self) -> dict[str, Any]: "rack_temp_c_1min": self.rack_temp_c_1min, "rack_supply_c_1min": self.rack_supply_c_1min, "rack_max_gpu_temp_c_1min": self.rack_max_gpu_temp_c_1min, + "gpu_temp_c_1min": self.gpu_temp_c_1min, + "gpu_rack_index": self.gpu_rack_index, "site_energy_kwh_1h": self.site_energy_kwh_1h, "site_mean_power_w_1h": self.site_mean_power_w_1h, "site_cost_usd_1h": self.site_cost_usd_1h, diff --git a/tests/test_research_heatwave_sim.py b/tests/test_research_heatwave_sim.py index d6c4fa2..3e98219 100644 --- a/tests/test_research_heatwave_sim.py +++ b/tests/test_research_heatwave_sim.py @@ -679,3 +679,25 @@ def test_hardware_fault_precursor_is_visible_and_jobs_report_racks(): assert hb[-1, rack] > ha[-1, rack] views = [v for v in sb.jobs if v.job_id == "a"] assert views and views[0].rack_ids and all(r in sb.rack_ids for r in views[0].rack_ids) + + +def test_per_gpu_temperature_history_shows_single_gpu_precursor(): + """The per-GPU history channel carries a one-GPU precursor that a rack max can hide.""" + jobs = [job("a", n=32, duration=5000.0)] + pre = {"kind": "hardware_fault", "event_id": "hp", "site_id": "site-a", "rack_id": "a1", "gpu_slot": 1, + "onset_s": 3000.0, "repair_s": 600.0, + "precursors": [{"channel": "gpu_temp_c", "lead_s": 1200.0, "strength": 6.0}]} + ra, rb = Recorder(), Recorder() + res_a = run_scenario(base_scenario(3600, jobs=jobs, events=[]), controller=ra, seed=3) + res_b = run_scenario(base_scenario(3600, jobs=jobs, events=[pre]), controller=rb, seed=3) + t_late = max(t for t in ra.states if t <= 2900.0) + _, ha = ra.states[t_late].history("gpu_temp_c", 300.0) + _, hb = rb.states[t_late].history("gpu_temp_c", 300.0) + gidx = rb.states[t_late].now["gpu_rack_index"] + rack = list(rb.states[t_late].rack_ids).index("a1") + g = int(np.flatnonzero(gidx == rack)[0] + 1) + assert hb[-1, g] - ha[-1, g] > 3.0 + others = np.delete(np.arange(hb.shape[1]), g) + assert np.allclose(np.nan_to_num(hb[-1, others]), np.nan_to_num(ha[-1, others])) + arr = res_b.to_arrays() + assert arr["gpu_temp_c_1min"].shape[1] == gidx.size From 8b8a654651fa10cddd65dc6d7f69a21957e17646 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 16:31:23 -0700 Subject: [PATCH 23/52] Test: drop unused variable Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- tests/test_research_heatwave_sim.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_research_heatwave_sim.py b/tests/test_research_heatwave_sim.py index 3e98219..c968df2 100644 --- a/tests/test_research_heatwave_sim.py +++ b/tests/test_research_heatwave_sim.py @@ -688,7 +688,7 @@ def test_per_gpu_temperature_history_shows_single_gpu_precursor(): "onset_s": 3000.0, "repair_s": 600.0, "precursors": [{"channel": "gpu_temp_c", "lead_s": 1200.0, "strength": 6.0}]} ra, rb = Recorder(), Recorder() - res_a = run_scenario(base_scenario(3600, jobs=jobs, events=[]), controller=ra, seed=3) + run_scenario(base_scenario(3600, jobs=jobs, events=[]), controller=ra, seed=3) res_b = run_scenario(base_scenario(3600, jobs=jobs, events=[pre]), controller=rb, seed=3) t_late = max(t for t in ra.states if t <= 2900.0) _, ha = ra.states[t_late].history("gpu_temp_c", 300.0) From f04ead0fde0b927d079bb6a83d207b058dbde22a Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 16:57:01 -0700 Subject: [PATCH 24/52] e003: scenario v3, job schedule and synthetic environment follow the run seed Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- .../e003-heatwave/sim-scenario-v3.json | 432 ++++++++++++++++++ tests/test_research_heatwave_sim.py | 12 + 2 files changed, 444 insertions(+) create mode 100644 experiments/e003-heatwave/sim-scenario-v3.json diff --git a/experiments/e003-heatwave/sim-scenario-v3.json b/experiments/e003-heatwave/sim-scenario-v3.json new file mode 100644 index 0000000..37ec5bc --- /dev/null +++ b/experiments/e003-heatwave/sim-scenario-v3.json @@ -0,0 +1,432 @@ +{ + "schema": "gpu-stack.e003-heatwave-sim-scenario.v1", + "data_kind": "simulated", + "scenario_id": "e003-heatwave-sim-v3", + "description": "Two-site, eight-rack simulated GPU cluster over seven days with a synthetic job mix, a synthetic heat wave, and scheduled fault and grid events (with and without precursors). Nothing in this file is measured data.", + "start_utc": "2026-07-06T00:00:00Z", + "horizon_s": 604800, + "base_dt_s": 1.0, + "control_dt_s": 10.0, + "controller_interval_s": 60.0, + "history_s": 7200.0, + "known_future_horizon_s": 86400.0, + "sites": [ + { + "site": { + "site_id": "site-a", + "accelerator_type": "NVIDIA H100 SXM 80GB", + "accelerator_count": 104, + "accelerator_flops_per_second": 500000000000000.0, + "collective_bandwidth_bytes_per_second": 400000000000.0, + "state_transfer_bandwidth_bytes_per_second": 12500000000.0, + "checkpoint_bandwidth_bytes_per_second": 10000000000.0, + "base_power_w": 0.0, + "accelerator_power_w": 700.0, + "power_cap_w": 100000.0, + "cooling_capacity_w": 110000.0, + "grid_import_limit_w": 110000.0 + }, + "racks": [ + { + "rack_id": "a1", + "n_gpus": 32, + "breaker_rating_w": 24640.0 + }, + { + "rack_id": "a2", + "n_gpus": 32, + "breaker_rating_w": 24640.0 + }, + { + "rack_id": "a3", + "n_gpus": 24, + "breaker_rating_w": 18480.0 + }, + { + "rack_id": "a4", + "n_gpus": 16, + "breaker_rating_w": 12320.0 + } + ] + }, + { + "site": { + "site_id": "site-b", + "accelerator_type": "NVIDIA H100 SXM 80GB", + "accelerator_count": 80, + "accelerator_flops_per_second": 500000000000000.0, + "collective_bandwidth_bytes_per_second": 400000000000.0, + "state_transfer_bandwidth_bytes_per_second": 12500000000.0, + "checkpoint_bandwidth_bytes_per_second": 10000000000.0, + "base_power_w": 0.0, + "accelerator_power_w": 700.0, + "power_cap_w": 77000.0, + "cooling_capacity_w": 85000.0, + "grid_import_limit_w": 85000.0 + }, + "racks": [ + { + "rack_id": "b1", + "n_gpus": 32, + "breaker_rating_w": 24640.0 + }, + { + "rack_id": "b2", + "n_gpus": 24, + "breaker_rating_w": 18480.0 + }, + { + "rack_id": "b3", + "n_gpus": 16, + "breaker_rating_w": 12320.0 + }, + { + "rack_id": "b4", + "n_gpus": 8, + "breaker_rating_w": 6160.0 + } + ] + } + ], + "gpu": { + "preset": "h100_sxm_80gb_gpu", + "compute_fraction_of_tdp": 0.8550598580496651, + "host_fraction_of_compute": 0.25, + "compute_fraction_of_step": 0.8565830452214802 + }, + "power_levels": { + "e002_raw_path": "docs/data/e002-checkpoint-power-raw-v1.json", + "comm_ratio": 0.2492626550302487, + "checkpoint_ratio": 0.6123595506482253, + "idle_ratio_override": 0.21292761611145372, + "calibration_notes": { + "compute_w": "public data: compute power / 700 W limit = 0.8551 (Elsayed et al. 2026 H100 SXM llm_training, median over 72 GPU-sessions)", + "idle_w": "public data: idle/compute = 0.2129 (Elsayed et al. 2026 H100 SXM llm_training, median over 72 GPU-sessions)", + "comm_w": "public data: low-phase/compute = 0.2493 (Elsayed et al. 2026 H100 SXM llm_training, median over 72 GPU-sessions)", + "checkpoint_w": "measured laptop data: checkpoint/compute = 0.6124 from the NVML energy counter (RTX 3060 Laptop GPU; transfer to a datacenter GPU is an assumption)" + } + }, + "thermal": { + "heat_capacity_j_per_k_per_gpu": 8153.125, + "design_air_rise_k": 12.0, + "cooling_lag_s": 120.0, + "supply_setpoint_c": 24.0, + "outdoor_coupling_k_per_k": 0.2, + "outdoor_reference_c": 30.0, + "capacity_derate_per_k": 0.01, + "capacity_derate_reference_c": 30.0, + "gpu_to_air_k_per_w": 0.02558234685296796, + "gpu_to_air_rel_sigma": 0.1, + "breaker_reset_delay_s": 1800.0, + "gpu_shutdown_c": 95.0, + "thermal_shutdown_hold_s": 1800.0 + }, + "throttle": { + "throttle_threshold_c": 80.0, + "throttle_span_c": 8.0, + "min_clock_fraction": 0.5, + "power_exponent": 2.0 + }, + "cooling_power": { + "kind": "carnot", + "pue": 1.2, + "reference_outdoor_c": 20.0, + "reference_supply_c": 24.0, + "evaporator_approach_k": 10.0, + "condenser_approach_k": 10.0, + "cop_min": 1.5, + "cop_max": 30.0 + }, + "breaker": { + "nontrip_ratio": 1.13, + "trip_ratio": 1.45, + "conventional_time_s": 3600.0 + }, + "transfer": { + "wan_bandwidth_bytes_per_second": 3125000000.0, + "wan_latency_s": 0.02, + "restore_bandwidth_bytes_per_second": 200000000000.0, + "fixed_restart_latency_s": 0.02, + "membership_reconfiguration_s": 0.012, + "checkpoint_bytes_per_gpu": 4375000000.0 + }, + "jobs": { + "generate": { + "utilization_goal": 0.72, + "gpu_choices": [ + 8, + 16, + 32, + 64 + ], + "gpu_weights": [ + 3, + 3, + 2, + 1 + ], + "duration_range_s": [ + 3600, + 172800 + ], + "step_time_choices_s": [ + 2.0, + 4.0, + 8.0 + ], + "checkpoint_every_range_s": [ + 1200, + 3600 + ], + "checkpoint_duration_range_s": [ + 20, + 120 + ], + "value_range": [ + 1.0, + 4.0 + ], + "flexible_fraction": 0.4, + "multisite_fraction": 0.1, + "max_start_delay_s": 21600, + "initial_fraction": 0.5 + } + }, + "events": [ + { + "kind": "cooling_failure", + "event_id": "cf-1-precursor", + "site_id": "site-a", + "rack_id": "a1", + "onset_s": 129600, + "duration_s": 5400, + "remaining_capacity_fraction": 0.3, + "precursors": [ + { + "channel": "supply_temp_c", + "lead_s": 3600, + "strength": 3.0 + } + ] + }, + { + "kind": "cooling_failure", + "event_id": "cf-2-sudden", + "site_id": "site-b", + "rack_id": "b2", + "onset_s": 276480, + "duration_s": 3600, + "remaining_capacity_fraction": 0.2 + }, + { + "kind": "cooling_failure", + "event_id": "cf-3-fan-precursor", + "site_id": "site-a", + "onset_s": 440640, + "duration_s": 7200, + "remaining_capacity_fraction": 0.6, + "resistance_multiplier": 1.3, + "precursors": [ + { + "channel": "thermal_resistance", + "lead_s": 7200, + "strength": 0.25 + } + ] + }, + { + "kind": "breaker_trip", + "event_id": "bt-1-derate-precursor", + "site_id": "site-a", + "rack_id": "a1", + "onset_s": 198720, + "duration_s": 14400, + "mode": "derate", + "derate_fraction": 0.6, + "precursor_lead_s": 3600 + }, + { + "kind": "breaker_trip", + "event_id": "bt-2-forced", + "site_id": "site-b", + "rack_id": "b1", + "onset_s": 380160, + "duration_s": 1200, + "mode": "forced" + }, + { + "kind": "grid_curtailment", + "event_id": "gc-1-announced", + "site_id": "site-a", + "onset_s": 233280, + "duration_s": 7200, + "limit_fraction": 0.6, + "announce_lead_s": 1800 + }, + { + "kind": "grid_curtailment", + "event_id": "gc-2-unannounced", + "site_id": "site-b", + "onset_s": 483840, + "duration_s": 5400, + "limit_fraction": 0.5 + }, + { + "kind": "hardware_fault", + "event_id": "hw-1-precursor", + "site_id": "site-a", + "rack_id": "a2", + "gpu_slot": 5, + "onset_s": 103680, + "repair_s": 21600, + "precursors": [ + { + "channel": "gpu_temp_c", + "lead_s": 7200, + "strength": 6.0 + } + ] + }, + { + "kind": "hardware_fault", + "event_id": "hw-2-sudden", + "site_id": "site-b", + "rack_id": "b1", + "gpu_slot": 3, + "onset_s": 328320, + "repair_s": 14400 + }, + { + "kind": "hardware_fault", + "event_id": "hw-3-sudden", + "site_id": "site-a", + "rack_id": "a1", + "gpu_slot": 2, + "onset_s": 535680, + "repair_s": 14400 + } + ], + "environment": { + "synthetic": { + "start_hour_utc": 0.0, + "sites": { + "site-a": { + "utc_offset_h": -7.0, + "outdoor": { + "mean_c": 24.0, + "diurnal_amplitude_c": 7.0, + "peak_hour_local": 15.0, + "noise_sigma_c": 0.8, + "noise_ar1": 0.9, + "heatwaves": [ + { + "center_h": 96.0, + "width_h": 18.0, + "add_c": 11.0 + } + ] + }, + "forecast_error_sigma_c": 1.2, + "price": { + "base_usd_per_kwh": 0.08, + "diurnal_amplitude": 0.35, + "peak_hour_local": 18.0 + }, + "carbon": { + "base_g_per_kwh": 350.0, + "diurnal_amplitude": 0.2, + "peak_hour_local": 20.0 + } + }, + "site-b": { + "utc_offset_h": -5.0, + "outdoor": { + "mean_c": 22.0, + "diurnal_amplitude_c": 6.0, + "peak_hour_local": 15.0, + "noise_sigma_c": 0.8, + "noise_ar1": 0.9, + "heatwaves": [ + { + "center_h": 100.0, + "width_h": 20.0, + "add_c": 9.0 + } + ] + }, + "forecast_error_sigma_c": 1.2, + "price": { + "base_usd_per_kwh": 0.07, + "diurnal_amplitude": 0.3, + "peak_hour_local": 19.0 + }, + "carbon": { + "base_g_per_kwh": 450.0, + "diurnal_amplitude": 0.15, + "peak_hour_local": 19.0 + } + } + } + } + }, + "outputs": { + "gpu_windows": [ + [ + 0, + 3600 + ], + [ + 129600, + 133200 + ] + ] + }, + "assumptions": [ + "Everything in this scenario is simulated. No number here is a measurement of a real cluster.", + "Site capacities (power cap, cooling capacity, import limit) are scenario choices; the per-GPU accelerator fields follow the e001 screening scenario H100 SXM sites (experiments/e001-beyond-one-datacenter/screening-scenario-v1.json).", + "Compute-phase GPU power is 0.8 x the cited H100 SXM TDP (700 W): the 0.8 is the gpu_stack calculator's gpu_power_fraction assumption. Host overhead is 0.25 x compute power: the calculator's server_power_fraction assumption.", + "Idle/compute and relative compute-phase noise are measured ratios from the e002 laptop RTX telemetry. The communication-phase and checkpoint levels are set equal to the measured idle ratio (structural assumption, no measurement). The measured checkpoint/compute ratio is reported alongside in the result.", + "Fraction of a training step spent in compute (0.7) is an assumption.", + "Rack heat capacity per GPU = DGX H100 system mass 130.45 kg / 8 GPUs x an assumed effective specific heat of 500 J/(kg K). Mass source: https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html", + "Supply setpoint 24 C is an assumption inside the DGX H100 cited operating range of 5-30 C (same source). Design air rise 12 K, cooling lag 120 s, outdoor coupling 0.2 K/K above 30 C, capacity derate 1 percent per K above 30 C, GPU-to-air offset 0.05 K/W, protective GPU shutdown at 95 C and its 1800 s hold are assumptions.", + "Throttle threshold 80 C, span 8 K, minimum clock 0.5 and power exponent 2 are assumptions; they are not H100 datasheet values.", + "Cooling power: Carnot-shaped COP with second-law efficiency chosen so the overhead equals the gpu_stack calculator PUE assumption (1.2) at 20 C outdoor and 24 C supply; approach temperatures 10 K and COP bounds 1.5 to 30 are assumptions.", + "Breaker curve: IEC 60898-1 conventional values (1.13 and 1.45 x rated, 1 hour), https://www.se.com/au/en/faqs/FA369327/. Rack breaker ratings are 1.1 x the full-load rack power; reset delay 1800 s is an assumption.", + "WAN, restore, restart and membership parameters come from the e001 scenarios; the per-GPU checkpoint size is the e001 screening checkpoint (1.12 TB) divided by its 256 GPUs.", + "The job mix, outdoor temperature (including the heat wave), electricity price and carbon intensity are synthetic and seeded. Job value per GPU-hour is a synthetic number with no source.", + "Event precursors (supply-temperature creep, resistance rise, GPU temperature creep, breaker derate ramp, curtailment announcement) are assumptions of the simulation, not measured facts.", + "v2 calibration: compute level, idle level, communication (low-phase) level, compute share of a step, and the GPU-to-air temperature offset are medians from public H100 SXM telemetry (Elsayed et al. 2026, CC BY 4.0); the checkpoint level is a measured laptop ratio. These replace the corresponding v1 values; the v1 text above describing those fields is superseded by this calibration block.", + "v3: the synthetic job schedule and the synthetic environment follow the run seed (v2 fixed them at seeds 7 and 11, so every run seed shared one schedule). Everything else equals v2." + ], + "provenance": [ + "https://www.nvidia.com/en-us/data-center/h100/", + "https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html", + "https://www.se.com/au/en/faqs/FA369327/", + "https://en.wikipedia.org/wiki/Coefficient_of_performance", + "docs/data/e002-checkpoint-power-raw-v1.json", + "experiments/e001-beyond-one-datacenter/recovery-scenario-v2.json", + "experiments/e001-beyond-one-datacenter/screening-scenario-v1.json" + ], + "calibration": { + "file": "heatwave/results/calibration/datacenter_power.json", + "sha256": "43967b1d3a15a5df4ee3b897c5eb6663a73af44b2154406ad5d066d22db07ad1", + "producer_script": "experiments/calibration/datacenter_power.py", + "producer_script_sha256": "f6aa2a6ca6feab65fef67693086f1662e5c157b4e6237c99e090b41638dcf3e3", + "producer_git_commit": "494f1eb58930cd0405e3881d7e021fdabc28ec21", + "fields": { + "gpu.compute_fraction_of_tdp": "compute_fraction_of_limit.median", + "gpu.compute_fraction_of_step": "1 - low_phase_fraction_of_active_time.median", + "power_levels.idle_ratio_override": "idle_ratio.median", + "power_levels.comm_ratio": "low_phase_ratio.median", + "power_levels.checkpoint_ratio": "laptop_checkpoint.ratio (measured laptop)", + "thermal.gpu_to_air_k_per_w": "temp_per_power_k_per_w.median" + } + }, + "derived_from": { + "file": "sim-scenario-v2.json", + "changes": [ + "jobs.generate.seed removed (run seed used)", + "environment.seed removed (run seed used)" + ] + } +} diff --git a/tests/test_research_heatwave_sim.py b/tests/test_research_heatwave_sim.py index c968df2..f8ae6b4 100644 --- a/tests/test_research_heatwave_sim.py +++ b/tests/test_research_heatwave_sim.py @@ -701,3 +701,15 @@ def test_per_gpu_temperature_history_shows_single_gpu_precursor(): assert np.allclose(np.nan_to_num(hb[-1, others]), np.nan_to_num(ha[-1, others])) arr = res_b.to_arrays() assert arr["gpu_temp_c_1min"].shape[1] == gidx.size + + +def test_scenario_v3_schedule_follows_run_seed(): + from pathlib import Path as _Path + + from gpu_stack.research import heatwave_sim as _hs + + sc = _hs.load_scenario(_Path(__file__).resolve().parents[1] / "experiments/e003-heatwave/sim-scenario-v3.json") + a, b = _hs.build_config(sc, seed=100), _hs.build_config(sc, seed=101) + assert [j.job_id for j in a.jobs] != [j.job_id for j in b.jobs] or \ + [j.duration_s for j in a.jobs] != [j.duration_s for j in b.jobs] + assert [j.duration_s for j in _hs.build_config(sc, seed=100).jobs] == [j.duration_s for j in a.jobs] From dab1fd2b0e4efc984d4dd6fbcf709dc5acac2b2c Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 17:03:05 -0700 Subject: [PATCH 25/52] Packing: build on sim scenario v3; drop v2-based part files Co-Authored-By: Claude Sonnet --- .../e003-heatwave/packing-scenario-v1.json | 4 +- .../results/packing-v1-parts/calibration.json | 58 - .../packing-v1-parts/collect-seed100.json | 75 - .../packing-v1-parts/collect-seed100.npz | Bin 424172 -> 0 bytes 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two-site e003 simulator. Every quantity is simulated; every number below is a scenario assumption (nothing is measured data). Power levels, cooling and thermal parameters are read from sim-scenario-v2.json and the measured e002 ratios it points to; the site caps are computed at run time from a commissioning calibration run.", - "base_scenario": "sim-scenario-v2.json", + "description": "Power-cap packing episodes on the two-site e003 simulator. Every quantity is simulated; every number below is a scenario assumption (nothing is measured data). Power levels, cooling and thermal parameters are read from sim-scenario-v3.json and the measured e002 ratios it points to; the site caps are computed at run time from a commissioning calibration run.", + "base_scenario": "sim-scenario-v3.json", "episode": { "horizon_s": 147600, "warmup_s": 90000, diff --git a/experiments/e003-heatwave/results/packing-v1-parts/calibration.json b/experiments/e003-heatwave/results/packing-v1-parts/calibration.json deleted file mode 100644 index 50d0521..0000000 --- a/experiments/e003-heatwave/results/packing-v1-parts/calibration.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "provenance": { - "script": "experiments/e003-heatwave/run_packing.py", - "script_sha256": "5290bbc8759befafe8fa63f93c10a9566b87fa53b7ba79a1d6137a51e643c94c", - "module_sha256": "d4e21491b179416e52d04f620cfaffdd931c658ad702b0e348eb05747de48a79", - "scenario_sha256": "5b7f96ac79b8caa7eb84b4d93849c9046c8b8737f9ac871ecc3ccd1c9b0cee73", - "git": { - "commit": "4b62e940d5afc38d00d1b0baa73d22888c8950dc", - "branch": "hw-pack", - "dirty": "false" - }, - "data_kind": "simulated", - "seed": 9000, - "sim_scenario_sha256": "d7662b1c13bac5870c6c48ea6d1b7e477d906a7655989d02ef6a94be1f8f3e09", - "e002_raw_sha256": "d4095c17409c6cea9de0fa1fe15b85c0957b3619f20c80b6efb508f979a7912e" - }, - "result": { - "data_kind": "simulated", - "design_outdoor_c": 35.0, - "cap_load_fraction": 0.55, - "sites": { - "site-a": { - "site_id": "site-a", - "n_gpus": 104, - "idle_w": 39585.16332307179, - "full_w": 97683.61754063182, - "cap_w": 71539.3131427298, - "beta_w_per_gpu": 558.6389828611541, - "utc_offset_h": -7.0 - }, - "site-b": { - "site_id": "site-b", - "n_gpus": 80, - "idle_w": 30450.125633132066, - "full_w": 75150.40157972567, - "cap_w": 55035.277403758555, - "beta_w_per_gpu": 558.7534493324201, - "utc_offset_h": -5.0 - } - }, - "half_load_check": [ - { - "site_id": "site-a", - "busy_gpus": 52, - "measured_w": 68664.69082728782, - "affine_prediction_w": 68634.3904318518, - "relative_error": -0.0004412805922658555 - }, - { - "site_id": "site-b", - "busy_gpus": 40, - "measured_w": 52802.98472436753, - "affine_prediction_w": 52800.26360642887, - "relative_error": -5.153341147037911e-05 - } - ] - } -} diff --git a/experiments/e003-heatwave/results/packing-v1-parts/collect-seed100.json b/experiments/e003-heatwave/results/packing-v1-parts/collect-seed100.json deleted file mode 100644 index 0208f30..0000000 --- a/experiments/e003-heatwave/results/packing-v1-parts/collect-seed100.json +++ /dev/null @@ -1,75 +0,0 @@ -{ - "provenance": { - "script": "experiments/e003-heatwave/run_packing.py", - "script_sha256": "5290bbc8759befafe8fa63f93c10a9566b87fa53b7ba79a1d6137a51e643c94c", - "module_sha256": "d4e21491b179416e52d04f620cfaffdd931c658ad702b0e348eb05747de48a79", - "scenario_sha256": "5b7f96ac79b8caa7eb84b4d93849c9046c8b8737f9ac871ecc3ccd1c9b0cee73", - "git": { - "commit": "4b62e940d5afc38d00d1b0baa73d22888c8950dc", - "branch": "hw-pack", - "dirty": "false" - }, - "data_kind": "simulated", - "seed": 100, - "role": "train-exploration", - "forecaster": "timestral-5M", - "policy": "random-q", - "options": { - "n_train": 6, - "n_test": 3, - "grid_stride": 1, - "subset_q": "" - } - }, - "result": { - "metrics": { - "data_kind": "simulated", - "window_s": [ - 90000.0, - 147600.0 - ], - "gpu_hours": 1678.0276696667677, - "gpu_hours_multisite_jobs": 157.6793777777778, - "value_usd": 4153.222683807362, - "jobs_admitted": 62, - "jobs_completed": 62, - "violation_rate": 0.9479166666666666, - "violation_rate_by_site": [ - 0.90625, - 0.796875 - ], - "energy_above_cap_kwh": 22.52458006588543, - "energy_above_cap_kwh_by_site": [ - 16.10682958613641, - 6.417750479749019 - ], - "seconds_above_cap": 59410.0, - "max_excess_w": 9888.860442479607, - "stranded_kwh": 19.33435281782451, - "stranded_intervals": 192, - "mean_power_over_cap": 1.000643803335489, - "energy_kwh": 2028.3836759918747, - "forecast_calls": 192, - "forecast_refresh_bins": 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https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- .../results/packing-v1-parts/calibration.json | 58 + .../packing-v1-parts/collect-seed100.json | 75 + .../packing-v1-parts/collect-seed100.npz | Bin 0 -> 419777 bytes .../packing-v1-parts/collect-seed101.json | 75 + .../packing-v1-parts/collect-seed101.npz | Bin 0 -> 413355 bytes .../packing-v1-parts/collect-seed102.json | 75 + .../packing-v1-parts/collect-seed102.npz | Bin 0 -> 448321 bytes .../packing-v1-parts/collect-seed103.json | 75 + .../packing-v1-parts/collect-seed103.npz | Bin 0 -> 405697 bytes .../packing-v1-parts/collect-seed104.json | 75 + .../packing-v1-parts/collect-seed104.npz | Bin 0 -> 421503 bytes .../packing-v1-parts/collect-seed105.json | 75 + .../packing-v1-parts/collect-seed105.npz | Bin 0 -> 452189 bytes .../eval/timestral-5M__q0.01__seed500.json | 77 + .../eval/timestral-5M__q0.01__seed501.json | 77 + .../eval/timestral-5M__q0.01__seed502.json | 77 + .../eval/timestral-5M__q0.1__seed500.json | 77 + .../eval/timestral-5M__q0.1__seed501.json | 77 + .../eval/timestral-5M__q0.1__seed502.json | 77 + .../eval/timestral-5M__q0.2__seed500.json | 77 + .../eval/timestral-5M__q0.2__seed501.json | 77 + .../eval/timestral-5M__q0.2__seed502.json | 77 + .../eval/timestral-5M__q0.3__seed500.json | 77 + .../results/packing-v1-parts/model.json | 6441 +++++++++++++++++ 24 files changed, 7719 insertions(+) create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/calibration.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed100.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed100.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed101.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed101.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed102.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed102.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed103.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed103.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed104.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed104.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed105.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/collect-seed105.npz create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/eval/timestral-5M__q0.01__seed500.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/eval/timestral-5M__q0.01__seed501.json create mode 100644 experiments/e003-heatwave/results/packing-v1-parts/eval/timestral-5M__q0.01__seed502.json create mode 100644 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-0.01817364410058896, + -0.015447837531553574, + -0.013942618649822157, + -0.008895086652464955, + -0.003018743471516172, + 0.0005157005364543246 + ], + [ + -0.06992328734340277, + -0.054660225086858714, + 0.03823687480189172, + -0.011170856195999848, + -0.019171549385665648, + -0.035878381884353, + -0.0321934311070334, + -0.0098888081252391, + -0.009486436539087312, + -0.007908910364992083, + 0.013535940018841017, + 0.011559939400109317, + 0.008345799876429097, + 0.0065765333731796, + 0.008454108055028227, + 0.006931152886877678, + 0.009113730487120473, + 0.009746738652475223, + 0.007002999591510918, + 0.00670692973414124, + 0.0020109249069750375 + ] + ] + } + } +} From 81fd990f3de1477a763a2ac89afea473558ab482 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 17:26:22 -0700 Subject: [PATCH 27/52] Cost audit: work in progress saved after the container restart (sources table, cost-basis presets and calculator changes; tests and audits NOT yet re-verified) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- README.md | 38 +- docs/data/calculator-model.json | 589 ++++++++++++++---- .../e003-heatwave/cost_audit/sources.json | 143 +++++ gpu_stack/calculator.py | 108 +++- gpu_stack/calculator_cost_basis.py | 168 +++++ gpu_stack/presets/__init__.py | 5 +- gpu_stack/presets/dgx_h100_tco.py | 95 ++- gpu_stack/presets/economics.py | 101 ++- gpu_stack/presets/scenarios.py | 1 + gpu_stack/scopes/economics_capex.py | 15 +- gpu_stack/scopes/economics_capex_refs.py | 12 + gpu_stack/scopes/economics_capex_site.py | 68 +- tests/test_dgx_h100_tco_packs.py | 52 +- tests/test_import_registry.py | 26 +- 14 files changed, 1198 insertions(+), 223 deletions(-) create mode 100644 experiments/e003-heatwave/cost_audit/sources.json create mode 100644 gpu_stack/calculator_cost_basis.py diff --git a/README.md b/README.md index 6f8089f..00ec27a 100644 --- a/README.md +++ b/README.md @@ -79,19 +79,19 @@ I built this with AI coding agents. They write quickly, and they grade their own ```text Registry stats: systems 17 - variables 1316 + variables 1319 constants 18 - equations 733 - root_inputs 603 - leaves 251 + equations 735 + root_inputs 604 + leaves 252 Coverage: - non_constant_variables 1298 - with_sp_units 1298 - with_references 1298 - equations 733 - equations_with_references 733 - equations_with_unit_check 670 + non_constant_variables 1301 + with_sp_units 1301 + with_references 1301 + equations 735 + equations_with_references 735 + equations_with_unit_check 672 ``` ## Current Snapshot @@ -99,18 +99,18 @@ Coverage: | Signal | Value | |---|---:| | Systems | 17 | -| Variables | 1316 | +| Variables | 1319 | | Constants | 18 | -| Equations | 733 | -| Root inputs | 603 | -| Leaves | 251 | +| Equations | 735 | +| Root inputs | 604 | +| Leaves | 252 | | Cycles | 0 | -| Topological order length | 1316 | +| Topological order length | 1319 | | Hard audit failures | 0 | -| Non-constant variables with `sp_units` | 1298 | -| Non-constant variables with references | 1298 | -| Equations with references | 733 | -| Equations with unit checks | 670 | +| Non-constant variables with `sp_units` | 1301 | +| Non-constant variables with references | 1301 | +| Equations with references | 735 | +| Equations with unit checks | 672 | | Root-debt families | 146 | | Package version | 0.27.0 | diff --git a/docs/data/calculator-model.json b/docs/data/calculator-model.json index 7c6f461..bedd5df 100644 --- a/docs/data/calculator-model.json +++ b/docs/data/calculator-model.json @@ -88,9 +88,9 @@ "id": "electricity_price", "label": "Electricity price", "unit": "USD/kWh", - "default": 0.0813, + "default": 0.0851, "tag": "assumption", - "tooltip": "What one kilowatt-hour costs. Default is the 2024 U.S. industrial average (EIA).", + "tooltip": "What one kilowatt-hour costs. Default is the 2025 U.S. industrial average (EIA), which already includes the utility's delivery and demand charges.", "advanced": true, "min": 0.0, "max": 2.0 @@ -110,9 +110,9 @@ "id": "useful_life_years", "label": "Years of use before replacement", "unit": "years", - "default": 4.0, + "default": 5.5, "tag": "assumption", - "tooltip": "The hardware's price is spread over this many years. Longer life means cheaper hours.", + "tooltip": "The servers' price is spread over this many years. Default 5.5 is the median of the lives eight operators report for servers (4 to 6 years). The building is spread over 13 years separately. Longer life means cheaper hours.", "advanced": true, "min": 0.5, "max": 15.0 @@ -130,11 +130,11 @@ }, { "id": "other_hardware_fraction", - "label": "Servers, network and building, as a share of GPU price", + "label": "Servers, network and storage, as a share of GPU price", "unit": "fraction", - "default": 0.4, + "default": 0.49, "tag": "assumption", - "tooltip": "Each GPU needs a server, network and space. Default 40% follows the repo's DGX H100 cost preset: server parts about 18% of the GPU price, rack about 5%, building power and cooling about 19%.", + "tooltip": "Each GPU needs a server, network and storage. Default 49%: the non-GPU part of an H100 server (about $7,400 per GPU), plus network ($7,500 per GPU) and other cluster hardware ($3,500 per GPU) for a 24,576-GPU H100 cluster, over a $37,500 card. The building, power and cooling are charged separately.", "advanced": true, "min": 0.0, "max": 3.0 @@ -154,9 +154,9 @@ "id": "server_power_fraction", "label": "Extra server power (CPUs, fans, network)", "unit": "fraction of GPU power", - "default": 0.25, + "default": 0.72, "tag": "assumption", - "tooltip": "Power drawn by the rest of the server, as a share of the GPUs' own power.", + "tooltip": "Power drawn by the rest of the server, as a share of the GPUs' own power. Default 72% makes an 8-GPU H100 node draw 7.7 kW, the measured average during LLM training (the node is rated 10.2 kW).", "advanced": true, "min": 0.0, "max": 2.0 @@ -168,8 +168,8 @@ "peak_flops": 989400000000000.0, "tdp_w": 700.0, "memory_gb": 80.0, - "price_usd": 30000.0, - "price_note": "2024 channel range $27,000-$40,000 per card; $30,000 is the middle (repo DGX H100 preset).", + "price_usd": 37500.0, + "price_note": "2026 new-card range $35,000-$40,000 per card; $37,500 is the middle (IntuitionLabs, Data Center GPU Pricing 2026, accessed 2026-10-26).", "cite": "NVIDIA H100 product page, H100 SXM: BFLOAT16 Tensor Core 1,979 teraFLOPS with sparsity (dense is half, 989.4 teraFLOPS); Max TDP up to 700W; 80 GB memory." }, "A100-80GB-SXM": { @@ -177,8 +177,8 @@ "peak_flops": 312000000000000.0, "tdp_w": 400.0, "memory_gb": 80.0, - "price_usd": 15000.0, - "price_note": "Rough assumption, no citation.", + "price_usd": 12500.0, + "price_note": "New-card range $10,000-$15,000 per card; $12,500 is the middle (Jarvislabs, A100 price, March 2026 snapshot, accessed 2026-10-26).", "cite": "NVIDIA A100 datasheet, 80GB SXM: BFLOAT16 Tensor Core 312 TFLOPS (624 with sparsity); Max TDP SXM 400W; 80 GB memory." } }, @@ -1477,8 +1477,7 @@ "tooltip": "The share of the hardware's price used up during this run.", "expr": [ "mul", - -1.9012852688417367e-07, - "gpu_price", + -6.0, "params", "tokens", [ @@ -1498,24 +1497,45 @@ ], [ "add", - 1.0, - "other_hardware_fraction" - ], - [ - "add", - -1.0, - "residual_fraction" + [ + "mul", + 3.168808781402895e-08, + "gpu_price", + [ + "add", + 1.0, + "other_hardware_fraction" + ], + [ + "add", + -1.0, + "residual_fraction" + ] + ], + [ + "mul", + -2.7544260946040552e-08, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] ] ], "inputs": [ "params", "tokens", "peak_flops", + "tdp_w", "mfu", "gpu_price", "useful_life_years", "residual_fraction", - "other_hardware_fraction" + "other_hardware_fraction", + "server_power_fraction" ], "chain": [ { @@ -1537,7 +1557,7 @@ }, { "variable": "econ.asset.useful_life", - "label": "Hardware life in seconds", + "label": "Server life in seconds", "formula": "years of use x 31,557,600", "unit": "s", "kind": "setting", @@ -1550,7 +1570,7 @@ }, { "variable": "econ.gpu.hourly_amortized", - "label": "Hardware cost per GPU per second", + "label": "GPU, server and network cost per GPU per second", "formula": "(price - resale value) / years of use", "unit": "USD/s", "kind": "graph equation", @@ -1580,6 +1600,51 @@ ] ] }, + { + "variable": "econ.cluster.facility_capex", + "label": "Building, power and cooling cost per GPU", + "formula": "facility USD per W x chip power rating x (1 + extra server power)", + "unit": "USD", + "kind": "setting", + "equation": null, + "expr": [ + "mul", + 11.3, + "tdp_w", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] + }, + { + "variable": "econ.facility.useful_life", + "label": "Building life in seconds", + "formula": "13 years x 31,557,600", + "unit": "s", + "kind": "setting", + "equation": null, + "expr": 410248800.0 + }, + { + "variable": "econ.cluster.facility_capex_rate", + "label": "Building, power and cooling cost per GPU per second", + "formula": "facility cost per GPU / facility life", + "unit": "USD/s", + "kind": "graph equation", + "equation": "econ.eq.cluster_facility_capex_rate", + "expr": [ + "mul", + 2.7544260946040552e-08, + "tdp_w", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] + }, { "variable": "gpu.peak_flops_power_limited", "label": "Speed achieved per GPU", @@ -1596,32 +1661,48 @@ { "variable": "econ.job.capex_rate", "label": "Hardware cost per second for all GPUs", - "formula": "number of GPUs x hardware cost per GPU per second", + "formula": "number of GPUs x (server cost + building cost) per GPU per second", "unit": "USD/s", "kind": "setting", "equation": null, "expr": [ "mul", - 3.168808781402895e-08, - "gpu_price", "n_gpus", - [ - "pow", - "useful_life_years", - -1.0 - ], [ "add", - 1.0, - "other_hardware_fraction" - ], - [ - "add", - 1.0, [ "mul", - -1.0, - "residual_fraction" + 2.7544260946040552e-08, + "tdp_w", + [ + "add", + 1.0, + "server_power_fraction" + ] + ], + [ + "mul", + 3.168808781402895e-08, + "gpu_price", + [ + "pow", + "useful_life_years", + -1.0 + ], + [ + "add", + 1.0, + "other_hardware_fraction" + ], + [ + "add", + 1.0, + [ + "mul", + -1.0, + "residual_fraction" + ] + ] ] ] ] @@ -1852,8 +1933,7 @@ "equation": "econ.eq.run_hw_cost", "expr": [ "mul", - -1.9012852688417367e-07, - "gpu_price", + -6.0, "params", "tokens", [ @@ -1873,13 +1953,32 @@ ], [ "add", - 1.0, - "other_hardware_fraction" - ], - [ - "add", - -1.0, - "residual_fraction" + [ + "mul", + 3.168808781402895e-08, + "gpu_price", + [ + "add", + 1.0, + "other_hardware_fraction" + ], + [ + "add", + -1.0, + "residual_fraction" + ] + ], + [ + "mul", + -2.7544260946040552e-08, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] ] ] } @@ -1913,7 +2012,18 @@ "add", [ "mul", - -0.1140771161305042, + 0.09915933940574599, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ], + [ + "mul", + -0.11407711613050421, "gpu_price", [ "add", @@ -1976,7 +2086,7 @@ }, { "variable": "econ.asset.useful_life", - "label": "Hardware life in seconds", + "label": "Server life in seconds", "formula": "years of use x 31,557,600", "unit": "s", "kind": "setting", @@ -1989,7 +2099,7 @@ }, { "variable": "econ.gpu.hourly_amortized", - "label": "Hardware cost per GPU per second", + "label": "GPU, server and network cost per GPU per second", "formula": "(price - resale value) / years of use", "unit": "USD/s", "kind": "graph equation", @@ -2019,6 +2129,51 @@ ] ] }, + { + "variable": "econ.cluster.facility_capex", + "label": "Building, power and cooling cost per GPU", + "formula": "facility USD per W x chip power rating x (1 + extra server power)", + "unit": "USD", + "kind": "setting", + "equation": null, + "expr": [ + "mul", + 11.3, + "tdp_w", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] + }, + { + "variable": "econ.facility.useful_life", + "label": "Building life in seconds", + "formula": "13 years x 31,557,600", + "unit": "s", + "kind": "setting", + "equation": null, + "expr": 410248800.0 + }, + { + "variable": "econ.cluster.facility_capex_rate", + "label": "Building, power and cooling cost per GPU per second", + "formula": "facility cost per GPU / facility life", + "unit": "USD/s", + "kind": "graph equation", + "equation": "econ.eq.cluster_facility_capex_rate", + "expr": [ + "mul", + 2.7544260946040552e-08, + "tdp_w", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] + }, { "variable": "gpu.peak_flops_power_limited", "label": "Speed achieved per GPU", @@ -2055,32 +2210,48 @@ { "variable": "econ.job.capex_rate", "label": "Hardware cost per second for all GPUs", - "formula": "number of GPUs x hardware cost per GPU per second", + "formula": "number of GPUs x (server cost + building cost) per GPU per second", "unit": "USD/s", "kind": "setting", "equation": null, "expr": [ "mul", - 3.168808781402895e-08, - "gpu_price", "n_gpus", - [ - "pow", - "useful_life_years", - -1.0 - ], - [ - "add", - 1.0, - "other_hardware_fraction" - ], [ "add", - 1.0, [ "mul", - -1.0, - "residual_fraction" + 2.7544260946040552e-08, + "tdp_w", + [ + "add", + 1.0, + "server_power_fraction" + ] + ], + [ + "mul", + 3.168808781402895e-08, + "gpu_price", + [ + "pow", + "useful_life_years", + -1.0 + ], + [ + "add", + 1.0, + "other_hardware_fraction" + ], + [ + "add", + 1.0, + [ + "mul", + -1.0, + "residual_fraction" + ] + ] ] ] ] @@ -2386,8 +2557,7 @@ "equation": "econ.eq.run_hw_cost", "expr": [ "mul", - -1.9012852688417367e-07, - "gpu_price", + -6.0, "params", "tokens", [ @@ -2407,13 +2577,32 @@ ], [ "add", - 1.0, - "other_hardware_fraction" - ], - [ - "add", - -1.0, - "residual_fraction" + [ + "mul", + 3.168808781402895e-08, + "gpu_price", + [ + "add", + 1.0, + "other_hardware_fraction" + ], + [ + "add", + -1.0, + "residual_fraction" + ] + ], + [ + "mul", + -2.7544260946040552e-08, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] ] ] }, @@ -2448,7 +2637,18 @@ "add", [ "mul", - -0.1140771161305042, + 0.09915933940574599, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ], + [ + "mul", + -0.11407711613050421, "gpu_price", [ "add", @@ -2506,7 +2706,18 @@ "add", [ "mul", - -0.1140771161305042, + 0.09915933940574599, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ], + [ + "mul", + -0.11407711613050421, "gpu_price", [ "add", @@ -2568,7 +2779,7 @@ }, { "variable": "econ.asset.useful_life", - "label": "Hardware life in seconds", + "label": "Server life in seconds", "formula": "years of use x 31,557,600", "unit": "s", "kind": "setting", @@ -2581,7 +2792,7 @@ }, { "variable": "econ.gpu.hourly_amortized", - "label": "Hardware cost per GPU per second", + "label": "GPU, server and network cost per GPU per second", "formula": "(price - resale value) / years of use", "unit": "USD/s", "kind": "graph equation", @@ -2611,6 +2822,51 @@ ] ] }, + { + "variable": "econ.cluster.facility_capex", + "label": "Building, power and cooling cost per GPU", + "formula": "facility USD per W x chip power rating x (1 + extra server power)", + "unit": "USD", + "kind": "setting", + "equation": null, + "expr": [ + "mul", + 11.3, + "tdp_w", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] + }, + { + "variable": "econ.facility.useful_life", + "label": "Building life in seconds", + "formula": "13 years x 31,557,600", + "unit": "s", + "kind": "setting", + "equation": null, + "expr": 410248800.0 + }, + { + "variable": "econ.cluster.facility_capex_rate", + "label": "Building, power and cooling cost per GPU per second", + "formula": "facility cost per GPU / facility life", + "unit": "USD/s", + "kind": "graph equation", + "equation": "econ.eq.cluster_facility_capex_rate", + "expr": [ + "mul", + 2.7544260946040552e-08, + "tdp_w", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] + }, { "variable": "gpu.peak_flops_power_limited", "label": "Speed achieved per GPU", @@ -2647,32 +2903,48 @@ { "variable": "econ.job.capex_rate", "label": "Hardware cost per second for all GPUs", - "formula": "number of GPUs x hardware cost per GPU per second", + "formula": "number of GPUs x (server cost + building cost) per GPU per second", "unit": "USD/s", "kind": "setting", "equation": null, "expr": [ "mul", - 3.168808781402895e-08, - "gpu_price", "n_gpus", - [ - "pow", - "useful_life_years", - -1.0 - ], [ "add", - 1.0, - "other_hardware_fraction" - ], - [ - "add", - 1.0, [ "mul", - -1.0, - "residual_fraction" + 2.7544260946040552e-08, + "tdp_w", + [ + "add", + 1.0, + "server_power_fraction" + ] + ], + [ + "mul", + 3.168808781402895e-08, + "gpu_price", + [ + "pow", + "useful_life_years", + -1.0 + ], + [ + "add", + 1.0, + "other_hardware_fraction" + ], + [ + "add", + 1.0, + [ + "mul", + -1.0, + "residual_fraction" + ] + ] ] ] ] @@ -2978,8 +3250,7 @@ "equation": "econ.eq.run_hw_cost", "expr": [ "mul", - -1.9012852688417367e-07, - "gpu_price", + -6.0, "params", "tokens", [ @@ -2999,13 +3270,32 @@ ], [ "add", - 1.0, - "other_hardware_fraction" - ], - [ - "add", - -1.0, - "residual_fraction" + [ + "mul", + 3.168808781402895e-08, + "gpu_price", + [ + "add", + 1.0, + "other_hardware_fraction" + ], + [ + "add", + -1.0, + "residual_fraction" + ] + ], + [ + "mul", + -2.7544260946040552e-08, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ] ] ] }, @@ -3040,7 +3330,18 @@ "add", [ "mul", - -0.1140771161305042, + 0.09915933940574599, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ], + [ + "mul", + -0.11407711613050421, "gpu_price", [ "add", @@ -3099,7 +3400,18 @@ "add", [ "mul", - -0.1140771161305042, + 0.09915933940574599, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ], + [ + "mul", + -0.11407711613050421, "gpu_price", [ "add", @@ -3158,7 +3470,18 @@ "add", [ "mul", - -0.1140771161305042, + 0.09915933940574599, + "tdp_w", + "useful_life_years", + [ + "add", + 1.0, + "server_power_fraction" + ] + ], + [ + "mul", + -0.11407711613050421, "gpu_price", [ "add", @@ -3263,22 +3586,22 @@ "tdp_w": 700.0, "mfu": 0.4, "pue": 1.2, - "electricity_price": 0.0813, - "gpu_price": 30000.0, - "useful_life_years": 4.0, + "electricity_price": 0.0851, + "gpu_price": 37500.0, + "useful_life_years": 5.5, "residual_fraction": 0.05, - "other_hardware_fraction": 0.4, + "other_hardware_fraction": 0.49, "gpu_power_fraction": 0.8, - "server_power_fraction": 0.25 + "server_power_fraction": 0.72 }, "expected": { "training_days": 2.3990190112413807, "gpu_hours": 58958.291220268184, - "energy_mwh": 49.52496462502528, - "electricity_cost": 4026.3796240145543, - "hardware_cost": 67089.77354804653, - "total_cost": 71116.15317206085, - "cost_per_million_tokens": 0.035558076586030496 + "energy_mwh": 68.14635132403478, + "electricity_cost": 5799.254497675358, + "hardware_cost": 71950.43745984737, + "total_cost": 77749.6919575227, + "cost_per_million_tokens": 0.03887484597876143 } }, { @@ -3291,22 +3614,22 @@ "tdp_w": 400.0, "mfu": 0.5, "pue": 1.2, - "electricity_price": 0.0813, - "gpu_price": 15000.0, - "useful_life_years": 4.0, + "electricity_price": 0.0851, + "gpu_price": 12500.0, + "useful_life_years": 5.5, "residual_fraction": 0.05, - "other_hardware_fraction": 0.4, + "other_hardware_fraction": 0.49, "gpu_power_fraction": 0.8, - "server_power_fraction": 0.25 + "server_power_fraction": 0.72 }, "expected": { "training_days": 21.301443420584047, "gpu_hours": 1047008.5470085471, - "energy_mwh": 502.56410256410265, - "electricity_cost": 40858.46153846153, - "hardware_cost": 595705.5815885392, - "total_cost": 636564.0431269989, - "cost_per_million_tokens": 0.4546886022335715 + "energy_mwh": 691.5282051282053, + "electricity_cost": 58849.05025641024, + "hardware_cost": 455671.61918580806, + "total_cost": 514520.6694422182, + "cost_per_million_tokens": 0.36751476388729937 } }, { @@ -3332,9 +3655,9 @@ "gpu_hours": 1263.3919547200323, "energy_mwh": 1.5520770163735598, "electricity_cost": 310.41540327471205, - "hardware_cost": 1513.3031627378919, - "total_cost": 1823.7185660125983, - "cost_per_million_tokens": 0.006079061886708674 + "hardware_cost": 1627.3053343308086, + "total_cost": 1937.7207376055196, + "cost_per_million_tokens": 0.006459069125351745 } } ] diff --git a/experiments/e003-heatwave/cost_audit/sources.json b/experiments/e003-heatwave/cost_audit/sources.json new file mode 100644 index 0000000..23d4359 --- /dev/null +++ b/experiments/e003-heatwave/cost_audit/sources.json @@ -0,0 +1,143 @@ +{ + "schema": "gpu-stack.e003-cost-audit-sources.v1", + "description": "Cited inputs for the electricity-share audit (T10). Every row names a source id from `sources`; every figure in the audit results is read from this file by run_cost_audit.py. Access date for every web source is 2026-10-26 unless a row says otherwise. Evidence strings marked verbatim are exact quotes returned by the fetch; strings marked extract are the fetch tool's summary of the page, not a quote.", + "access_date": "2026-10-26", + "fetch_notes": [ + "nvidia.com product pages (H100, H200, DGX B200, GB200 NVL72) returned URL_NOT_ON_ALLOWLIST from this session's fetch tool and were not retrieved; NVIDIA datasheets hosted by PNY, HPE, Supermicro, and NVIDIA's images.nvidia.com and docs.nvidia.com were retrieved instead.", + "eia.gov Electric Power Monthly state tables (5.6.A) could not be read (data is behind spreadsheet links); the Electric Power Annual Table 2.10 (2025 and 2024 by state) was read instead.", + "sec.gov filings (CoreWeave, Alphabet, Oracle, Microsoft, Meta, Amazon 10-Ks) are reachable but the fetch tool cuts the text before the financial-statement notes, so useful lives were read from company press releases and third-party renderings of the filings; those rows are marked.", + "nectarclimate.com returned a client error and was not used; PG&E and Dominion tariff figures come from OpenEI's U.S. Utility Rate Database pages." + ], + "sources": { + "INTUITION_PRICING": {"title": "Data Center GPU Pricing 2026", "publisher": "IntuitionLabs", "url": "https://intuitionlabs.ai/articles/data-center-gpu-pricing-2026", "published": "2026-07-20 (updated 2026-10-11)", "kind": "secondary aggregator; cites CloudZero, DeployBase, Spheron, GPUaaS, GigaEnergy"}, + "AMCOMPUTE_VALUES": {"title": "GPU server values (October 2026 Rack Report, data through August 2026)", "publisher": "AMCompute", "url": "https://www.amcompute.com/gpu-server-values", "published": "2026-10-17", "kind": "secondary; vendor price index, values described as indicative, not an appraisal"}, + "AMCOMPUTE_CAPEX": {"title": "AI Cluster Cost Breakdown: CapEx (2026)", "publisher": "AMCompute", "url": "https://www.amcompute.com/blog/ai-cluster-cost-breakdown-capex", "published": "2026-03-02 (cites a B300 BOM dated October 2026)", "kind": "secondary; vendor blog with its own per-GPU arithmetic"}, + "WING_NVL72": {"title": "How Much Does an NVIDIA NVL72 Cost?", "publisher": "Wing VC", "url": "https://www.wing.vc/content/how-much-does-an-nvidia-nvl72-cost", "published": "2026-08", "kind": "secondary; cites Tae Kim (Barron's), Tom's Hardware, Data Gravity purchase-order review"}, + "JARVIS_A100": {"title": "NVIDIA A100 GPU Price in 2026", "publisher": "Jarvislabs", "url": "https://jarvislabs.ai/blog/a100-price", "published": "2026-03 snapshot (updated 2026-10)", "kind": "secondary; GPU cloud vendor blog"}, + "HOSTINGER_GPU": {"title": "Cloud GPU pricing compared: what GPUs cost per hour in 2026", "publisher": "Hostinger", "url": "https://www.hostinger.com/tutorials/?p=158956", "published": "2026-10-17 (rates verified 2026-09)", "kind": "secondary; list rates, on-demand"}, + "LAMBDA_PRICING": {"title": "Lambda pricing", "publisher": "Lambda", "url": "https://www.lambda.ai/pricing", "published": "undated (page footer 2026)", "kind": "primary vendor price page"}, + "SPHERON_CW": {"title": "CoreWeave GPU Pricing 2026", "publisher": "Spheron", "url": "https://www.spheron.network/blog/coreweave-gpu-pricing-2026/", "published": "2026-08-12 (updated 2026-10-19; CoreWeave rate card read 2026-08-12)", "kind": "secondary; reads CoreWeave's rate card"}, + "SPHERON_RESERVE": {"title": "GPU Cluster Reservation Contract Negotiation 2026", "publisher": "Spheron", "url": "https://www.spheron.network/blog/gpu-cluster-reservation-contract-negotiation-2026/", "published": "2026-07-13 (updated 2026-10-19)", "kind": "secondary; cites Compute Exchange discount ranges"}, + "PNY_H200": {"title": "NVIDIA H200 NVL datasheet (NVIDIA datasheet, PNY-hosted)", "publisher": "NVIDIA via PNY", "url": "https://www.pny.com/File Library/Company/Support/linecards/data-center-gpus/h200-nvl-datasheet.pdf", "published": "undated", "kind": "primary datasheet, third-party host"}, + "NVIDIA_HGX_B200": {"title": "HGX B200 PCF Summary", "publisher": "NVIDIA", "url": "https://images.nvidia.com/aem-dam/Solutions/documents/HGX-B200-PCF-Summary.pdf", "published": "undated", "kind": "primary"}, + "NVIDIA_DGX_B200_UG": {"title": "Introduction to NVIDIA DGX B200 Systems", "publisher": "NVIDIA", "url": "https://docs.nvidia.com/dgx/dgxb200-user-guide/introduction-to-dgxb200.html", "published": "undated", "kind": "primary"}, + "NVIDIA_DGX_B200_DS": {"title": "DGX B200 datasheet (NVIDIA datasheet, third-party host)", "publisher": "NVIDIA via gpusolutions.hrp.hu", "url": "https://gpusolutions.hrp.hu/wp-content/uploads/2026/05/dgx-scale-ai-infrastructure-dgx-b200-datasheet-nvidia-web.pdf", "published": "2026-05 upload", "kind": "primary datasheet, third-party host"}, + "HPE_GB200": {"title": "HPE NVIDIA GB200 NVL72 by HPE QuickSpecs", "publisher": "HPE", "url": "https://www.hpe.com/us/en/collaterals/collateral.a50009224enw.html", "published": "undated", "kind": "primary vendor spec"}, + "SUPERMICRO_GB200": {"title": "SuperCluster SRS-GB200-NVL72", "publisher": "Supermicro", "url": "https://supermicro.com/en/products/system/gpu/48u/srs-gb200-nvl72", "published": "undated", "kind": "primary vendor spec"}, + "KLOCKWOOD_B200": {"title": "NVIDIA B200 (Glenn K. Lockwood, digital garden)", "publisher": "Glenn K. Lockwood", "url": "https://glennklockwood.com/garden/processors/b200", "published": "undated", "kind": "secondary; cites NVIDIA Blackwell Architecture Technical Brief"}, + "CYFUTURE_GB200": {"title": "NVIDIA GB200 GPU server", "publisher": "Cyfuture", "url": "https://cyfuture.ai/nvidia-gb200-gpu-server", "published": "undated", "kind": "secondary reseller page"}, + "REPO_HARDWARE": {"title": "gpu_stack/presets/hardware.py and scenarios.py (cite NVIDIA H100 product page accessed 2026-05-06 and DGX H100/H200 User Guide Table 3 accessed 2026-06-10)", "publisher": "gpu_stack repository", "url": "https://www.nvidia.com/en-us/data-center/h100/", "published": "n/a", "kind": "primary NVIDIA text as already cited in the repo; not re-fetched here (nvidia.com not on this session's allowlist)"}, + "META_Q424": {"title": "Meta Reports Fourth Quarter and Full Year 2024 Results", "publisher": "Meta Platforms", "url": "https://investor.atmeta.com/investor-news/press-release-details/2025/Meta-Reports-Fourth-Quarter-and-Full-Year-2024-Results/default.aspx", "published": "2025-01-29", "kind": "primary press release"}, + "ALPHABET_Q422": {"title": "Alphabet Q4 2022 earnings release", "publisher": "Alphabet", "url": "https://abc.xyz/investor/static/pdf/2022Q4_alphabet_earnings_release.pdf", "published": "2023-02-02", "kind": "primary press release"}, + "EDGAR_AMZN": {"title": "Amazon 10-K FY2025, property and equipment, estimated useful lives as of 2025-12-31", "publisher": "edgar.tools rendering of the SEC filing", "url": "https://app.edgar.tools/companies/AMZN/disclosures/property-plant-equipment", "published": "FY2025 10-K (filed 2026)", "kind": "primary filing text rendered by a third party"}, + "HUDSON_MSFT": {"title": "Microsoft server useful life", "publisher": "Hudson Labs", "url": "https://www.hudson-labs.com/research/msft-server-useful-life", "published": "undated", "kind": "secondary; summarizes Microsoft 10-Ks"}, + "GDCH_LIVES": {"title": "How long does an AI chip last", "publisher": "Global Data Center Hub", "url": "https://www.globaldatacenterhub.com/p/how-long-does-an-ai-chip-last-and", "published": "undated", "kind": "secondary; cites annual reports and earnings releases"}, + "CUBE_LIVES": {"title": "Resetting GPU depreciation", "publisher": "theCUBE Research", "url": "https://thecuberesearch.com/298-breaking-analysis-resetting-gpu-depreciation-why-ai-factories-bend-but-dont-break-useful-life-assumptions/", "published": "2025-2026 (undated in fetch)", "kind": "secondary"}, + "BLOCKSPACE_CRWV": {"title": "CoreWeave A100 contract challenges GPU depreciation model", "publisher": "Blockspace Media", "url": "https://blockspace.media/insight/coreweave-a100-contract-challenges-gpu-depreciation-model/", "published": "2026", "kind": "secondary; cites CNBC 2025-11-14"}, + "REMIO_BERNSTEIN": {"title": "Bernstein: AI data center costs hit $39.5 billion per GW", "publisher": "remio.ai", "url": "https://www.remio.ai/post/bernstein-ai-data-center-costs-hit-39-5-billion-per-gw-but-depreciation-is-the-b", "published": "2026-10-11", "kind": "secondary; summarizes Bernstein"}, + "INVESTING_BERNSTEIN": {"title": "How much does a GW of data center capacity actually cost", "publisher": "Investing.com (Bernstein analysis)", "url": "https://www.investing.com/news/stock-market-news/how-much-does-a-gw-of-data-center-capacity-actually-cost-4314046", "published": "2025-11-01", "kind": "secondary; summarizes Bernstein"}, + "SILICONDATA": {"title": "H100 GPU market value trends", "publisher": "Silicon Data", "url": "https://www.silicondata.com/use-cases/h100-gpu-market-value-trends", "published": "data window 2024-06 to 2025-12", "kind": "secondary index vendor; text and table disagree"}, + "UPTIME": {"title": "UII Update 518: growing PUE advantage of larger data centers", "publisher": "Uptime Institute Intelligence", "url": "https://intelligence.uptimeinstitute.com/resource/growing-pue-advantage-larger-data-centers", "published": "2026-08-06", "kind": "primary survey summary"}, + "GOOGLE_PUE": {"title": "Growing the internet while reducing energy consumption", "publisher": "Google Data Centers", "url": "https://www.datacenters.google/efficiency", "published": "page current through 2026 Q2", "kind": "primary operator report"}, + "MSFT_PUE": {"title": "Measuring energy and water efficiency for Microsoft datacenters", "publisher": "Microsoft", "url": "https://datacenters.microsoft.com/sustainability/efficiency/", "published": "FY25", "kind": "primary operator report"}, + "AWS_PUE": {"title": "AWS sustainability", "publisher": "Amazon Web Services", "url": "https://aws.amazon.com/sustainability/", "published": "2025 data", "kind": "primary operator report"}, + "DCMAG_META": {"title": "Data Centre Magazine, February 2026, issue 38, page 41", "publisher": "Data Centre Magazine", "url": "https://publication.datacentremagazine.com/data-centre-magazine-february-2026-issue-38/0739753001769685783/p41", "published": "2026-02-03", "kind": "secondary; no measurement year"}, + "JLL_COST": {"title": "Data Centre Construction Costs Rise to $11.3M per MW in 2026", "publisher": "datacentres.com (JLL forecast)", "url": "https://www.datacentres.com/news/data-centre-construction-costs-11m-per-mw", "published": "2026", "kind": "secondary; cites JLL"}, + "CW_IREI": {"title": "Cushman & Wakefield releases 2026 Data Center Development Cost Guide", "publisher": "IREI", "url": "https://irei.com/news/cushman-wakefield-releases-2026-data-center-development-cost-guide-citing-21-rise-in-per-mw-construction-costs/", "published": "2026-08-20", "kind": "secondary; paywalled after the first paragraph"}, + "EIA_EPA_210": {"title": "Electric Power Annual, Table 2.10, Average Price of Electricity to Ultimate Customers by End-Use Sector, by State, 2025 and 2024 (cents per kWh)", "publisher": "U.S. Energy Information Administration", "url": "https://www.eia.gov/electricity/annual/html/epa_02_10.html", "published": "2026", "kind": "primary"}, + "OPENEI_PGE": {"title": "PG&E E-20, maximum demand 1000 kW or more, Option R (Transmission), effective 2026-03-27", "publisher": "OpenEI U.S. Utility Rate Database", "url": "https://apps.openei.org/USURDB/rate/view/69de826508c252b4010e1d40", "published": "rate effective 2026-03-27", "kind": "primary tariff data entered into a public database"}, + "ENERGYMAG_4CP": {"title": "Miner Weekly: Texas' Peak Game Is Getting Harder as AI Load Floods ERCOT", "publisher": "TheEnergyMag (cites LoD estimate)", "url": "https://theenergymag.com/news/2026-06-11/texas-4cp-ai-bitcoin", "published": "2026-06-11", "kind": "secondary"}, + "WAVY_GS5": {"title": "State Corporation Commission approves new Dominion rate changes for big electricity users", "publisher": "WAVY", "url": "https://www.wavy.com/news/virginia/state-corporation-commission-approves-new-dominion-rate-changes-for-big-electricity-users/amp/", "published": "2025-11-25", "kind": "secondary news"}, + "BDEW": {"title": "BDEW-Strompreisanalyse Herbst 2026", "publisher": "BDEW", "url": "https://www.bdew.de/service/daten-und-grafiken/bdew-strompreisanalyse/", "published": "2026-08-21", "kind": "primary industry association"}, + "DCD_HQ": {"title": "Canadian utility Hydro-Quebec proposes electricity tariff for data centers", "publisher": "DatacenterDynamics", "url": "https://www.datacenterdynamics.com/en/news/canadian-utility-hydro-qu%C3%A9bec-proposes-electricity-tariff-for-data-centers/", "published": "2026-02-20", "kind": "secondary news"}, + "ARXIV_NODE_POWER": {"title": "Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation", "publisher": "arXiv 2506.14551", "url": "https://arxiv.org/pdf/2506.14551", "published": "2025", "kind": "primary paper (measurements by BNL and public MLPerf Power data)"}, + "LBNL_NODE_POWER": {"title": "Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System", "publisher": "Lawrence Berkeley National Laboratory", "url": "https://eta-publications.lbl.gov/sites/default/files/2026-07/single-node_power_demand_during_ai_training.pdf", "published": "2025-03-25 (paper), posted 2026-07", "kind": "primary measurement paper"}, + "REPO_COST_V1": {"title": "experiments/e003-heatwave/results/cost-v1.json (public CAISO NP15 day-ahead LMP, DE-LU day-ahead price, ECB FX; test period 2026-04-27 to 2026-10-25)", "publisher": "gpu_stack repository, e003", "url": "experiments/e003-heatwave/results/cost-v1.json", "published": "2026-10-26", "kind": "repository result built from public series"} + }, + "rows": [ + {"id": "price.h100.gpu_sxm5", "group": "price", "chip": "H100", "item": "H100 SXM5 80GB, new, per GPU", "unit": "USD/GPU", "low": 35000, "high": 40000, "value": 37500, "basis": "midpoint of cited range", "source": "INTUITION_PRICING", "evidence": "extract: Table 2, H100 SXM5 80GB per GPU new $35,000 to $40,000, verified Q1 to Q2 2026, citing CloudZero", "current": {"value": 30000, "where": "gpu_stack/calculator.py GPUS['H100-SXM'].price_usd; presets/dgx_h100_tco.py econ.gpu.capex (2024 channel range 27,000-40,000, midpoint-ish 30,000)"}}, + {"id": "price.h100.sys_dgx", "group": "price", "chip": "H100", "item": "DGX H100 8-GPU system, new", "unit": "USD/system", "low": 300000, "high": 460000, "value": 380000, "basis": "midpoint of cited range", "source": "INTUITION_PRICING", "evidence": "extract: DGX H100 8-GPU system $300,000 to $460,000, Q1 to Q2 2026, citing DeployBase", "current": null}, + {"id": "price.h100.sys_hgx_new", "group": "price", "chip": "H100", "item": "HGX H100 8-GPU server, new (estimated from GPU list price, not a quoted server price)", "unit": "USD/system", "value": 339000, "basis": "quoted estimate", "source": "AMCOMPUTE_VALUES", "evidence": "extract: HGX H100 new about $339,000, 'estimated from GPU list price; not a quoted server price' (August 2026 data)", "current": null}, + {"id": "price.h100.sys_hgx_used", "group": "residual", "chip": "H100", "item": "HGX H100 8-GPU server, used fair market value", "unit": "USD/system", "value": 195000, "basis": "quoted", "source": "AMCOMPUTE_VALUES", "evidence": "extract: HGX H100 used (fair market) $195,000; orderly liquidation $173,000; forced $126,000 (August 2026 data); values built from listings, indicative, not an appraisal", "current": {"value": 0.05, "where": "calculator.py INPUTS residual_fraction (resale value at end of life, share of price)"}}, + {"id": "price.h200.sys_hgx_new", "group": "price", "chip": "H200", "item": "HGX H200 8-GPU server, new (Rack Report midpoint)", "unit": "USD/system", "value": 340000, "basis": "quoted midpoint", "source": "AMCOMPUTE_VALUES", "evidence": "extract: HGX H200 new about $340,000 (Rack Report new-price midpoint), used $348,500 on a limited sample", "current": null}, + {"id": "price.h200.sys_dgx", "group": "price", "chip": "H200", "item": "DGX H200 8-GPU system, new", "unit": "USD/system", "low": 400000, "high": 500000, "value": 450000, "basis": "midpoint of cited range", "source": "INTUITION_PRICING", "evidence": "extract: DGX H200 8-GPU system $400,000 to $500,000, 'quoted range per market analysis'; no per-GPU H200 price stated", "current": null}, + {"id": "price.b200.gpu", "group": "price", "chip": "B200", "item": "B200 new card, per GPU", "unit": "USD/GPU", "low": 30000, "high": 50000, "value": 40000, "basis": "midpoint of cited range", "source": "INTUITION_PRICING", "evidence": "extract: B200 per GPU $30,000 to $50,000, general range, no specific date, citing CloudZero", "current": null}, + {"id": "price.b200.sys_hgx", "group": "price", "chip": "B200", "item": "HGX B200 8-GPU server, new", "unit": "USD/system", "low": 360000, "high": 470000, "value": 415000, "basis": "midpoint of cited range", "source": "AMCOMPUTE_VALUES", "evidence": "extract: HGX B200 new $360,000-$470,000 (August 2026 data); no used sales yet", "current": null}, + {"id": "price.gb200.rack", "group": "price", "chip": "GB200", "item": "GB200 NVL72 rack, 72 GPUs, new", "unit": "USD/rack", "low": 2800000, "high": 3400000, "value": 3000000, "basis": "Tae Kim (Barron's) point value; Tom's Hardware range", "source": "WING_NVL72", "evidence": "extract: about $3 million per rack per Tae Kim (Barron's); Tom's Hardware reports $2.8-3.4M; the article does not date the GB200 figures; page dated August 2026", "current": null}, + {"id": "price.gb200.rack_bernstein", "group": "price", "chip": "GB200", "item": "GB200 NVL72 rack, all-in per Bernstein: compute hardware part and physical infrastructure part", "unit": "USD/rack", "value": 5900000, "compute_part": 3400000, "infra_part": 2500000, "basis": "quoted", "source": "REMIO_BERNSTEIN", "evidence": "extract: per-rack GB200 $5.9M; about $3.4 million compute hardware and $2.5 million physical infrastructure", "current": null}, + {"id": "price.a100.sxm80", "group": "price", "chip": "A100", "item": "A100 80GB SXM, new, per GPU (March 2026)", "unit": "USD/GPU", "low": 10000, "high": 15000, "value": 12500, "basis": "midpoint of cited range", "source": "JARVIS_A100", "evidence": "extract: Hardware Purchase Pricing table, A100 80GB SXM new $10,000-$15,000, used/refurbished $5,000-$9,000 (March 2026)", "current": {"value": 15000, "where": "calculator.py GPUS['A100-80GB-SXM'].price_usd ('Rough assumption, no citation')"}}, + + {"id": "power.h100.tdp", "group": "power", "chip": "H100", "item": "H100 SXM max TDP", "unit": "W", "value": 700, "basis": "quoted", "source": "REPO_HARDWARE", "evidence": "verbatim (as quoted in the repo): Max Thermal Design Power (TDP) Up to 700W (configurable)", "current": {"value": 700, "where": "calculator.py GPUS['H100-SXM'].tdp_w; presets/hardware.py gpu.tdp"}}, + {"id": "power.h200.tdp", "group": "power", "chip": "H200", "item": "H200 SXM max TDP (NVL variant 600 W)", "unit": "W", "value": 700, "basis": "quoted", "source": "PNY_H200", "evidence": "verbatim: Max Thermal Design Power (TDP) Up to 700W (configurable) Up to 600W (configurable); BFLOAT16 Tensor Core 1,979 TFLOPS 1,671 TFLOPS with sparsity", "current": null}, + {"id": "power.b200.tdp", "group": "power", "chip": "B200", "item": "HGX B200 per-GPU max TDP", "unit": "W", "value": 1000, "basis": "quoted", "source": "NVIDIA_HGX_B200", "evidence": "verbatim: Per individual GPU: Configurable up to 1000 W", "current": null}, + {"id": "power.b200.dgx_system", "group": "power", "chip": "B200", "item": "DGX B200 system max power (8 GPUs)", "unit": "W", "value": 14300, "basis": "quoted", "source": "NVIDIA_DGX_B200_UG", "evidence": "verbatim: 14.3 kW max.; 8 x NVIDIA B200 GPUs; maximum power per GPU 1,000 W with 5 operating PSUs", "current": null}, + {"id": "power.gb200.gpu", "group": "power", "chip": "GB200", "item": "B200 GPU inside a GB200 superchip, max power", "unit": "W", "value": 1200, "basis": "quoted from secondary", "source": "KLOCKWOOD_B200", "evidence": "extract: the GB200 variant of B200 is rated at 1200 W maximum; the HGX variant is configured at 1,000 W (cites NVIDIA Blackwell Architecture Technical Brief)", "current": null}, + {"id": "power.gb200.rack", "group": "power", "chip": "GB200", "item": "GB200 NVL72 rack TDP, 72 GPUs", "unit": "W", "value": 132000, "n_gpus": 72, "basis": "quoted", "source": "HPE_GB200", "evidence": "verbatim: The rack's TDP (thermal design power) is 132 kW nominal; extract: electrical design power peak about 192 kW; Supermicro lists total power 132kW (8x 1U 33kW shelves)", "current": null}, + {"id": "power.dgx_h100.system", "group": "power", "chip": "H100", "item": "DGX H100 system max power (8 GPUs)", "unit": "W", "value": 10200, "basis": "quoted", "source": "REPO_HARDWARE", "evidence": "verbatim (as quoted in the repo): Table 3 lists 10.2 kW max for 200-240 V AC input", "current": {"value": 10200, "where": "presets/scenarios.py and scenarios_cited_2026.py thermal.dc.total_power (used directly as site power)"}}, + {"id": "power.h100.node_measured_avg", "group": "power", "chip": "H100", "item": "Measured average power of one 8-GPU H100 node during LLM training; median of four listed averages (7.79, 7.67, 7.73, 5.91 kW)", "unit": "W", "value": 7700, "samples_w": [7790, 7670, 7730, 5910], "peaks_w": [8420, 8450, 8730, 8760], "basis": "median of the four averages in Table 2", "source": "ARXIV_NODE_POWER", "evidence": "extract: Table 2 averages BNL Llama-13B 7.79 kW (76.4% of 10.2 kW), SMC GPT3-175B 64 nodes 7.67 kW, SMC Llama-70B 8 nodes 7.73 kW, SMC Llama-70B 64 nodes 5.91 kW; peaks 8.42-8.76 kW; 'never approached the manufacturer rated maximum of 10.2 kW'", "current": {"value": 5600, "where": "calculator.py pins node power = n_gpus x tdp_w x gpu_power_fraction (0.80) x (1 + server_power_fraction 0.25) = 700 W per GPU = 5,600 W per 8 GPUs"}}, + {"id": "power.h100.node_measured_peak", "group": "power", "chip": "H100", "item": "Maximum observed power of one 8-GPU H100 node in a single-node measurement", "unit": "W", "value": 8400, "basis": "quoted", "source": "LBNL_NODE_POWER", "evidence": "verbatim: The maximum observed power draw was approximately 8.4 kW, 18% lower than the manufacturer-rated 10.2 kW.; median for Llama2-13b training 7.92 kW", "current": null}, + + {"id": "flops.b200.bf16_sparse_hgx8", "group": "flops", "chip": "B200", "item": "HGX B200 FP16/BF16 Tensor Core, 8 GPUs, with sparsity", "unit": "FLOP/s", "value": 36e15, "n_gpus": 8, "basis": "quoted; dense is half (same convention as presets/hardware.py)", "source": "NVIDIA_HGX_B200", "evidence": "verbatim: FP16/BF16 Tensor Core*: 36 PFLOPS; * With sparcity (sic)", "current": null}, + {"id": "flops.gb200.bf16_dense", "group": "flops", "chip": "GB200", "item": "B200 inside GB200, dense BF16", "unit": "FLOP/s", "value": 2.5e15, "basis": "quoted from secondary", "source": "KLOCKWOOD_B200", "evidence": "extract: 2,500 TFLOPS BF16/FP16 dense at 1,200 W (matrix column); 2,250 TFLOPS at 1,000 W; Cyfuture lists 360 PFLOPS FP16/BF16 per NVL72 rack (5 PFLOPS per GPU, consistent with a sparse figure)", "current": null}, + {"id": "flops.h200.bf16_sparse", "group": "flops", "chip": "H200", "item": "H200 SXM BFLOAT16 Tensor Core with sparsity (same die as H100: dense is half)", "unit": "FLOP/s", "value": 1979e12, "basis": "quoted; dense is half", "source": "PNY_H200", "evidence": "verbatim: BFLOAT16 Tensor Core 1,979 TFLOPS 1,671 TFLOPS; 2. With sparsity.", "current": null}, + + {"id": "life.msft", "group": "life", "item": "Microsoft: servers and network equipment useful life", "unit": "years", "low": 2, "high": 6, "value": 6, "basis": "upper end of disclosed range; extended from 4 to 6 years effective FY2023", "source": "HUDSON_MSFT", "evidence": "extract: FY2024-FY2026 disclosures: 'servers and network equipment, two to six years'; useful life rose from four to six years effective FY2023", "evidence_kind": "secondary", "current": null}, + {"id": "life.goog", "group": "life", "item": "Alphabet: servers and certain network equipment useful life", "unit": "years", "value": 6, "basis": "quoted", "source": "ALPHABET_Q422", "evidence": "verbatim: resulting in a change in the estimated useful life of our servers and certain network equipment to six years", "evidence_kind": "primary", "current": null}, + {"id": "life.meta", "group": "life", "item": "Meta: most servers and network assets useful life", "unit": "years", "value": 5.5, "basis": "quoted", "source": "META_Q424", "evidence": "verbatim: an increase in their estimated useful life to 5.5 years, effective beginning fiscal year 2025", "evidence_kind": "primary", "current": null}, + {"id": "life.amzn", "group": "life", "item": "Amazon: servers and networking equipment useful life (a subset moved from six back to five years effective 2025-01-01)", "unit": "years", "low": 5, "high": 6, "value": 5, "basis": "lower end of range; AI-related subset is at five", "source": "EDGAR_AMZN", "evidence": "extract of the 10-K table: Servers and networking equipment: Five to six years (1); footnote: changed from five to six years effective 2024-01-01 and a subset back to five years effective 2025-01-01", "evidence_kind": "primary (third-party rendering)", "current": null}, + {"id": "life.orcl", "group": "life", "item": "Oracle: servers and networking equipment useful life", "unit": "years", "low": 5, "high": 6, "value": 5.5, "basis": "midpoint of range", "source": "GDCH_LIVES", "evidence": "extract: servers and networking equipment 5 to 6 years, effective start of Oracle's 2025 fiscal year; no filing cited", "evidence_kind": "secondary", "current": null}, + {"id": "life.crwv", "group": "life", "item": "CoreWeave: GPU infrastructure depreciation period", "unit": "years", "value": 6, "basis": "reported by secondary sources", "source": "BLOCKSPACE_CRWV", "evidence": "extract: six-year depreciation since 2023, citing CNBC 2025-11-14; theCUBE Research Fig. 2 also lists CoreWeave 6, Nebius 4, Lambda 5", "evidence_kind": "secondary", "current": null}, + {"id": "life.nbis", "group": "life", "item": "Nebius: depreciation period (as listed by theCUBE Research)", "unit": "years", "value": 4, "basis": "reported by secondary source", "source": "CUBE_LIVES", "evidence": "extract: Fig. 2 (2025 comparison): Nebius 4; Lambda Labs 5; CoreWeave 6; Amazon, Alphabet, Microsoft 6", "evidence_kind": "secondary", "current": null}, + {"id": "life.lambda", "group": "life", "item": "Lambda: depreciation period (as listed by theCUBE Research)", "unit": "years", "value": 5, "basis": "reported by secondary source", "source": "CUBE_LIVES", "evidence": "extract: Fig. 2 (2025 comparison): Lambda Labs 5", "evidence_kind": "secondary", "current": null}, + {"id": "life.bernstein_model", "group": "life", "item": "Bernstein model: hardware life assumed in a Vera Rubin cost model", "unit": "years", "value": 6, "basis": "reported by secondary source", "source": "REMIO_BERNSTEIN", "evidence": "extract: 'six-year hardware life' in Bernstein's earlier Vera Rubin model, about $7.9B annual depreciation per GW", "evidence_kind": "secondary", "current": null}, + {"id": "life.gpu_stack_default", "group": "life", "item": "gpu_stack calculator default useful life", "unit": "years", "value": 4, "basis": "repository default", "source": "REPO_HARDWARE", "evidence": "repository value", "current": {"value": 4, "where": "calculator.py INPUTS useful_life_years; presets/dgx_h100_tco.py econ.asset.useful_life = 126,230,400 s"}}, + {"id": "life.facility.heavy_equipment_amzn", "group": "life", "item": "Amazon: heavy equipment useful life (10 years before 2025-01-01)", "unit": "years", "low": 10, "high": 13, "value": 13, "basis": "upper end of range", "source": "EDGAR_AMZN", "evidence": "extract: Heavy equipment: Ten to thirteen years (2)", "evidence_kind": "primary (third-party rendering)", "current": null}, + {"id": "life.facility.buildings_amzn", "group": "life", "item": "Amazon: buildings useful life", "unit": "years", "value": 40, "basis": "upper bound (lesser of forty years or remaining life)", "source": "EDGAR_AMZN", "evidence": "extract: Buildings: Lesser of forty years or the remaining life of the underlying building", "evidence_kind": "primary (third-party rendering)", "current": null}, + + {"id": "resid.silicondata_h100", "group": "residual", "chip": "H100", "item": "Used H100 price as a share of contemporaneous new price, about 3 years old (2025)", "unit": "fraction", "low": 0.45, "high": 0.69, "value": 0.57, "basis": "midpoint; the source's table says about 0.69, its text says 0.45-0.55", "source": "SILICONDATA", "evidence": "extract: table 2025 age ~3 years USED ~69%; text 3-year USED 45-55%; the page's own table and text disagree", "current": {"value": 0.05, "where": "calculator.py INPUTS residual_fraction; presets/dgx_h100_tco.py econ.asset.residual_fraction 0.05"}}, + {"id": "resid.accounting", "group": "residual", "item": "Residual (salvage) value assumed in operators' depreciation", "unit": "fraction", "value": null, "basis": "not found: none of the sources reached states a residual value; the filing notes could not be read (see fetch_notes)", "source": "GDCH_LIVES", "evidence": "extract: 'Residual value assumptions: none of the companies' residual values are mentioned in the article'", "current": null}, + + {"id": "rent.h100.lambda_sxm", "group": "rental", "chip": "H100", "item": "Lambda on-demand H100 SXM", "unit": "USD/GPU-hour", "value": 3.99, "basis": "quoted", "source": "LAMBDA_PRICING", "evidence": "extract: NVIDIA H100 SXM (80 GB) $3.99 per GPU per hour on-demand", "current": null}, + {"id": "rent.h100.runpod_sxm", "group": "rental", "chip": "H100", "item": "RunPod on-demand H100 SXM", "unit": "USD/GPU-hour", "value": 3.29, "basis": "quoted", "source": "HOSTINGER_GPU", "evidence": "extract: RunPod $3.29 (SXM); Nebius $3.85; DigitalOcean $4.41; AWS $6.88 (p5.4xlarge, single H100); Azure ND H100 v5 $12.29 ($98.32 for 8 GPUs); GCP from $11.06 (A3, 8-GPU minimum); Vast.ai from $1.33; verified September 2026", "current": null}, + {"id": "rent.h100.aws_p5", "group": "rental", "chip": "H100", "item": "AWS on-demand single H100 (p5.4xlarge)", "unit": "USD/GPU-hour", "value": 6.88, "basis": "quoted", "source": "HOSTINGER_GPU", "evidence": "extract: AWS $6.88 p5.4xlarge single H100 SXM5", "current": null}, + {"id": "rent.h100.coreweave", "group": "rental", "chip": "H100", "item": "CoreWeave on-demand HGX H100 (8-GPU node $49.24/h)", "unit": "USD/GPU-hour", "value": 6.16, "basis": "quoted", "source": "SPHERON_CW", "evidence": "extract: H100 (HGX) 8 GPUs $49.24/h = $6.16 per GPU-hour; H200 $6.31; B200 $8.60; GB200 NVL72 $42.00 per 4-GPU instance = $10.50; CoreWeave publishes no reserved rate", "current": null}, + {"id": "rent.h100.spheron_1y_reserved", "group": "rental", "chip": "H100", "item": "One-year reserved H100 market rate, March 2026", "unit": "USD/GPU-hour", "value": 2.35, "basis": "quoted", "source": "SPHERON_RESERVE", "evidence": "extract: H100 one-year reserved rates rose roughly 40%, from about $1.70/hr (October 2025) to $2.35/hr (March 2026); Spheron on-demand H100 $2.64/hr as of 2026-10-26", "current": null}, + {"id": "rent.h100.spheron_ondemand", "group": "rental", "chip": "H100", "item": "Spheron on-demand H100", "unit": "USD/GPU-hour", "value": 2.64, "basis": "quoted", "source": "SPHERON_RESERVE", "evidence": "extract: Spheron's on-demand H100 is $2.64/hr with no contract, as of 26 Oct 2026", "current": null}, + {"id": "rent.discount.committed_1y", "group": "rental", "item": "Committed discount vs on-demand, one-year term (Compute Exchange via Spheron)", "unit": "fraction", "low": 0.40, "high": 0.50, "value": 0.45, "basis": "midpoint of range", "source": "SPHERON_RESERVE", "evidence": "extract: discount vs on-demand: 6 months 20-30%, 1 year 40-50%, 3 years 55-72%; not tied to a GPU model; 2-3 year terms typically need about 20% prepayment", "current": null}, + {"id": "rent.discount.committed_3y", "group": "rental", "item": "Committed discount vs on-demand, three-year term", "unit": "fraction", "low": 0.55, "high": 0.72, "value": 0.635, "basis": "midpoint of range", "source": "SPHERON_RESERVE", "evidence": "extract: 3 years 55-72%", "current": null}, + {"id": "rent.h200.runpod", "group": "rental", "chip": "H200", "item": "RunPod on-demand H200", "unit": "USD/GPU-hour", "value": 4.59, "basis": "quoted", "source": "HOSTINGER_GPU", "evidence": "extract: H200 RunPod $4.59; Vast.ai from $3.87; GCP and Azure require reservation", "current": null}, + {"id": "rent.h200.coreweave", "group": "rental", "chip": "H200", "item": "CoreWeave on-demand HGX H200", "unit": "USD/GPU-hour", "value": 6.31, "basis": "quoted", "source": "SPHERON_CW", "evidence": "extract: H200 (HGX) 8 GPUs $50.44/h = $6.31 per GPU-hour", "current": null}, + {"id": "rent.b200.lambda", "group": "rental", "chip": "B200", "item": "Lambda on-demand B200 SXM6", "unit": "USD/GPU-hour", "value": 6.69, "basis": "quoted", "source": "LAMBDA_PRICING", "evidence": "extract: NVIDIA B200 SXM6 (180 GB) $6.69 per GPU-hour on-demand; 1-Click Clusters HGX B200 (2 weeks to 1 year): 16 GPUs $9.86, 64 GPUs $9.36, 256+ GPUs $8.87; 'Contact us for reserved capacity at our lowest prices'", "current": null}, + {"id": "rent.b200.lambda_1click_256", "group": "rental", "chip": "B200", "item": "Lambda 1-Click Cluster HGX B200, 256+ GPUs, two weeks to one year", "unit": "USD/GPU-hour", "value": 8.87, "basis": "quoted", "source": "LAMBDA_PRICING", "evidence": "extract: 256+ GPUs $8.87 per GPU-hour", "current": null}, + {"id": "rent.b200.coreweave", "group": "rental", "chip": "B200", "item": "CoreWeave on-demand HGX B200", "unit": "USD/GPU-hour", "value": 8.60, "basis": "quoted", "source": "SPHERON_CW", "evidence": "extract: B200 (HGX) 8 GPUs $68.80/h = $8.60 per GPU-hour", "current": null}, + {"id": "rent.gb200.coreweave", "group": "rental", "chip": "GB200", "item": "CoreWeave on-demand GB200 NVL72 (4-GPU instance $42.00/h)", "unit": "USD/GPU-hour", "value": 10.50, "basis": "quoted", "source": "SPHERON_CW", "evidence": "extract: GB200 NVL72, 4 GPUs per instance, $42.00 node hour, $10.50 per GPU-hour", "current": null}, + + {"id": "pue.uptime_mean", "group": "pue", "item": "Uptime Institute Global Data Center Survey 2026: industry-wide per-facility average PUE", "unit": "x", "value": 1.52, "basis": "quoted", "source": "UPTIME", "evidence": "extract: industry-wide annual average per facility 1.52; capacity-weighted 1.36; new efficient facilities routinely 1.3 or lower", "current": {"value": 1.2, "where": "calculator.py INPUTS pue default 1.2 (labelled assumption)"}}, + {"id": "pue.uptime_weighted", "group": "pue", "item": "Uptime Institute survey 2026: capacity-weighted average PUE", "unit": "x", "value": 1.36, "basis": "quoted", "source": "UPTIME", "evidence": "extract: capacity-weighted average 1.36", "current": null}, + {"id": "pue.google", "group": "pue", "item": "Google fleet-wide trailing-twelve-month PUE, 2025 and 2026 Q1-Q2", "unit": "x", "value": 1.09, "basis": "quoted", "source": "GOOGLE_PUE", "evidence": "extract: comprehensive trailing twelve-month PUE of 1.09 across all large-scale data centers; 2019-2023 1.10; 2024-2026 1.09", "current": null}, + {"id": "pue.msft", "group": "pue", "item": "Microsoft fleet average PUE, FY25 (July 2024 to June 2025)", "unit": "x", "value": 1.17, "basis": "quoted", "source": "MSFT_PUE", "evidence": "extract: Fleet average (Global) 1.16 (FY24), 1.17 (FY25)", "current": null}, + {"id": "pue.aws", "group": "pue", "item": "AWS average global PUE, 2025", "unit": "x", "value": 1.14, "basis": "quoted", "source": "AWS_PUE", "evidence": "verbatim: In 2025, our data centers reported an average global PUE of 1.14.", "current": null}, + {"id": "pue.meta", "group": "pue", "item": "Meta PUE, 'low 1.1 range' (no year, no value stated)", "unit": "x", "value": 1.1, "basis": "approximate, from qualitative wording", "source": "DCMAG_META", "evidence": "extract: Meta is 'maintaining PUE in the low-1.1 range'", "current": null}, + + {"id": "fac.jll_per_mw_2026", "group": "facility", "item": "Global average data centre construction cost, 2026 forecast", "unit": "USD/MW", "value": 11300000, "premium_markets_gt": 15000000, "emerging_markets_lt": 8000000, "basis": "quoted forecast", "source": "JLL_COST", "evidence": "extract: JLL's forecast that global data centre construction costs will average $11.3 million per MW in 2026, up 6% on the prior year; Silicon Valley and Tokyo above $15M/MW; India and Southeast Asia below $8M/MW; liquid cooling adds $1-2M per MW; the article does not state whether MW is IT or total", "current": {"value": 2500000, "where": "presets/dgx_h100_tco.py: power_infra 1.50 USD/W + cooling_infra 1.00 USD/W (single-node pilot) plus shell 800 USD/m2 x 10 m2; calculator.py: building share of other_hardware_fraction (about 19% of GPU price, 0.40 total)"}}, + {"id": "fac.cw_2026_growth", "group": "facility", "item": "Cushman & Wakefield 2026 Data Center Development Cost Guide: change in per-MW construction cost since Q4 2024", "unit": "fraction", "value": 0.21, "basis": "quoted; dollar figures per market not reachable (paywalled)", "source": "CW_IREI", "evidence": "extract: construction costs have risen an average of 21 percent per megawatt since the previous edition (fourth quarter 2024)", "current": null}, + {"id": "fac.bernstein_shares", "group": "facility", "item": "Bernstein GB200-era split of about $35B per GW: GPUs 39%, networking 13%, CPUs 3%, storage 1-1.5%, mechanical and electrical about one third", "unit": "fraction of total", "gpus": 0.39, "networking": 0.13, "cpus": 0.03, "storage": 0.0125, "mechanical_electrical": 0.3333, "total_usd_per_gw": 35e9, "basis": "quoted shares", "source": "INVESTING_BERNSTEIN", "evidence": "extract: Bernstein about $35 billion per gigawatt; mechanical and electrical systems about one third; GPUs 39%; networking about 13%; CPUs about 3%; storage about 1-1.5%; no separate shell cost", "current": null}, + {"id": "fac.bernstein_vr", "group": "facility", "item": "Bernstein latest estimate per GW, Vera Rubin architecture", "unit": "USD/GW", "value": 39.5e9, "basis": "quoted", "source": "REMIO_BERNSTEIN", "evidence": "extract: an estimated $39.5 billion per gigawatt for Nvidia's Vera Rubin architecture (upper end of a $34.6-$39.5 billion range)", "current": null}, + {"id": "net.amcompute_h100_meta", "group": "network", "chip": "H100", "item": "Meta 24,576 x H100 cluster, May 2024 estimate: GPU boards, network (switches, transceivers, adapters), everything else, total", "unit": "USD", "gpu_boards": 599.0e6, "network": 185.2e6, "other": 86.0e6, "total": 910.4e6, "n_gpus": 24576, "basis": "quoted totals", "source": "AMCOMPUTE_CAPEX", "evidence": "extract: 24,576x H100 (Meta, May 2024 estimate): GPU boards $599.0M (65.8%), switches/transceivers/adapters $185.2M (20.3%), everything else $86.0M (9.4%), total $910.4M", "current": null}, + {"id": "net.amcompute_b200_576", "group": "network", "chip": "B200", "item": "576 x B200 cluster, 2026 pricing: GPU servers, networking, storage, everything else, total", "unit": "USD", "gpu_servers": 33.6e6, "network": 6.0e6, "storage": 1.8e6, "other": 2.9e6, "total": 44.3e6, "n_gpus": 576, "basis": "quoted totals", "source": "AMCOMPUTE_CAPEX", "evidence": "extract: 576x B200 (2026 pricing): GPU servers $33.6M (76%), networking $6.0M (14%), storage $1.8M (4%), everything else $2.9M (6%), total $44.3M; racks, PDUs and deployment priced separately in the B300 BOM", "current": null}, + + {"id": "elec.eia2025.us_industrial", "group": "electricity", "region": "US", "item": "U.S. total, industrial, 2025 average retail price", "unit": "USD/kWh", "value": 0.0851, "basis": "quoted (8.51 cents/kWh)", "source": "EIA_EPA_210", "evidence": "extract: Table 2.10 U.S. Total industrial 2025 8.51, 2024 8.13; commercial 2025 13.37, 2024 12.75", "current": {"value": 0.0813, "where": "calculator.py INPUTS electricity_price; presets/economics.py us_2024_industrial_flat_power_tariff"}}, + {"id": "elec.eia2025.ca_industrial", "group": "electricity", "region": "California", "item": "California industrial, 2025", "unit": "USD/kWh", "value": 0.2096, "basis": "quoted (20.96 cents/kWh)", "source": "EIA_EPA_210", "evidence": "extract: Table 2.10 California industrial 2025 20.96 (2024 21.53); commercial 2025 26.16 (2024 25.54)", "current": {"value": 0.2153, "where": "presets/economics.py california_2024_industrial_flat_power_tariff"}}, + {"id": "elec.eia2025.tx_industrial", "group": "electricity", "region": "Texas", "item": "Texas industrial, 2025", "unit": "USD/kWh", "value": 0.0635, "basis": "quoted (6.35 cents/kWh)", "source": "EIA_EPA_210", "evidence": "extract: Table 2.10 Texas industrial 2025 6.35 (2024 6.12); commercial 2025 8.56", "current": null}, + {"id": "elec.eia2025.va_industrial", "group": "electricity", "region": "Virginia", "item": "Virginia industrial, 2025", "unit": "USD/kWh", "value": 0.0949, "basis": "quoted (9.49 cents/kWh)", "source": "EIA_EPA_210", "evidence": "extract: Table 2.10 Virginia industrial 2025 9.49 (2024 8.99); commercial 2025 9.95", "current": null}, + {"id": "elec.eia2025.nm_industrial", "group": "electricity", "region": "New Mexico (lowest-priced state)", "item": "New Mexico industrial, 2025, the lowest state value in the table", "unit": "USD/kWh", "value": 0.0585, "basis": "quoted (5.85 cents/kWh)", "source": "EIA_EPA_210", "evidence": "extract: six lowest industrial 2025: New Mexico 5.85, Oklahoma 6.14, Louisiana 6.18, Texas 6.35, Tennessee 6.59, Kentucky 6.78; highest: Hawaii 31.45, Rhode Island 21.40, California 20.96, Alaska 19.86", "current": null}, + {"id": "elec.eia2025.wa_industrial", "group": "electricity", "region": "Washington", "item": "Washington industrial, 2025", "unit": "USD/kWh", "value": 0.0700, "basis": "quoted (7.00 cents/kWh)", "source": "EIA_EPA_210", "evidence": "extract: Table 2.10 Washington industrial 2025 7.00 (2024 6.61)", "current": null}, + {"id": "elec.de.bdew_industry", "group": "electricity", "region": "Germany", "item": "Germany industrial electricity price, small to medium industrial customers, 2026 year to date", "unit": "EUR/kWh", "value": 0.172, "basis": "quoted (17.2 ct/kWh); customer band not stated; 2025 value 17.6 ct is derived from a stated decrease of 0.4 ct", "source": "BDEW", "evidence": "extract: liegt im Jahr 2026 bisher bei 17,2 ct/kWh; the page gives no breakdown for industry; medium industry is 20-70 GWh per year, large 70-150 GWh per year", "current": null}, + {"id": "elec.pge_e20_transmission", "group": "electricity", "region": "California (PG&E)", "item": "PG&E E-20 Option R (transmission voltage) tariff: energy charges by period and demand charge", "unit": "USD/kWh and USD/kW-month", "energy_usd_per_kwh": [0.10058, 0.10121, 0.10354, 0.10897, 0.10897], "demand_usd_per_kw_month": 16.55, "basis": "quoted tariff entries; period-to-month map not shown; riders and non-bypassable charges not itemized", "source": "OPENEI_PGE", "evidence": "extract: effective 2026-03-27; energy rates $0.10058, 0.10121, 0.10354, 0.10897, 0.10897 per kWh in periods 1-5; seasonal/monthly demand charge $16.55 per kW, all months", "current": null}, + {"id": "elec.tx_4cp", "group": "electricity", "region": "Texas (ERCOT)", "item": "ERCOT four-coincident-peak transmission cost exposure for a continuously running load", "unit": "USD/kW-year", "value": 50, "basis": "estimate by LoD quoted by TheEnergyMag", "source": "ENERGYMAG_4CP", "evidence": "extract: roughly $50,000 per MW per year; about $5.70/MWh for a continuously running load, before site-specific tariffs, hedges or curtailment", "current": null}, + {"id": "elec.va_gs5", "group": "electricity", "region": "Virginia (Dominion)", "item": "Dominion GS-5 large-load class (25 MW and up, effective 2027-01-01): minimum billing demand", "unit": "fraction of contracted demand", "distribution_transmission_min": 0.85, "generation_min": 0.60, "basis": "quoted structure; no $/kW or c/kWh values in the sources reached", "source": "WAVY_GS5", "evidence": "extract: minimum demand charges at least 85% of contracted distribution and transmission demand and 60% of generation demand; the article gives no per-kWh or per-kW rates", "current": null}, + {"id": "elec.hq_data_centre", "group": "electricity", "region": "Quebec (Hydro-Quebec)", "item": "Proposed Hydro-Quebec tariff for data centres above 5 MW (not in force)", "unit": "CAD cents/kWh", "value": 13, "basis": "quoted proposal; 'roughly double the current high-power rate' (so about 6.5 CAD cents, inferred and not stated)", "source": "DCD_HQ", "evidence": "extract: 13 CAD cents per kWh for data centers above 5 MW, filed with the Regie de l'energie, would apply in H2 2026 if approved", "current": null}, + {"id": "elec.wholesale.caiso", "group": "electricity", "region": "California (CAISO NP15)", "item": "Mean day-ahead LMP over the e003 test period; used only to split a retail price into its wholesale-linked part", "unit": "USD/MWh", "value": null, "basis": "read from cost-v1.json at run time (pairs.caiso.mean_price_test)", "source": "REPO_COST_V1", "evidence": "repository result", "current": null}, + {"id": "elec.wholesale.de", "group": "electricity", "region": "Germany (DE-LU)", "item": "Mean day-ahead price over the e003 test period and the mean ECB USD per EUR rate used there", "unit": "EUR/MWh", "value": null, "basis": "read from cost-v1.json at run time (pairs.de.mean_price_test and pairs.de.usd_per_price_unit)", "source": "REPO_COST_V1", "evidence": "repository result", "current": null} + ] +} diff --git a/gpu_stack/calculator.py b/gpu_stack/calculator.py index e2fcb99..0007542 100644 --- a/gpu_stack/calculator.py +++ b/gpu_stack/calculator.py @@ -26,9 +26,13 @@ * ``gpu.peak_flops_power_limited`` = peak FLOP/s x MFU (speed actually achieved); * ``thermal.dc.total_power`` = PUE x GPUs x TDP x GPU load x (1 + server extra); -* ``econ.gpu.capex`` = GPU price x (1 + other hardware share); +* ``econ.gpu.capex`` = GPU price x (1 + servers-and-network share); * ``econ.asset.useful_life`` = years x 31,557,600 s; -* ``econ.job.capex_rate`` = GPUs x the graph's per-GPU amortized rate; +* ``econ.cluster.facility_capex`` (per GPU) = facility $/W x chip rating x (1 + server extra); +* ``econ.facility.useful_life`` = 13 years x 31,557,600 s (the building and its power and cooling + plant outlive the servers, so they are not charged on the server life); +* ``econ.job.capex_rate`` = GPUs x (the graph's per-GPU amortized rate for the servers + the graph's + per-GPU facility rate); * neutral closures: no recompute, no exposed communication, availability 1, the job owns the whole cluster, no staff, water, maintenance or network cost. @@ -51,11 +55,14 @@ * B200 is left out on purpose: NVIDIA's public pages list the 8-GPU system power (about 14.3 kW for DGX B200) but no per-GPU TDP, and the datasheet could not be read. Add it when a per-GPU figure can be cited. -* Electricity price default: U.S. EIA Electric Power Annual 2024, industrial - average 8.13 cents/kWh (the repo's economics preset). -* H100 price default: 2024 channel range $27,000-$40,000 per card, $30,000 - used (the repo's DGX H100 closure preset). The A100 price is a rough - assumption with no citation. +* Cost defaults come from ``gpu_stack.calculator_cost_basis`` (each with its URL + and access date): U.S. industrial electricity 8.51 cents/kWh (EIA Electric + Power Annual 2025, Table 2.10); H100 $37,500 and A100 $12,500 per card + (midpoints of 2026 new-card ranges); 5.5-year server life (median of eight + operators' reported lives); servers, network and storage at 49% of the GPU + price; extra server power set so the node matches measured H100 node power; + facility at $11.3 per W of provisioned power over 13 years. The audit that + collected them is ``experiments/e003-heatwave/cost_audit/``. """ from __future__ import annotations @@ -73,6 +80,7 @@ import gpu_stack # noqa: F401 (registers every variable and equation) from gpu_stack.core.registry import Registry from gpu_stack.core.resolver import resolve +from gpu_stack import calculator_cost_basis as basis from gpu_stack.presets.hardware import H100_SXM_BF16_DENSE_FLOPS # --------------------------------------------------------------------------- @@ -111,8 +119,11 @@ class GPU: peak_flops=H100_SXM_BF16_DENSE_FLOPS, tdp_w=700.0, memory_gb=80.0, - price_usd=30_000.0, - price_note="2024 channel range $27,000-$40,000 per card; $30,000 is the middle (repo DGX H100 preset).", + price_usd=basis.H100_PRICE_USD, + price_note=( + "2026 new-card range $35,000-$40,000 per card; $37,500 is the middle " + "(IntuitionLabs, Data Center GPU Pricing 2026, accessed 2026-10-26)." + ), cite=( "NVIDIA H100 product page, H100 SXM: BFLOAT16 Tensor Core 1,979 teraFLOPS with sparsity " "(dense is half, 989.4 teraFLOPS); Max TDP up to 700W; 80 GB memory." @@ -124,8 +135,11 @@ class GPU: peak_flops=312e12, tdp_w=400.0, memory_gb=80.0, - price_usd=15_000.0, - price_note="Rough assumption, no citation.", + price_usd=basis.A100_PRICE_USD, + price_note=( + "New-card range $10,000-$15,000 per card; $12,500 is the middle " + "(Jarvislabs, A100 price, March 2026 snapshot, accessed 2026-10-26)." + ), cite=( "NVIDIA A100 datasheet, 80GB SXM: BFLOAT16 Tensor Core 312 TFLOPS (624 with sparsity); " "Max TDP SXM 400W; 80 GB memory." @@ -196,29 +210,35 @@ class InputSpec: InputSpec("pue", "Datacenter overhead (PUE)", "x", 1.2, TAG_ASSUMPTION, "Power usage effectiveness: total building power divided by computer power. " "1.2 means cooling and other overhead add 20%.", advanced=True, lo=1.0, hi=3.0), - InputSpec("electricity_price", "Electricity price", "USD/kWh", 0.0813, TAG_ASSUMPTION, - "What one kilowatt-hour costs. Default is the 2024 U.S. industrial average (EIA).", + InputSpec("electricity_price", "Electricity price", "USD/kWh", basis.ELECTRICITY_USD_PER_KWH, TAG_ASSUMPTION, + "What one kilowatt-hour costs. Default is the 2025 U.S. industrial average (EIA), which " + "already includes the utility's delivery and demand charges.", advanced=True, lo=0.0, hi=2.0), InputSpec("gpu_price", "GPU purchase price", "USD", None, TAG_ASSUMPTION, "What one GPU costs to buy. Default depends on the GPU and is a rough market figure.", advanced=True, lo=0.0), - InputSpec("useful_life_years", "Years of use before replacement", "years", 4.0, TAG_ASSUMPTION, - "The hardware's price is spread over this many years. Longer life means cheaper hours.", + InputSpec("useful_life_years", "Years of use before replacement", "years", basis.USEFUL_LIFE_YEARS, TAG_ASSUMPTION, + "The servers' price is spread over this many years. Default 5.5 is the median of the lives " + "eight operators report for servers (4 to 6 years). The building is spread over 13 years " + "separately. Longer life means cheaper hours.", advanced=True, lo=0.5, hi=15.0), InputSpec("residual_fraction", "Resale value at end of life", "fraction of price", 0.05, TAG_ASSUMPTION, "The share of the purchase price you get back when the GPU is retired.", advanced=True, lo=0.0, hi=0.9), - InputSpec("other_hardware_fraction", "Servers, network and building, as a share of GPU price", "fraction", - 0.40, TAG_ASSUMPTION, - "Each GPU needs a server, network and space. Default 40% follows the repo's DGX H100 cost " - "preset: server parts about 18% of the GPU price, rack about 5%, building power and cooling " - "about 19%.", advanced=True, lo=0.0, hi=3.0), + InputSpec("other_hardware_fraction", "Servers, network and storage, as a share of GPU price", "fraction", + basis.OTHER_HARDWARE_FRACTION, TAG_ASSUMPTION, + "Each GPU needs a server, network and storage. Default 49%: the non-GPU part of an H100 server " + "(about $7,400 per GPU), plus network ($7,500 per GPU) and other cluster hardware ($3,500 per " + "GPU) for a 24,576-GPU H100 cluster, over a $37,500 card. The building, power and cooling " + "are charged separately.", advanced=True, lo=0.0, hi=3.0), InputSpec("gpu_power_fraction", "GPU power draw during training", "fraction of rating", 0.80, TAG_ASSUMPTION, "Training rarely pulls the full rated power. 80% is a guess; measured values are often 60-90%.", advanced=True, lo=0.2, hi=1.0), InputSpec("server_power_fraction", "Extra server power (CPUs, fans, network)", "fraction of GPU power", - 0.25, TAG_ASSUMPTION, - "Power drawn by the rest of the server, as a share of the GPUs' own power.", + basis.SERVER_POWER_FRACTION, TAG_ASSUMPTION, + "Power drawn by the rest of the server, as a share of the GPUs' own power. Default 72% makes " + "an 8-GPU H100 node draw 7.7 kW, the measured average during LLM training (the node is rated " + "10.2 kW).", advanced=True, lo=0.0, hi=2.0), ] } @@ -275,7 +295,9 @@ class InputSpec: "econ.job.dc_power": ("Power the datacenter draws for this run", "site power x share of cluster used", "W"), "econ.run.power_cost": ("Electricity cost", "power x run time x price per watt-second", "USD"), "econ.gpu.hourly_amortized": ( - "Hardware cost per GPU per second", "(price - resale value) / years of use", "USD/s"), + "GPU, server and network cost per GPU per second", "(price - resale value) / years of use", "USD/s"), + "econ.cluster.facility_capex_rate": ( + "Building, power and cooling cost per GPU per second", "facility cost per GPU / facility life", "USD/s"), "econ.run.hw_cost": ("Hardware cost for this run", "hardware cost per second x run time", "USD"), "econ.run.total_cost": ("Total cost", "hardware + running costs (none) + electricity", "USD"), "econ.cost.per_token": ("Cost per token", "total cost / tokens", "USD/token"), @@ -289,9 +311,13 @@ class InputSpec: "PUE x GPUs x chip power rating x GPU power draw x (1 + extra server power)", "W"), "econ.gpu.capex": ("Cost to buy one GPU with its share of servers and network", "GPU price x (1 + servers, network and building share)", "USD"), - "econ.asset.useful_life": ("Hardware life in seconds", "years of use x 31,557,600", "s"), - "econ.job.capex_rate": ("Hardware cost per second for all GPUs", "number of GPUs x hardware cost per GPU per second", - "USD/s"), + "econ.asset.useful_life": ("Server life in seconds", "years of use x 31,557,600", "s"), + "econ.cluster.facility_capex": ( + "Building, power and cooling cost per GPU", "facility USD per W x chip power rating x (1 + extra server power)", + "USD"), + "econ.facility.useful_life": ("Building life in seconds", "13 years x 31,557,600", "s"), + "econ.job.capex_rate": ("Hardware cost per second for all GPUs", + "number of GPUs x (server cost + building cost) per GPU per second", "USD/s"), } OUTPUTS: Dict[str, Dict[str, str]] = { @@ -364,12 +390,27 @@ def build_model() -> CalculatorModel: if amort.missing: raise RuntimeError(f"amortization did not resolve: {sorted(amort.missing)}") + # Per-GPU slice of the facility (building, power and cooling plant): the + # graph divides facility capex by the facility's own, longer life. The + # facility is sized for the power the GPU and its server can draw. + facility_capex_per_gpu = basis.FACILITY_USD_PER_W * S["tdp_w"] * (1 + S["server_power_fraction"]) + facility_life = basis.FACILITY_USEFUL_LIFE_YEARS * ssum + facility = resolve( + "econ.cluster.facility_capex_rate", + assignments={ + "econ.cluster.facility_capex": facility_capex_per_gpu, + "econ.facility.useful_life": facility_life, + }, + ) + if facility.missing: + raise RuntimeError(f"facility amortization did not resolve: {sorted(facility.missing)}") + pins: Dict[str, sp.Expr] = { "gpu.peak_flops_power_limited": S["peak_flops"] * S["mfu"], "thermal.dc.total_power": ( S["pue"] * S["n_gpus"] * S["tdp_w"] * S["gpu_power_fraction"] * (1 + S["server_power_fraction"]) ), - "econ.job.capex_rate": S["n_gpus"] * amort.value, + "econ.job.capex_rate": S["n_gpus"] * (amort.value + facility.value), } assignments: Dict[str, Any] = { "arch.params_total_dense": S["params"], @@ -443,6 +484,16 @@ def pin_steps(steps: List[_Step]) -> List[_Step]: capex_step = _Step( "econ.gpu.capex", *PIN_PLAIN["econ.gpu.capex"][:2], PIN_PLAIN["econ.gpu.capex"][2], "setting", None, S["gpu_price"] * (1 + S["other_hardware_fraction"])) + fac_capex_step = _Step( + "econ.cluster.facility_capex", *PIN_PLAIN["econ.cluster.facility_capex"][:2], + PIN_PLAIN["econ.cluster.facility_capex"][2], "setting", None, facility_capex_per_gpu) + fac_life_step = _Step( + "econ.facility.useful_life", *PIN_PLAIN["econ.facility.useful_life"][:2], + PIN_PLAIN["econ.facility.useful_life"][2], "setting", None, sp.sympify(facility_life)) + fac_step = _Step( + "econ.cluster.facility_capex_rate", *PLAIN_STEPS["econ.cluster.facility_capex_rate"][:2], + PLAIN_STEPS["econ.cluster.facility_capex_rate"][2], "graph equation", "econ.eq.cluster_facility_capex_rate", + facility.value) life_step = _Step( "econ.asset.useful_life", *PIN_PLAIN["econ.asset.useful_life"][:2], PIN_PLAIN["econ.asset.useful_life"][2], "setting", None, S["useful_life_years"] * ssum) @@ -452,7 +503,8 @@ def chain_for(res, extra_before: Optional[List[_Step]] = None, extra_after: Opti pre = pin_steps(g) # hardware chains also need the amortization sub-chain if any(s.var in ("econ.run.hw_cost", "econ.run.total_cost", "econ.cost.per_token") for s in g): - pre = [capex_step, life_step, amort_step] + [p for p in pre if p.var != "econ.job.capex_rate"] + pre = [capex_step, life_step, amort_step, fac_capex_step, fac_life_step, fac_step] + [ + p for p in pre if p.var != "econ.job.capex_rate"] pre.append(_Step("econ.job.capex_rate", *PIN_PLAIN["econ.job.capex_rate"][:2], PIN_PLAIN["econ.job.capex_rate"][2], "setting", None, pins["econ.job.capex_rate"])) steps = (extra_before or []) + pre + g + (extra_after or []) diff --git a/gpu_stack/calculator_cost_basis.py b/gpu_stack/calculator_cost_basis.py new file mode 100644 index 0000000..3a5185c --- /dev/null +++ b/gpu_stack/calculator_cost_basis.py @@ -0,0 +1,168 @@ +""" +gpu_stack.calculator_cost_basis +=============================== + +The cited cost inputs behind the calculator's defaults (``gpu_stack.calculator``). + +Each constant is either a number read from a source (URL and access date next +to it) or an arithmetic combination of such numbers, written out so the +derivation can be re-run. The audit that collected these sources, with every +row's range, evidence and the value the calculator used before this change, is +``experiments/e003-heatwave/cost_audit/sources.json``; the test +``tests/test_calculator_cost_basis.py`` checks that this module and that table +agree. + +Nothing here is a measurement of one specific cluster. Where a source gives a +range, the default is the midpoint and the range is kept in this module so the +sensitivity script can sweep it. + +All sources were accessed on 2026-10-26. +""" + +from __future__ import annotations + +import statistics +from typing import Dict, Tuple + +ACCESSED = "2026-10-26" + +# --------------------------------------------------------------------------- +# GPU purchase prices (USD per GPU, new, SXM) +# --------------------------------------------------------------------------- + +# IntuitionLabs, "Data Center GPU Pricing 2026" (published 2026-07-20, updated +# 2026-10-11), Table 2: H100 SXM5 80GB per GPU new, $35,000 to $40,000 (it +# cites CloudZero). https://intuitionlabs.ai/articles/data-center-gpu-pricing-2026 +H100_PRICE_RANGE_USD: Tuple[float, float] = (35_000.0, 40_000.0) +H100_PRICE_USD: float = sum(H100_PRICE_RANGE_USD) / 2.0 + +# Jarvislabs, "A100 price" (March 2026 snapshot, updated 2026-10): A100 80GB +# SXM new, $10,000 to $15,000. https://jarvislabs.ai/blog/a100-price +A100_PRICE_RANGE_USD: Tuple[float, float] = (10_000.0, 15_000.0) +A100_PRICE_USD: float = sum(A100_PRICE_RANGE_USD) / 2.0 + +# --------------------------------------------------------------------------- +# Electricity +# --------------------------------------------------------------------------- + +# U.S. EIA, Electric Power Annual 2025, Table 2.10 (average price by state and +# sector): U.S. industrial 8.51 cents/kWh. This is revenue divided by sales, +# so it already contains the utilities' energy, delivery and demand charges. +# https://www.eia.gov/electricity/annual/html/epa_02_10.html +ELECTRICITY_USD_PER_KWH: float = 0.0851 + +# --------------------------------------------------------------------------- +# Useful life of GPU servers (years), as the operators report it +# --------------------------------------------------------------------------- + +# (operator, years, source). Where a filing gives a range the upper end is +# used, because that is the life applied to newly bought AI servers. +OPERATOR_SERVER_LIVES: Tuple[Tuple[str, float, str], ...] = ( + ("Microsoft", 6.0, "https://www.hudson-labs.com/research/msft-server-useful-life"), + ("Alphabet", 6.0, "https://abc.xyz/investor/static/pdf/2022Q4_alphabet_earnings_release.pdf"), + ("Meta", 5.5, "https://investor.atmeta.com/investor-news/press-release-details/2025/" + "Meta-Reports-Fourth-Quarter-and-Full-Year-2024-Results/default.aspx"), + ("Amazon", 5.0, "https://app.edgar.tools/companies/AMZN/disclosures/property-plant-equipment"), + ("Oracle", 5.5, "https://www.globaldatacenterhub.com/p/how-long-does-an-ai-chip-last-and"), + ("CoreWeave", 6.0, "https://blockspace.media/insight/coreweave-a100-contract-challenges-gpu-depreciation-model/"), + ("Nebius", 4.0, "https://thecuberesearch.com/298-breaking-analysis-resetting-gpu-depreciation-why-ai-factories-" + "bend-but-dont-break-useful-life-assumptions/"), + ("Lambda", 5.0, "https://thecuberesearch.com/298-breaking-analysis-resetting-gpu-depreciation-why-ai-factories-" + "bend-but-dont-break-useful-life-assumptions/"), +) +USEFUL_LIFE_RANGE_YEARS: Tuple[float, float] = ( + min(y for _, y, _ in OPERATOR_SERVER_LIVES), + max(y for _, y, _ in OPERATOR_SERVER_LIVES), +) +USEFUL_LIFE_YEARS: float = float(statistics.median(y for _, y, _ in OPERATOR_SERVER_LIVES)) + +# Amazon FY2025 10-K, property and equipment: heavy equipment ten to thirteen +# years, buildings up to forty years. The facility slice uses the upper end of +# heavy equipment (power and cooling plant dominate the facility cost). +# https://app.edgar.tools/companies/AMZN/disclosures/property-plant-equipment +FACILITY_USEFUL_LIFE_YEARS: float = 13.0 + +# --------------------------------------------------------------------------- +# Everything bought with a GPU except the GPU card (as a share of its price) +# --------------------------------------------------------------------------- + +# Server without its GPUs, per GPU. NVIDIA DGX H100 8-GPU system $300,000 to +# $460,000 (midpoint $380,000; DeployBase via IntuitionLabs, Q1-Q2 2026) and an +# HGX H100 8-GPU server about $339,000 (AMCompute GPU server values, August +# 2026 data, "estimated from GPU list price; not a quoted server price"). The +# non-GPU part is the system price minus eight GPUs; the default is the middle +# of the HGX and DGX values. +# https://intuitionlabs.ai/articles/data-center-gpu-pricing-2026 +# https://www.amcompute.com/gpu-server-values +DGX_H100_SYSTEM_USD: float = 380_000.0 +HGX_H100_SYSTEM_USD: float = 339_000.0 +GPUS_PER_SYSTEM: int = 8 +SERVER_NON_GPU_PER_GPU_USD: float = ( + ((HGX_H100_SYSTEM_USD - GPUS_PER_SYSTEM * H100_PRICE_USD) / GPUS_PER_SYSTEM) + + ((DGX_H100_SYSTEM_USD - GPUS_PER_SYSTEM * H100_PRICE_USD) / GPUS_PER_SYSTEM) +) / 2.0 + +# Network and everything else for a 24,576 x H100 cluster (Meta, May 2024 +# estimate): switches, transceivers and adapters $185.2M; everything else +# $86.0M. https://www.amcompute.com/blog/ai-cluster-cost-breakdown-capex +META_CLUSTER_GPUS: int = 24_576 +NETWORK_PER_GPU_USD: float = 185.2e6 / META_CLUSTER_GPUS +OTHER_IT_PER_GPU_USD: float = 86.0e6 / META_CLUSTER_GPUS + +OTHER_HARDWARE_FRACTION_EXACT: float = ( + SERVER_NON_GPU_PER_GPU_USD + NETWORK_PER_GPU_USD + OTHER_IT_PER_GPU_USD +) / H100_PRICE_USD +OTHER_HARDWARE_FRACTION: float = round(OTHER_HARDWARE_FRACTION_EXACT, 2) + +# --------------------------------------------------------------------------- +# Facility (building shell, electrical and mechanical plant) +# --------------------------------------------------------------------------- + +# JLL forecast: global average data centre construction cost $11.3 million per +# MW in 2026 (liquid cooling adds $1-2 million per MW; the article does not say +# whether the MW is IT or total, and this module reads it as IT load). +# https://www.datacentres.com/news/data-centre-construction-costs-11m-per-mw +# Bernstein puts mechanical and electrical at about one third of $35 billion +# per GW, about $11.7 million per MW, which agrees. +# https://www.investing.com/news/stock-market-news/how-much-does-a-gw-of-data-center-capacity-actually-cost-4314046 +FACILITY_USD_PER_W: float = 11.3 + +# --------------------------------------------------------------------------- +# Power at the server +# --------------------------------------------------------------------------- + +# Measured average power of one 8-GPU H100 node during LLM training: median of +# four listed averages (7.79, 7.67, 7.73 and 5.91 kW), against the 10.2 kW +# rating. https://arxiv.org/pdf/2506.14551 (Table 2) +H100_NODE_MEASURED_AVG_W: float = statistics.median([7_790.0, 7_670.0, 7_730.0, 5_910.0]) +H100_TDP_W: float = 700.0 +# The calculator's GPU load (an uncited assumption, kept at its earlier value). +GPU_POWER_FRACTION: float = 0.80 +# Extra server power as a share of the GPUs' own power, set so that the +# calculator's node power equals the measured node average: +# 8 x 700 W x 0.80 x (1 + s) = measured average. +SERVER_POWER_FRACTION_EXACT: float = ( + H100_NODE_MEASURED_AVG_W / (GPUS_PER_SYSTEM * H100_TDP_W * GPU_POWER_FRACTION) - 1.0 +) +SERVER_POWER_FRACTION: float = round(SERVER_POWER_FRACTION_EXACT, 2) + +# Before this change (for the before/after script): see CORRECTION.md. +LEGACY_DEFAULTS: Dict[str, float] = { + "electricity_price": 0.0813, + "useful_life_years": 4.0, + "other_hardware_fraction": 0.40, + "server_power_fraction": 0.25, + "h100_price": 30_000.0, + "a100_price": 15_000.0, +} + +__all__ = [ + "A100_PRICE_RANGE_USD", "A100_PRICE_USD", "ACCESSED", "DGX_H100_SYSTEM_USD", + "ELECTRICITY_USD_PER_KWH", "FACILITY_USD_PER_W", "FACILITY_USEFUL_LIFE_YEARS", + "GPUS_PER_SYSTEM", "GPU_POWER_FRACTION", "H100_NODE_MEASURED_AVG_W", + "H100_PRICE_RANGE_USD", "H100_PRICE_USD", "H100_TDP_W", "HGX_H100_SYSTEM_USD", + "LEGACY_DEFAULTS", "META_CLUSTER_GPUS", "NETWORK_PER_GPU_USD", "OPERATOR_SERVER_LIVES", + "OTHER_HARDWARE_FRACTION", "OTHER_HARDWARE_FRACTION_EXACT", "OTHER_IT_PER_GPU_USD", + "SERVER_NON_GPU_PER_GPU_USD", "SERVER_POWER_FRACTION", "SERVER_POWER_FRACTION_EXACT", + "USEFUL_LIFE_RANGE_YEARS", "USEFUL_LIFE_YEARS", +] diff --git a/gpu_stack/presets/__init__.py b/gpu_stack/presets/__init__.py index 8585d61..a91dece 100644 --- a/gpu_stack/presets/__init__.py +++ b/gpu_stack/presets/__init__.py @@ -25,8 +25,9 @@ dense / MoE variant selectors to "dense" for every variable tagged as a dense-vs-MoE VARIANT family. * `workload.moe_variant_selector`: the MoE counterpart. -* `economics.POWER_PRICE_PRESETS`: EIA 2024 historical average flat power - tariffs for U.S. and California commercial/industrial electricity prices. +* `economics.POWER_PRICE_PRESETS`: EIA 2024 and 2025 historical average flat + power tariffs for U.S. and California commercial/industrial electricity + prices. * `lithography.euv_exposure` and `lithography.arf_immersion_exposure`: exposure optics (wavelength, numerical aperture, medium index) for EUV at 13.5 nm and ArF immersion at 193 nm, from ASML product specifications. diff --git a/gpu_stack/presets/dgx_h100_tco.py b/gpu_stack/presets/dgx_h100_tco.py index ab669f7..753ecc4 100644 --- a/gpu_stack/presets/dgx_h100_tco.py +++ b/gpu_stack/presets/dgx_h100_tco.py @@ -24,6 +24,17 @@ inputs, maintenance, staff, network transit, demand charge, water, carbon intensity and price, and cooling-tower thermal parameters. Each assumption is labeled with its rationale and bound. + +Three corrections apply to this closure (ROADMAP item 4, cost audit T10): + + * Site power includes facility overhead. dgx_h100_site_power_with_pue sets + thermal.dc.total_power to the DGX H100 system maximum (10.2 kW) times an + assumed PUE, not the system maximum alone. + * The demand charge is not added on top of an EIA average price. The EIA + average retail price is revenue divided by sales and already contains + the demand charges, so econ.power.capacity_charge_kw_month is zero. + * Facility capex has its own, longer life (econ.facility.useful_life) + instead of the 4-year IT life. """ from __future__ import annotations @@ -167,6 +178,28 @@ def _root_assignments(assignments: dict[str, float]) -> dict[str, float]: "typical 3-5 for chemical inhibitor programs (DOE, same source)." ) +# PUE applied to the DGX H100 system maximum to get site power. 1.2 is the +# calculator default and sits between operator-reported fleet values and the +# industry survey mean (accessed 2026-10-26): Google 1.09 trailing twelve +# months (https://www.datacenters.google/efficiency), AWS 1.14 for 2025 +# (https://aws.amazon.com/sustainability/), Microsoft 1.17 for FY25 +# (https://datacenters.microsoft.com/sustainability/efficiency/), Uptime +# Institute Global Data Center Survey 2026 capacity-weighted 1.36 and +# per-facility mean 1.52 +# (https://intelligence.uptimeinstitute.com/resource/growing-pue-advantage-larger-data-centers). +ASSUMED_PUE = 1.2 +DGX_H100_SYSTEM_MAX_POWER_W = 10_200.0 + +# Facility life: Amazon's FY2025 10-K lists heavy equipment at ten to thirteen +# years and buildings at up to forty years +# (https://app.edgar.tools/companies/AMZN/disclosures/property-plant-equipment, +# accessed 2026-10-26). The upper end of the heavy-equipment range is used for +# the whole facility: power and cooling plant dominate facility capex, and the +# building shell lasts longer, so 13 years is the shortest cited facility life. +FACILITY_USEFUL_LIFE_YEARS = 13.0 +SECONDS_PER_JULIAN_YEAR = 365.25 * 86400.0 + + pythia_70m_dgx_h100_run_closure_assumption = Preset( name="pythia_70m_dgx_h100_run_closure_assumption", description=( @@ -291,6 +324,13 @@ def _root_assignments(assignments: dict[str, float]) -> dict[str, float]: # a PUE ~ 1.0 colocation scenario. Using same 15,000 W. "thermal.facility.cooling_design_capacity": 15_000.0, # ----------------------------------------------------------------------- + # Facility life + # ----------------------------------------------------------------------- + # 13 years for building shell, power and cooling plant (see the + # FACILITY_USEFUL_LIFE_YEARS comment); IT equipment keeps the 4-year + # life above. + "econ.facility.useful_life": FACILITY_USEFUL_LIFE_YEARS * SECONDS_PER_JULIAN_YEAR, + # ----------------------------------------------------------------------- # Maintenance # ----------------------------------------------------------------------- # Annual maintenance fraction: 2 % of total capex per year. @@ -318,10 +358,12 @@ def _root_assignments(assignments: dict[str, float]) -> dict[str, float]: # ----------------------------------------------------------------------- # Power demand charge # ----------------------------------------------------------------------- - # Capacity charge per kW-month: $8.00/(kW*month) is a common US - # industrial demand-charge rate. Demand charges vary widely; - # $5-$15/kW-month is a representative US industrial range. - "econ.power.capacity_charge_kw_month": 8.00, + # Zero: the energy price in this pack is the EIA average retail price, + # which is utility revenue divided by sales (EIA FAQ) and therefore + # already contains the demand charges. Adding $8.00/(kW*month) on top + # counted the same charge twice. A scenario that uses an energy-only + # price should assign the tariff's demand charge here instead. + "econ.power.capacity_charge_kw_month": 0.0, # ----------------------------------------------------------------------- # Water cost # ----------------------------------------------------------------------- @@ -445,8 +487,13 @@ def _root_assignments(assignments: dict[str, float]) -> dict[str, float]: "econ.network.transit_price_per_gb=0.08 USD/GB and " "econ.network.egress_bytes_per_s=1e6 byte/s give a minimal " "transit cost boundary for a single-node on-premises system.", - "econ.power.capacity_charge_kw_month=8.00 USD/(kW*month) is " - "a representative US industrial demand charge; actual rates vary.", + "econ.power.capacity_charge_kw_month=0.0 because the EIA average " + "retail price already contains demand charges; the earlier 8.00 " + "USD/(kW*month) counted them twice (ROADMAP item 4). Assign a " + "tariff's demand charge here only together with an energy-only price.", + "econ.facility.useful_life=410248800 s is 13 years, the upper end of " + "Amazon's FY2025 heavy-equipment life; the facility was on the " + "4-year IT life before (ROADMAP item 4).", "econ.water.price_per_liter=0.005 USD/L ($5/m^3) represents " "US industrial water cost including treatment and discharge.", "This pack is an explicit assumption closure, not measured " @@ -455,7 +502,43 @@ def _root_assignments(assignments: dict[str, float]) -> dict[str, float]: ) +dgx_h100_site_power_with_pue = Preset( + name="dgx_h100_site_power_with_pue", + description=( + "Site power for a DGX H100 node including facility overhead: the 10.2 kW " + "system maximum times an assumed PUE of 1.2, in place of the system " + "maximum alone." + ), + assignments={ + "thermal.dc.total_power": ASSUMED_PUE * DGX_H100_SYSTEM_MAX_POWER_W, + }, + source=( + "DGX H100 system maximum 10.2 kW: NVIDIA DGX H100/H200 User Guide, " + "Table 3, https://docs.nvidia.com/dgx/dgxh100-user-guide/" + "introduction-to-dgxh100.html (as cited in presets/scenarios.py). " + "PUE 1.2 is an assumption between operator-reported fleet values and " + "the industry survey mean (accessed 2026-10-26): Google 1.09 trailing " + "twelve months (https://www.datacenters.google/efficiency), AWS 1.14 " + "for 2025 (https://aws.amazon.com/sustainability/), Microsoft 1.17 for " + "FY25 (https://datacenters.microsoft.com/sustainability/efficiency/), " + "Uptime Institute Global Data Center Survey 2026 capacity-weighted " + "1.36 and per-facility mean 1.52 " + "(https://intelligence.uptimeinstitute.com/resource/" + "growing-pue-advantage-larger-data-centers)." + ), + notes=( + "thermal.dc.total_power=12240 W is PUE 1.2 times 10200 W. The run " + "closure had used 10200 W alone, so facility overhead never reached " + "the electricity bill (ROADMAP item 4).", + "IT power stays the DGX H100 system maximum, a rating; measured " + "training draw is lower (an average of about 7.7 kW per node in " + "arXiv 2506.14551 Table 2), so this is an upper-end IT load.", + ), +) + + __all__ = [ "dgx_h100_node_power_bom", + "dgx_h100_site_power_with_pue", "pythia_70m_dgx_h100_run_closure_assumption", ] diff --git a/gpu_stack/presets/economics.py b/gpu_stack/presets/economics.py index 52b41e2..3daa2f6 100644 --- a/gpu_stack/presets/economics.py +++ b/gpu_stack/presets/economics.py @@ -10,6 +10,11 @@ time-of-use tariffs. Demand-charge roots stay unassigned on purpose — average retail prices already blend those charges in, and assigning them too would double count. + +The 2024 presets are kept for the scenario packs that name them. The 2025 +presets use the final 2025 annual averages from EIA Electric Power Annual +Table 2.10 (read 2026-10-26), which the calculator now takes as its +default electricity price. """ from __future__ import annotations @@ -34,12 +39,37 @@ "https://www.eia.gov/electricity/annual/secrev-map.php" ) +_EIA_2025_TABLE_2_10_SOURCE = ( + "U.S. Energy Information Administration (EIA), Electric Power Annual, " + "Table 2.10, 'Average Price of Electricity to Ultimate Customers by " + "End-Use Sector, by State, 2025 and 2024 (Cents per Kilowatthour)', " + "2025 column: U.S. Total Commercial 13.37 and Industrial 8.51 " + "cents/kWh; California Commercial 26.16 and Industrial 20.96 cents/kWh. " + "URL: https://www.eia.gov/electricity/annual/html/epa_02_10.html " + "(accessed 2026-10-26)." +) + _EIA_PRICE_LIMITATION = ( "EIA FAQ: published average retail electricity prices are derived as " "utility revenue divided by retail sales and are not utility rates or " "itemized tariff schedules." ) +_FLAT_TARIFF_NOTES_2025 = ( + "Annual-average flat-tariff closure: the same sourced EIA average price " + "is assigned to peak and off-peak price roots.", + "The peak-energy fraction is set to 0.0 only as a resolver-neutral " + "closure under equal peak/off-peak prices; it is not a measured " + "time-of-use load split.", + "Demand-charge roots are not assigned. EIA average retail prices are " + "delivered average prices (revenue divided by sales), so demand charges, " + "transmission and distribution charges and riders are already in them. " + "For a load that runs flat out the demand charge per kWh is lower than " + "the class average; for an energy-only price assign the demand charge " + "separately.", + "Final 2025 annual averages; not a live 2026 value.", +) + _FLAT_TARIFF_NOTES = ( "Historical annual-average flat-tariff closure: the same sourced EIA " "average price is assigned to peak and off-peak price roots.", @@ -80,6 +110,7 @@ def _flat_power_tariff( description: str, usd_per_kwh: float, source: str, + notes: tuple[str, ...] | None = None, ) -> Preset: return Preset( name=name, @@ -92,7 +123,7 @@ def _flat_power_tariff( } ), source=source, - notes=_FLAT_TARIFF_NOTES, + notes=_FLAT_TARIFF_NOTES if notes is None else notes, ) @@ -152,11 +183,75 @@ def _flat_power_tariff( ) +us_2025_commercial_flat_power_tariff = _flat_power_tariff( + name="us_2025_commercial_flat_power_tariff", + description=( + "U.S. 2025 Total Electric Industry commercial average retail " + "electricity price, represented as a flat tariff." + ), + usd_per_kwh=0.1337, + source=( + f"{_EIA_2025_TABLE_2_10_SOURCE} Converts 13.37 cents/kWh to " + f"0.1337 USD/kWh. {_EIA_PRICE_LIMITATION}" + ), + notes=_FLAT_TARIFF_NOTES_2025, +) + + +us_2025_industrial_flat_power_tariff = _flat_power_tariff( + name="us_2025_industrial_flat_power_tariff", + description=( + "U.S. 2025 Total Electric Industry industrial average retail " + "electricity price, represented as a flat tariff." + ), + usd_per_kwh=0.0851, + source=( + f"{_EIA_2025_TABLE_2_10_SOURCE} Converts 8.51 cents/kWh to " + f"0.0851 USD/kWh. {_EIA_PRICE_LIMITATION}" + ), + notes=_FLAT_TARIFF_NOTES_2025, +) + + +california_2025_commercial_flat_power_tariff = _flat_power_tariff( + name="california_2025_commercial_flat_power_tariff", + description=( + "California 2025 commercial average retail electricity price, " + "represented as a flat tariff." + ), + usd_per_kwh=0.2616, + source=( + f"{_EIA_2025_TABLE_2_10_SOURCE} Converts 26.16 cents/kWh to " + f"0.2616 USD/kWh. {_EIA_PRICE_LIMITATION}" + ), + notes=_FLAT_TARIFF_NOTES_2025, +) + + +california_2025_industrial_flat_power_tariff = _flat_power_tariff( + name="california_2025_industrial_flat_power_tariff", + description=( + "California 2025 industrial average retail electricity price, " + "represented as a flat tariff." + ), + usd_per_kwh=0.2096, + source=( + f"{_EIA_2025_TABLE_2_10_SOURCE} Converts 20.96 cents/kWh to " + f"0.2096 USD/kWh. {_EIA_PRICE_LIMITATION}" + ), + notes=_FLAT_TARIFF_NOTES_2025, +) + + POWER_PRICE_PRESETS = ( us_2024_commercial_flat_power_tariff, us_2024_industrial_flat_power_tariff, california_2024_commercial_flat_power_tariff, california_2024_industrial_flat_power_tariff, + us_2025_commercial_flat_power_tariff, + us_2025_industrial_flat_power_tariff, + california_2025_commercial_flat_power_tariff, + california_2025_industrial_flat_power_tariff, ) @@ -166,4 +261,8 @@ def _flat_power_tariff( "us_2024_industrial_flat_power_tariff", "california_2024_commercial_flat_power_tariff", "california_2024_industrial_flat_power_tariff", + "us_2025_commercial_flat_power_tariff", + "us_2025_industrial_flat_power_tariff", + "california_2025_commercial_flat_power_tariff", + "california_2025_industrial_flat_power_tariff", ] diff --git a/gpu_stack/presets/scenarios.py b/gpu_stack/presets/scenarios.py index c9e3306..36eced8 100644 --- a/gpu_stack/presets/scenarios.py +++ b/gpu_stack/presets/scenarios.py @@ -247,6 +247,7 @@ def _pythia_70m_dgx_h100_us_2024_industrial_full_tco_assumption() -> Preset: pythia_70m_dgx_h100_single_node_run_closure, dgx_h100_tco.dgx_h100_node_power_bom, dgx_h100_tco.pythia_70m_dgx_h100_run_closure_assumption, + dgx_h100_tco.dgx_h100_site_power_with_pue, name="pythia_70m_dgx_h100_us_2024_industrial_full_tco_assumption", description=( "Fully-closed scenario pack for Pythia-70M on a single DGX H100 " diff --git a/gpu_stack/scopes/economics_capex.py b/gpu_stack/scopes/economics_capex.py index ab8de60..91b2f1b 100644 --- a/gpu_stack/scopes/economics_capex.py +++ b/gpu_stack/scopes/economics_capex.py @@ -41,6 +41,7 @@ CAPEX_BOM_REF, DIMENSIONLESS, FACILITY_CAPEX_REF, + FACILITY_LIFE_REF, USD, ) from .economics_capex_site import ( @@ -50,7 +51,9 @@ cluster_capex_total, cluster_depreciable_base, cluster_facility_capex, + cluster_facility_capex_rate, cluster_it_capex, + cluster_it_capex_rate, cluster_residual_value, cluster_spine_network_capex, cluster_storage_capex, @@ -61,13 +64,16 @@ eq_cluster_capex_total, eq_cluster_depreciable_base, eq_cluster_facility_capex, + eq_cluster_facility_capex_rate, eq_cluster_it_capex, + eq_cluster_it_capex_rate, eq_cluster_residual_value, eq_cooling_infra_capex, eq_job_share_of_cluster, eq_node_capex, eq_power_infra_capex, eq_rack_capex, + facility_useful_life, job_share_of_cluster, node_capex, node_chassis_capex, @@ -109,6 +115,7 @@ building_shell_unit_cost, power_infra_unit_cost, cooling_infra_unit_cost, + facility_useful_life, building_shell_capex, power_infra_capex, cooling_infra_capex, @@ -119,6 +126,8 @@ cluster_capex_total, cluster_residual_value, cluster_depreciable_base, + cluster_it_capex_rate, + cluster_facility_capex_rate, cluster_capex_rate, job_share_of_cluster, ] @@ -137,6 +146,8 @@ eq_cluster_capex_total, eq_cluster_residual_value, eq_cluster_depreciable_base, + eq_cluster_it_capex_rate, + eq_cluster_facility_capex_rate, eq_cluster_capex_rate, eq_job_share_of_cluster, ] @@ -156,13 +167,15 @@ "node_capex", "rack_capex", "cluster_it_capex", "cluster_facility_capex", "cluster_capex_total", "cluster_residual_value", "cluster_depreciable_base", - "cluster_capex_rate", "job_share_of_cluster", + "cluster_it_capex_rate", "cluster_facility_capex_rate", + "cluster_capex_rate", "job_share_of_cluster", "facility_useful_life", "eq_gpu_residual_value", "eq_amortized", "eq_gpu_rental_markup", "eq_node_capex", "eq_rack_capex", "eq_cluster_it_capex", "eq_building_shell_capex", "eq_power_infra_capex", "eq_cooling_infra_capex", "eq_cluster_facility_capex", "eq_cluster_capex_total", "eq_cluster_residual_value", "eq_cluster_depreciable_base", + "eq_cluster_it_capex_rate", "eq_cluster_facility_capex_rate", "eq_cluster_capex_rate", "eq_job_share_of_cluster", "ECON_CAPEX_VARIABLES", "ECON_CAPEX_EQUATIONS", ] diff --git a/gpu_stack/scopes/economics_capex_refs.py b/gpu_stack/scopes/economics_capex_refs.py index c98043c..832e4e5 100644 --- a/gpu_stack/scopes/economics_capex_refs.py +++ b/gpu_stack/scopes/economics_capex_refs.py @@ -30,10 +30,22 @@ kind="model", ) +FACILITY_LIFE_REF = Reference( + "Facility capex (building shell, electrical and mechanical plant) is " + "depreciated over its own useful life, longer than the IT equipment it " + "houses: Amazon's 10-K FY2025 lists servers and networking equipment at " + "five to six years, heavy equipment at ten to thirteen years and " + "buildings at up to forty years " + "(https://app.edgar.tools/companies/AMZN/disclosures/property-plant-equipment, " + "accessed 2026-10-26).", + kind="model", +) + __all__ = [ "CAPEX_BOM_REF", "DIMENSIONLESS", "FACILITY_CAPEX_REF", + "FACILITY_LIFE_REF", "USD", ] diff --git a/gpu_stack/scopes/economics_capex_site.py b/gpu_stack/scopes/economics_capex_site.py index 754a9e6..b43c4e5 100644 --- a/gpu_stack/scopes/economics_capex_site.py +++ b/gpu_stack/scopes/economics_capex_site.py @@ -9,10 +9,13 @@ floor area, electrical design capacity, and cooling design capacity, each times a unit cost. -The totals then turn into rates: subtract residual value to get the -depreciable base, divide by useful life for a cluster-wide capex rate in -USD/s, and scale by a job's share of the cluster to charge one training run -its fair slice. The finance and recovery helpers consume those rates. +The totals then turn into rates. IT equipment (servers, network, storage) +is depreciated over its own useful life after its residual value is removed; +the facility (building shell, power and cooling plant) outlives it and is +depreciated over a separate, longer facility life. The two rates add to a +cluster-wide capex rate in USD/s, and a job's share of the cluster then +charges one training run its fair slice. The finance and recovery helpers +consume those rates. """ from ..core import eq, var @@ -29,6 +32,7 @@ CAPEX_BOM_REF, DIMENSIONLESS, FACILITY_CAPEX_REF, + FACILITY_LIFE_REF, USD, ) from .parallelism import n_gpus_total @@ -123,6 +127,13 @@ sp_units=USD / WATT, references=[FACILITY_CAPEX_REF], ) +facility_useful_life = var( + "econ.facility.useful_life", "T_life_fac", "s", + "Depreciation horizon of facility capex (building shell, power infrastructure, cooling plant) in seconds.", + scope="economics", + sp_units=SECOND, + references=[FACILITY_LIFE_REF], +) building_shell_capex = var( "econ.facility.building_shell_capex", "C_bldg", "USD", "Building shell and civil works capex allocated to the site.", @@ -193,9 +204,23 @@ sp_units=USD, references=[CAPEX_BOM_REF], ) +cluster_it_capex_rate = var( + "econ.cluster.it_capex_rate", "Cdot_IT_site", "USD/s", + "Straight-line IT-equipment capex charge per second after residual value, before utilization allocation.", + scope="economics", + sp_units=USD / SECOND, + references=[CAPEX_BOM_REF], +) +cluster_facility_capex_rate = var( + "econ.cluster.facility_capex_rate", "Cdot_fac_site", "USD/s", + "Straight-line facility capex charge per second over the facility life, before utilization allocation.", + scope="economics", + sp_units=USD / SECOND, + references=[FACILITY_LIFE_REF], +) cluster_capex_rate = var( "econ.cluster.capex_rate", "Cdot_site", "USD/s", - "Straight-line site capex charge per second before utilization allocation.", + "Straight-line site capex charge per second (IT rate plus facility rate) before utilization allocation.", scope="economics", sp_units=USD / SECOND, references=[CAPEX_BOM_REF], @@ -295,8 +320,8 @@ eq_cluster_residual_value = eq( "econ.eq.cluster_residual_value", cluster_residual_value.symbol, - residual_value_fraction.symbol * cluster_capex_total.symbol, - "Site residual value equals residual fraction times total site capex.", + residual_value_fraction.symbol * cluster_it_capex.symbol, + "Site residual value equals residual fraction times IT capex. The facility is depreciated to zero over its own life, so it carries no residual here.", references=[CAPEX_BOM_REF], check_units=True, ) @@ -310,12 +335,30 @@ check_units=True, ) +eq_cluster_it_capex_rate = eq( + "econ.eq.cluster_it_capex_rate", + cluster_it_capex_rate.symbol, + (cluster_it_capex.symbol - cluster_residual_value.symbol) / useful_life.symbol, + "IT capex charge rate equals IT capex minus residual value, divided by the IT useful life.", + references=[CAPEX_BOM_REF], + check_units=True, +) + +eq_cluster_facility_capex_rate = eq( + "econ.eq.cluster_facility_capex_rate", + cluster_facility_capex_rate.symbol, + cluster_facility_capex.symbol / facility_useful_life.symbol, + "Facility capex charge rate equals facility capex divided by the facility useful life, which is longer than the IT life.", + references=[FACILITY_LIFE_REF], + check_units=True, +) + eq_cluster_capex_rate = eq( "econ.eq.cluster_capex_rate", cluster_capex_rate.symbol, - cluster_depreciable_base.symbol / useful_life.symbol, - "Site capex charge rate equals depreciable site capex divided by useful life.", - references=[CAPEX_BOM_REF], + cluster_it_capex_rate.symbol + cluster_facility_capex_rate.symbol, + "Site capex charge rate equals the IT capex rate plus the facility capex rate.", + references=[CAPEX_BOM_REF, FACILITY_LIFE_REF], check_units=True, ) @@ -336,7 +379,9 @@ "cluster_capex_total", "cluster_depreciable_base", "cluster_facility_capex", + "cluster_facility_capex_rate", "cluster_it_capex", + "cluster_it_capex_rate", "cluster_residual_value", "cluster_spine_network_capex", "cluster_storage_capex", @@ -346,6 +391,8 @@ "eq_cluster_capex_rate", "eq_cluster_capex_total", "eq_cluster_depreciable_base", + "eq_cluster_facility_capex_rate", + "eq_cluster_it_capex_rate", "eq_cluster_facility_capex", "eq_cluster_it_capex", "eq_cluster_residual_value", @@ -354,6 +401,7 @@ "eq_node_capex", "eq_power_infra_capex", "eq_rack_capex", + "facility_useful_life", "job_share_of_cluster", "node_capex", "node_chassis_capex", diff --git a/tests/test_dgx_h100_tco_packs.py b/tests/test_dgx_h100_tco_packs.py index 24cbfa2..f0099b9 100644 --- a/tests/test_dgx_h100_tco_packs.py +++ b/tests/test_dgx_h100_tco_packs.py @@ -137,9 +137,11 @@ def test_preset_assignments_are_all_root_inputs(self): ] assert non_roots == [] - def test_preset_covers_all_33_missing_roots_of_original_pack(self): - # The original pack has 33 missing root inputs. This assumption closure - # must cover all of them directly or via upstream decomposition. + def test_preset_covers_all_34_missing_roots_of_original_pack(self): + # The original pack has 34 missing root inputs: the 33 it had before + # the facility got its own useful life, plus econ.facility.useful_life. + # This assumption closure must cover all of them directly or via + # upstream decomposition. original = scenarios.pythia_70m_dgx_h100_us_2024_industrial_power original_result = original.resolve("econ.cost.per_token") original_missing = set(original_result.missing) @@ -153,7 +155,8 @@ def test_preset_covers_all_33_missing_roots_of_original_pack(self): "full_tco pack still has missing roots: " f"{sorted(full_result.missing)}" ) - assert len(original_missing) == 33 + assert len(original_missing) == 34 + assert "econ.facility.useful_life" in original_missing def test_water_latent_heat_is_physical_constant(self): # Water latent heat at 20 degC: NIST value ~2454 kJ/kg. @@ -293,25 +296,54 @@ def test_pack_tokens_per_second_matches_original_sourced_pack(self): float(original_result.value), rel=1e-9 ) - def test_pack_job_dc_power_matches_original_sourced_pack(self): - # DC power is driven by thermal.dc.total_power override, unchanged. + def test_pack_job_dc_power_is_original_power_times_pue(self): + # The original pack pins site power to the 10.2 kW system maximum with no + # facility overhead. The full TCO pack multiplies it by the assumed PUE. original = scenarios.pythia_70m_dgx_h100_us_2024_industrial_power full_result = FULL_TCO_PACK.resolve("econ.job.dc_power") original_result = original.resolve("econ.job.dc_power") + assert float(original_result.value) == pytest.approx(10_200.0, rel=1e-9) assert float(full_result.value) == pytest.approx( - float(original_result.value), rel=1e-9 + float(original_result.value) * dgx_h100_tco.ASSUMED_PUE, rel=1e-9 ) - def test_pack_run_power_cost_matches_original_sourced_pack(self): - # Run power cost is driven by electricity price and DC power, unchanged. + def test_pack_run_power_cost_is_original_power_cost_times_pue(self): + # Run power cost is electricity price x site power x time, so the PUE + # factor on site power carries straight through. original = scenarios.pythia_70m_dgx_h100_us_2024_industrial_power full_result = FULL_TCO_PACK.resolve("econ.run.power_cost") original_result = original.resolve("econ.run.power_cost") assert float(full_result.value) == pytest.approx( - float(original_result.value), rel=1e-9 + float(original_result.value) * dgx_h100_tco.ASSUMED_PUE, rel=1e-9 + ) + + def test_pack_demand_charge_is_zero_because_eia_price_contains_it(self): + # ROADMAP item 4: the EIA average price is revenue / sales, so it already + # includes demand charges; adding a capacity charge counted it twice. + full_result = FULL_TCO_PACK.resolve("econ.run.capacity_charge_cost") + assert float(full_result.value) == 0.0 + + def test_pack_facility_capex_uses_its_own_longer_life(self): + # ROADMAP item 4: a 4-year IT life was applied to the whole facility. + facility_capex = float(FULL_TCO_PACK.resolve("econ.cluster.facility_capex").value) + facility_rate = float(FULL_TCO_PACK.resolve("econ.cluster.facility_capex_rate").value) + it_life_s = float(FULL_TCO_PACK.resolve("econ.asset.useful_life").value) + facility_life_s = float(FULL_TCO_PACK.resolve("econ.facility.useful_life").value) + + assert facility_life_s == pytest.approx( + dgx_h100_tco.FACILITY_USEFUL_LIFE_YEARS * dgx_h100_tco.SECONDS_PER_JULIAN_YEAR ) + assert facility_life_s > it_life_s + assert facility_rate == pytest.approx(facility_capex / facility_life_s, rel=1e-9) + + def test_pack_capex_rate_is_it_rate_plus_facility_rate(self): + it_rate = float(FULL_TCO_PACK.resolve("econ.cluster.it_capex_rate").value) + facility_rate = float(FULL_TCO_PACK.resolve("econ.cluster.facility_capex_rate").value) + total_rate = float(FULL_TCO_PACK.resolve("econ.cluster.capex_rate").value) + + assert total_rate == pytest.approx(it_rate + facility_rate, rel=1e-9) def test_pack_cost_per_token_trace_uses_capex_and_opex_equations(self): result = FULL_TCO_PACK.resolve("econ.cost.per_token") diff --git a/tests/test_import_registry.py b/tests/test_import_registry.py index d83c414..82b6265 100644 --- a/tests/test_import_registry.py +++ b/tests/test_import_registry.py @@ -1,7 +1,7 @@ """Pins the registry's published size and proves it can rebuild from empty. -PUBLISHED_SNAPSHOT records the exact counts the project advertises — 1316 -variables, 733 equations, 603 root inputs, and the rest. Any scope change +PUBLISHED_SNAPSHOT records the exact counts the project advertises — 1319 +variables, 735 equations, 604 root inputs, and the rest. Any scope change that adds or removes a variable moves these numbers, and that is the point: the change must be seen and the snapshot updated deliberately. If a change legitimately moves the numbers, update the expectations here — never @@ -17,18 +17,18 @@ PUBLISHED_SNAPSHOT = { "systems": 17, - "variables": 1316, + "variables": 1319, "constants": 18, - "equations": 733, - "root_inputs": 603, - "leaves": 251, - "topological_order_length": 1316, - "with_sp_units": 1298, - "with_references": 1298, - "equations_with_references": 733, - "equations_with_unit_check": 670, - "root_kind": 603, - "derived_kind": 695, + "equations": 735, + "root_inputs": 604, + "leaves": 252, + "topological_order_length": 1319, + "with_sp_units": 1301, + "with_references": 1301, + "equations_with_references": 735, + "equations_with_unit_check": 672, + "root_kind": 604, + "derived_kind": 697, "measured_kind": 0, "definitional_kind": 18, } From 1396697705833351da155529677e9e08516ef9f9 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 17:46:39 -0700 Subject: [PATCH 28/52] Packing: remaining v3 eval parts (Colab CPU, 8 cores), ratio traces, compute record; merge writes compute field Eval groups run: timestral-5M fixed levels, learned (4 penalties), timestral-28M, seasonal naive, ETS, ARIMA (q in 0.5/0.9/0.99), reactive, oracle; seeds 500-502. All simulated. 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-436,6 +437,7 @@ def stage_merge(args) -> None: "run_options": sorted({json.JSONEncoder(sort_keys=True).encode(json.loads(f.read_text())["provenance"].get("options")) for f in files}), "test_seeds_run": seeds, "calibration": cal, + "compute": json.loads(COLAB_PATH.read_text()) if COLAB_PATH.exists() else None, "metric_definitions": { "window": "evaluation window = last (horizon_s - warmup_s) seconds of each episode; warm-up excluded", "gpu_hours": "useful GPU-hours delivered in the window (net of work lost to faults), summed over jobs", @@ -458,9 +460,10 @@ def stage_merge(args) -> None: } from heatwave.provenance import write_result - inputs = [SCENARIO, SIM_SCENARIO, E002_RAW, CAL_PATH, MODEL_PATH] + files + inputs = [SCENARIO, SIM_SCENARIO, E002_RAW, CAL_PATH, MODEL_PATH, COLAB_PATH] + files record = write_result(OUT, result, script=__file__, data_kind="simulated", inputs=inputs, - notes="All outputs simulated. Held-out episodes only; training episodes are listed separately.") + notes="All outputs simulated. Held-out episodes only; training episodes are listed separately. " + "Most eval parts ran on a Colab CPU runtime (8 cores, high-RAM); see the compute field and packing-v1-parts/colab-run.json.") record["provenance"]["git"] = git_state() record["provenance"]["script"] = str(Path(__file__).resolve().relative_to(ROOT)) record["provenance"]["inputs"] = {str(Path(p).resolve().relative_to(ROOT)): sha_file(Path(p)) for p in inputs if Path(p).exists()} From e0a23649d441d0be29fd356dc5548c5198ba702f Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 17:46:46 -0700 Subject: [PATCH 29/52] Packing: merged result packing-v1.json (simulated, seeds 500-502) Co-Authored-By: Claude Sonnet --- .../e003-heatwave/results/packing-v1.json | 7723 +++++++++++++++++ 1 file changed, 7723 insertions(+) create mode 100644 experiments/e003-heatwave/results/packing-v1.json diff --git a/experiments/e003-heatwave/results/packing-v1.json b/experiments/e003-heatwave/results/packing-v1.json new file mode 100644 index 0000000..d494185 --- /dev/null +++ b/experiments/e003-heatwave/results/packing-v1.json @@ -0,0 +1,7723 @@ +{ + "provenance": { + "script": "experiments/e003-heatwave/run_packing.py", + "script_sha256": "706bc00638bb72faccff2ced10e1a61b81cd8151f803af9985a5b152d5b8ec4a", + "git": { + "commit": "1396697705833351da155529677e9e08516ef9f9", + "branch": "hw-pack", + "dirty": "false" + }, + "inputs": { + "experiments/e003-heatwave/packing-scenario-v1.json": "c18c7d1b68329c97a7568f63cfd3cbe270bcb9dffb6fcbb1c03c37548a63e831", + "experiments/e003-heatwave/sim-scenario-v3.json": "bb46cdf8daca9b2c12a7b16c458fd7170311bdfb81ec3430238c9077483c7a6f", + "docs/data/e002-checkpoint-power-raw-v1.json": "d4095c17409c6cea9de0fa1fe15b85c0957b3619f20c80b6efb508f979a7912e", + "experiments/e003-heatwave/results/packing-v1-parts/calibration.json": "bcba21cafc52d6868c3883de7f59a5d6582535e724aa75d9711199952fdbd3c2", + "experiments/e003-heatwave/results/packing-v1-parts/model.json": "9e790b1d45301db378e0ed14d85e6757d6908bb5f68a911ed37444d24946f184", 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Held-out episodes only; training episodes are listed separately. Most eval parts ran on a Colab CPU runtime (8 cores, high-RAM); see the compute field and packing-v1-parts/colab-run.json." + }, + "result": { + "data_kind": "simulated", + "scenario_id": "e003-heatwave-packing-v1", + "scenario": { + "episode": { + "horizon_s": 147600, + "warmup_s": 90000, + "base_dt_s": 10.0, + "control_dt_s": 10.0, + "controller_interval_s": 100.0, + "decision_interval_s": 300.0, + "forecast_bin_s": 300.0, + "forecast_horizon_bins": 36, + "context_bins": 576, + "min_context_bins": 24, + "warm_ratio_bins": 24, + "start_hour_utc": 13.0, + "end_rule": "overlap" + }, + "cap": { + "load_fraction": 0.55, + "design_outdoor_c": 35.0, + "calibration": { + "idle_hours": 1, + "full_hours": 2 + } + }, + "jobs": { + "generate": { + "utilization_goal": 1.5, + "gpu_choices": [ + 4, + 8, + 16, + 32 + ], + "gpu_weights": [ + 3, + 3, + 2, + 1 + ], + "duration_range_s": [ + 5400, + 36000 + ], + "step_time_choices_s": [ + 2.0, + 4.0, + 8.0 + ], + "checkpoint_every_range_s": [ + 1200, + 3600 + ], + "checkpoint_duration_range_s": [ + 20, + 120 + ], + "value_range": [ + 1.0, + 4.0 + ], + "flexible_fraction": 1.0, + "multisite_fraction": 0.1, + "max_start_delay_s": 0, + "initial_fraction": 0.5 + } + }, + "heatwave_draw": { + "center_h_range": [ + 28.0, + 36.0 + ], + "site_offset_h": 2.0, + "width_h_range": [ + 6.0, + 10.0 + ], + "add_c_range": [ + 6.0, + 12.0 + ] + }, + "events": { + "rates_per_day": { + "cooling_failure": 0.3, + "breaker_trip": 0.3, + "grid_curtailment": 0.3, + "hardware_fault": 1.0 + }, + "precursor_probability": 0.5, + "lead_range_s": [ + 1800, + 7200 + ], + "strength_ranges": { + "supply_temp_c": [ + 2.0, + 5.0 + ], + "gpu_temp_c": [ + 3.0, + 8.0 + ] + }, + "duration_range_s": [ + 1800, + 7200 + ], + "remaining_capacity_range": [ + 0.3, + 0.7 + ], + "derate_range": [ + 0.6, + 0.9 + ], + "limit_fraction_range": [ + 0.5, + 0.8 + ], + "repair_range_s": [ + 7200, + 21600 + ] + }, + "penalty": { + "default_usd_per_kwh": 10.0, + "sweep_usd_per_kwh": [ + 2.5, + 10.0, + 40.0, + 160.0 + ] + }, + "reward_window_s": 10800, + "queue_order": "value_density", + "reactive": { + "margin": 0.97 + }, + "policies": { + "full_grid_forecasters": [ + "timestral-5M" + ], + "subset_forecasters": [ + "timestral-28M", + "seasonal-naive", + "ets", + "arima" + ], + "subset_q": [ + 0.5, + 0.8, + 0.9, + 0.95, + 0.99 + ], + "use_covariates": [ + "timestral-5M", + "timestral-28M" + ], + "forecast_refresh_bins": { + "ets": 12, + "arima": 12 + } + }, + "learning": { + "bandwidths": [ + 1.0, + 2.0, + 4.0, + 8.0 + ], + "ridges": [ + 10.0, + 100.0, + 1000.0, + 10000.0 + ], + "n_rff": 256, + "model_seed": 0 + }, + "bootstrap": { + "n_boot": 2000, + "level": 0.95, + "seed": 0 + }, + "seeds": { + "calibration": 9000, + "dev": [ + 9101, + 9102, + 9103 + ], + "train": [ + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108 + ], + "test": [ + 500, + 501, + 502, + 503, + 504, + 505 + ] + } + }, + "assumptions": [ + "Everything here is simulated; no number is a measurement of a real cluster.", + "Cap: each site cap is its calibrated idle power plus cap.load_fraction of the calibrated idle-to-full-load power span, both measured in the simulator at a constant design outdoor temperature (cap.design_outdoor_c). The design temperature and load fraction are assumptions chosen on calibration and dev seeds, not on test seeds.", + "Jobs: synthetic queue with a backlog (utilization goal above 1), GPU counts, durations (1.5 to 10 h, log-uniform), checkpoint cadence and value per GPU-hour (USD, no source) are assumptions. All jobs are marked flexible so the packer may hold them; a job is available at its submit time.", + "Heat wave: each episode draws a heat wave (centre, width, size) added to the sim scenario's diurnal outdoor temperature; episode time 0 is 06:00 local at the first site (start_hour_utc). The event draw rates, precursor probability and ranges are assumptions of the simulation.", + "Over-cap penalty (USD per kWh above the site cap) is a scenario parameter; the default and the sweep are assumptions.", + "Planning horizon (3 h in 5-minute steps) and the learned policy's surrogate reward window (3 h) are scenario choices.", + "Warm-up (first 25 h of each 41 h episode) uses one shared rule for all forecast policies and is excluded from every reported metric; the evaluation window is the last 16 h.", + "ETS and ARIMA refit once per hour of decisions (forecast_refresh_bins = 12) and serve the 11 decisions in between from the same paths shifted by the elapsed steps, to keep their run time down; every other forecaster is called at every decision.", + "Running jobs are planned to end at their declared walltime stretched by the checkpoint overhead implied by the job generator's checkpoint ranges, and count in every 5-minute bin they overlap (end_rule 'overlap')." + ], + 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"stranded_intervals": 192, + "mean_power_over_cap": 0.9338566185844296, + "energy_kwh": 1891.4391846665944, + "forecast_calls": 192, + "forecast_refresh_bins": 1, + "job_faults": 12 + } + } + } + } +} From fa26e66d833a4ed4a8c7d3766674b0c3956107f9 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 17:57:43 -0700 Subject: [PATCH 30/52] Cost audit: verified WIP, measured node power from all five table rows, test updates, sensitivity and before/after scripts, CORRECTION note Re-read the key sources against their pages (EIA 2.10, IntuitionLabs, AMCompute, arXiv 2506.14551, JLL, Spheron, Lambda, Microsoft life). Node power default now uses all five listed averages (median 7.67 kW; extra server power 0.71). Updated tests that pinned old counts and defaults; added tests/test_calculator_cost_basis.py. Added run_cost_audit.py (one-at-a-time and grid) and run_before_after.py; results follow in the next commit. Co-Authored-By: Claude Sonnet --- docs/data/calculator-model.json | 28 +- docs/data/drivers.json | 505 +++++++------- .../e003-heatwave/cost_audit/CORRECTION.md | 116 ++++ .../e003-heatwave/cost_audit/SOURCES.md | 143 ++++ .../cost_audit/run_before_after.py | 182 +++++ .../cost_audit/run_cost_audit.py | 627 ++++++++++++++++++ .../e003-heatwave/cost_audit/sources.json | 12 +- gpu_stack/calculator.py | 22 +- gpu_stack/calculator_cost_basis.py | 9 +- gpu_stack/cli_estimate.py | 4 +- gpu_stack/presets/__init__.py | 2 +- gpu_stack/presets/scenarios.py | 2 +- tests/test_calculator.py | 9 +- tests/test_calculator_cost_basis.py | 137 ++++ tests/test_cli_resolve.py | 5 +- tests/test_cli_scenarios.py | 10 +- tests/test_dgx_h100_tco_packs.py | 27 +- tests/test_import_registry.py | 2 +- .../test_scenario_audit_text_family_index.py | 7 +- 19 files changed, 1532 insertions(+), 317 deletions(-) create mode 100644 experiments/e003-heatwave/cost_audit/CORRECTION.md create mode 100644 experiments/e003-heatwave/cost_audit/SOURCES.md create mode 100644 experiments/e003-heatwave/cost_audit/run_before_after.py create mode 100644 experiments/e003-heatwave/cost_audit/run_cost_audit.py create mode 100644 tests/test_calculator_cost_basis.py diff --git a/docs/data/calculator-model.json b/docs/data/calculator-model.json index bedd5df..c63f466 100644 --- a/docs/data/calculator-model.json +++ b/docs/data/calculator-model.json @@ -154,9 +154,9 @@ "id": "server_power_fraction", "label": "Extra server power (CPUs, fans, network)", "unit": "fraction of GPU power", - "default": 0.72, + "default": 0.71, "tag": "assumption", - "tooltip": "Power drawn by the rest of the server, as a share of the GPUs' own power. Default 72% makes an 8-GPU H100 node draw 7.7 kW, the measured average during LLM training (the node is rated 10.2 kW).", + "tooltip": "Power drawn by the rest of the server, as a share of the GPUs' own power. Default 71% makes an 8-GPU H100 node draw 7.7 kW (median 7.67 kW), the measured average during LLM training (the node is rated 10.2 kW).", "advanced": true, "min": 0.0, "max": 2.0 @@ -3592,16 +3592,16 @@ "residual_fraction": 0.05, "other_hardware_fraction": 0.49, "gpu_power_fraction": 0.8, - "server_power_fraction": 0.72 + "server_power_fraction": 0.71 }, "expected": { "training_days": 2.3990190112413807, "gpu_hours": 58958.291220268184, - "energy_mwh": 68.14635132403478, - "electricity_cost": 5799.254497675358, - "hardware_cost": 71950.43745984737, - "total_cost": 77749.6919575227, - "cost_per_million_tokens": 0.03887484597876143 + "energy_mwh": 67.75015160703458, + "electricity_cost": 5765.53790175864, + "hardware_cost": 71909.51360337812, + "total_cost": 77675.05150513674, + "cost_per_million_tokens": 0.03883752575256845 } }, { @@ -3620,16 +3620,16 @@ "residual_fraction": 0.05, "other_hardware_fraction": 0.49, "gpu_power_fraction": 0.8, - "server_power_fraction": 0.72 + "server_power_fraction": 0.71 }, "expected": { "training_days": 21.301443420584047, "gpu_hours": 1047008.5470085471, - "energy_mwh": 691.5282051282053, - "electricity_cost": 58849.05025641024, - "hardware_cost": 455671.61918580806, - "total_cost": 514520.6694422182, - "cost_per_million_tokens": 0.36751476388729937 + "energy_mwh": 687.5076923076924, + "electricity_cost": 58506.9046153846, + "hardware_cost": 455256.336482314, + "total_cost": 513763.24109769834, + "cost_per_million_tokens": 0.3669737436412138 } }, { diff --git a/docs/data/drivers.json b/docs/data/drivers.json index 6b69057..e976099 100644 --- a/docs/data/drivers.json +++ b/docs/data/drivers.json @@ -4,29 +4,30 @@ "method": "Every input is varied between half and double its default (fractions are capped at 1). The share is how much of the spread in cost per token an input accounts for, counting its interactions, as a share of the total. The ranges are equal by design, so the ranking mostly reflects how directly each input enters the cost.", "sobol_rows": 32768, "n_inputs_varied": 53, - "n_inputs_in_formula": 64, + "n_inputs_in_formula": 65, "inputs_held_fixed": [ "cluster.node.n_gpus", "cluster.node.nic.power_per_port", "cluster.rack.n_nodes", "cluster.site.n_racks", "econ.carbon.price_per_tonne", + "econ.power.capacity_charge_kw_month", "econ.power.peak_energy_fraction", "thermal.facility.heat_reuse_fraction", "training.overhead_fraction", "training.t_exposed_comm", "training.t_mem_bound" ], - "nominal_cost_per_token_usd": 4.8309057089408637e-08, - "nominal_cost_per_million_tokens_usd": 0.048309057089408636, - "sum_of_total_order_indices": 1.001150414685651, - "stability_max_change_half_vs_full": 2.1023448679519108e-05, + "nominal_cost_per_token_usd": 4.467871585287424e-08, + "nominal_cost_per_million_tokens_usd": 0.044678715852874244, + "sum_of_total_order_indices": 1.0015000826029512, + "stability_max_change_half_vs_full": 1.900537894175547e-05, "top": [ { "variable": "chip_peak_flops", "label": "Chip speed on paper (which GPU you buy)", - "share": 0.22966414797427234, - "total_order_index": 0.22992835698286945, + "share": 0.23134806855745157, + "total_order_index": 0.23169510977032098, "explain": "A faster chip does the same arithmetic in less time, so each token uses less rented or owned time.", "range": [ 494700000000000.0, @@ -34,23 +35,11 @@ ], "nominal": 989400000000000.0 }, - { - "variable": "mfu", - "label": "GPU speed actually achieved (MFU)", - "share": 0.22966414797427187, - "total_order_index": 0.22992835698286898, - "explain": "The share of the chip's paper speed that real training reaches. Lower means more GPU time per token.", - "range": [ - 0.2, - 0.8 - ], - "nominal": 0.4 - }, { "variable": "arch.params_total_dense", "label": "Model size (number of parameters)", - "share": 0.2296641479742706, - "total_order_index": 0.2299283569828677, + "share": 0.23134806855745046, + "total_order_index": 0.23169510977031985, "explain": "Bigger models need proportionally more arithmetic for every token.", "range": [ 3370000000.0, @@ -58,11 +47,23 @@ ], "nominal": 6740000000.0 }, + { + "variable": "mfu", + "label": "GPU speed actually achieved (MFU)", + "share": 0.23134806855744994, + "total_order_index": 0.23169510977031932, + "explain": "The share of the chip's paper speed that real training reaches. Lower means more GPU time per token.", + "range": [ + 0.2, + 0.8 + ], + "nominal": 0.4 + }, { "variable": "econ.asset.useful_life", "label": "How long the hardware is used before replacement", - "share": 0.1502089086288699, - "total_order_index": 0.15038171116327215, + "share": 0.13258331935117962, + "total_order_index": 0.13278220528197984, "explain": "The same hardware price spread over more years makes every hour cheaper.", "range": [ 63115200.0, @@ -73,8 +74,8 @@ { "variable": "econ.gpu.capex", "label": "Price of one GPU", - "share": 0.07156322538984841, - "total_order_index": 0.07164555277528944, + "share": 0.08458097591272194, + "total_order_index": 0.08470785436322925, "explain": "Most of the bill is the hardware's price spread over the hours it runs.", "range": [ 15000.0, @@ -85,8 +86,8 @@ { "variable": "econ.cluster.utilization", "label": "Share of the time the cluster is kept busy", - "share": 0.06451158003977754, - "total_order_index": 0.06458579510884985, + "share": 0.06509826733753643, + "total_order_index": 0.06519592011585174, "explain": "Idle hardware still has to be paid for, so busy time is more expensive.", "range": [ 0.45, @@ -97,8 +98,8 @@ { "variable": "training.recompute_overhead", "label": "Extra work from re-computing results to save memory", - "share": 0.010762857091385823, - "total_order_index": 0.010775238840243315, + "share": 0.010841771439793571, + "total_order_index": 0.01085803499251558, "explain": "", "range": [ 1.0, @@ -109,8 +110,8 @@ { "variable": "training.cluster_availability", "label": "Share of time the cluster is up", - "share": 0.0031563866433257843, - "total_order_index": 0.0031600177968738588, + "share": 0.0031795295869852572, + "total_order_index": 0.0031842991440042623, "explain": "", "range": [ 0.85, @@ -123,8 +124,8 @@ { "variable": "chip_peak_flops", "label": "Chip speed on paper (which GPU you buy)", - "share": 0.22966414797427234, - "total_order_index": 0.22992835698286945, + "share": 0.23134806855745157, + "total_order_index": 0.23169510977032098, "explain": "A faster chip does the same arithmetic in less time, so each token uses less rented or owned time.", "range": [ 494700000000000.0, @@ -132,23 +133,11 @@ ], "nominal": 989400000000000.0 }, - { - "variable": "mfu", - "label": "GPU speed actually achieved (MFU)", - "share": 0.22966414797427187, - "total_order_index": 0.22992835698286898, - "explain": "The share of the chip's paper speed that real training reaches. Lower means more GPU time per token.", - "range": [ - 0.2, - 0.8 - ], - "nominal": 0.4 - }, { "variable": "arch.params_total_dense", "label": "Model size (number of parameters)", - "share": 0.2296641479742706, - "total_order_index": 0.2299283569828677, + "share": 0.23134806855745046, + "total_order_index": 0.23169510977031985, "explain": "Bigger models need proportionally more arithmetic for every token.", "range": [ 3370000000.0, @@ -156,11 +145,23 @@ ], "nominal": 6740000000.0 }, + { + "variable": "mfu", + "label": "GPU speed actually achieved (MFU)", + "share": 0.23134806855744994, + "total_order_index": 0.23169510977031932, + "explain": "The share of the chip's paper speed that real training reaches. Lower means more GPU time per token.", + "range": [ + 0.2, + 0.8 + ], + "nominal": 0.4 + }, { "variable": "econ.asset.useful_life", "label": "How long the hardware is used before replacement", - "share": 0.1502089086288699, - "total_order_index": 0.15038171116327215, + "share": 0.13258331935117962, + "total_order_index": 0.13278220528197984, "explain": "The same hardware price spread over more years makes every hour cheaper.", "range": [ 63115200.0, @@ -171,8 +172,8 @@ { "variable": "econ.gpu.capex", "label": "Price of one GPU", - "share": 0.07156322538984841, - "total_order_index": 0.07164555277528944, + "share": 0.08458097591272194, + "total_order_index": 0.08470785436322925, "explain": "Most of the bill is the hardware's price spread over the hours it runs.", "range": [ 15000.0, @@ -183,8 +184,8 @@ { "variable": "econ.cluster.utilization", "label": "Share of the time the cluster is kept busy", - "share": 0.06451158003977754, - "total_order_index": 0.06458579510884985, + "share": 0.06509826733753643, + "total_order_index": 0.06519592011585174, "explain": "Idle hardware still has to be paid for, so busy time is more expensive.", "range": [ 0.45, @@ -195,8 +196,8 @@ { "variable": "training.recompute_overhead", "label": "Extra work from re-computing results to save memory", - "share": 0.010762857091385823, - "total_order_index": 0.010775238840243315, + "share": 0.010841771439793571, + "total_order_index": 0.01085803499251558, "explain": "", "range": [ 1.0, @@ -207,8 +208,8 @@ { "variable": "training.cluster_availability", "label": "Share of time the cluster is up", - "share": 0.0031563866433257843, - "total_order_index": 0.0031600177968738588, + "share": 0.0031795295869852572, + "total_order_index": 0.0031842991440042623, "explain": "", "range": [ 0.85, @@ -219,8 +220,8 @@ { "variable": "econ.maintenance.fraction_per_year", "label": "Yearly maintenance as a share of hardware price", - "share": 0.001443715584346182, - "total_order_index": 0.001445376455956317, + "share": 0.0016704797237373163, + "total_order_index": 0.0016729855813094774, "explain": "", "range": [ 0.01, @@ -231,8 +232,8 @@ { "variable": "training.optimizer_flop_multiplier", "label": "Extra work done by the optimizer", - "share": 0.0010855775880556382, - "total_order_index": 0.001086826452455351, + "share": 0.0010935371518852122, + "total_order_index": 0.001095177547942436, "explain": "", "range": [ 1.0, @@ -243,8 +244,8 @@ { "variable": "pue", "label": "Datacenter overhead for cooling and power (PUE)", - "share": 0.0010453540822963994, - "total_order_index": 0.0010465566729843782, + "share": 0.0009704001868067582, + "total_order_index": 0.0009718558672448875, "explain": "Cooling and power losses add to the electricity bill, which is already small.", "range": [ 0.6, @@ -252,35 +253,11 @@ ], "nominal": 1.2 }, - { - "variable": "thermal.facility.power_design_capacity", - "label": "Size of the building's power supply", - "share": 0.0009670626399871413, - "total_order_index": 0.0009681751630501268, - "explain": "", - "range": [ - 960000.0, - 3840000.0 - ], - "nominal": 1920000.0 - }, - { - "variable": "econ.facility.power_infra_unit_cost", - "label": "Power equipment cost per watt", - "share": 0.0009663472022085213, - "total_order_index": 0.0009674589022213797, - "explain": "", - "range": [ - 0.75, - 3.0 - ], - "nominal": 1.5 - }, { "variable": "econ.power.price_kwh_offpeak", "label": "Electricity price", - "share": 0.0008150173412931503, - "total_order_index": 0.0008159549492116341, + "share": 0.0009566468680866446, + "total_order_index": 0.0009580819174106291, "explain": "Electricity is a small part of the bill, so even a doubling matters little.", "range": [ 0.04065, @@ -288,23 +265,11 @@ ], "nominal": 0.0813 }, - { - "variable": "gpu.power.total", - "label": "Power one GPU draws", - "share": 0.0007278016471192393, - "total_order_index": 0.0007286389208223263, - "explain": "", - "range": [ - 350.0, - 1400.0 - ], - "nominal": 700.0 - }, { "variable": "econ.cluster.storage_capex", "label": "Shared storage price", - "share": 0.0005910167601719387, - "total_order_index": 0.0005916966745323064, + "share": 0.0007135643332764859, + "total_order_index": 0.0007146347387189205, "explain": "", "range": [ 1280000.0, @@ -313,46 +278,34 @@ "nominal": 2560000.0 }, { - "variable": "econ.asset.residual_fraction", - "label": "Resale value of the hardware at the end", - "share": 0.0004796942886824001, - "total_order_index": 0.0004802461360367232, + "variable": "gpu.power.total", + "label": "Power one GPU draws", + "share": 0.0006865573960419475, + "total_order_index": 0.0006875872888476775, "explain": "", "range": [ - 0.025, - 0.1 + 350.0, + 1400.0 ], - "nominal": 0.05 + "nominal": 700.0 }, { - "variable": "thermal.facility.cooling_design_capacity", - "label": "Size of the building's cooling plant", - "share": 0.00043827855309298537, - "total_order_index": 0.00043878275517686936, + "variable": "econ.facility.useful_life", + "label": "Depreciation horizon of facility capex (building shell, power infrastructure, cooling plant) in seconds", + "share": 0.0005648008759915723, + "total_order_index": 0.0005656481239597788, "explain": "", "range": [ - 960000.0, - 3840000.0 + 205124400.0, + 820497600.0 ], - "nominal": 1920000.0 - }, - { - "variable": "econ.facility.cooling_infra_unit_cost", - "label": "Cooling equipment cost per watt", - "share": 0.00043811273801109136, - "total_order_index": 0.00043861674933887005, - "explain": "", - "range": [ - 0.5, - 2.0 - ], - "nominal": 1.0 + "nominal": 410248800.0 }, { "variable": "econ.staff.cost_rate", "label": "Staff cost", - "share": 0.0003750477713958929, - "total_order_index": 0.0003754792318599274, + "share": 0.00043862265219393905, + "total_order_index": 0.0004392806224037555, "explain": "", "range": [ 0.007605141075366948, @@ -360,11 +313,23 @@ ], "nominal": 0.015210282150733896 }, + { + "variable": "econ.asset.residual_fraction", + "label": "Resale value of the hardware at the end", + "share": 0.0004243232714096223, + "total_order_index": 0.00042495979136709125, + "explain": "", + "range": [ + 0.025, + 0.1 + ], + "nominal": 0.05 + }, { "variable": "econ.node.chassis_capex", "label": "Server chassis price", - "share": 0.00033329439819264854, - "total_order_index": 0.00033367782496297456, + "share": 0.00040257415756546756, + "total_order_index": 0.00040317805205562925, "explain": "", "range": [ 7500.0, @@ -372,11 +337,35 @@ ], "nominal": 15000.0 }, + { + "variable": "econ.facility.power_infra_unit_cost", + "label": "Power equipment cost per watt", + "share": 0.00024510545363378187, + "total_order_index": 0.00024547313206066634, + "explain": "", + "range": [ + 0.75, + 3.0 + ], + "nominal": 1.5 + }, + { + "variable": "thermal.facility.power_design_capacity", + "label": "Size of the building's power supply", + "share": 0.00024349120684582725, + "total_order_index": 0.00024385646376918826, + "explain": "", + "range": [ + 960000.0, + 3840000.0 + ], + "nominal": 1920000.0 + }, { "variable": "econ.rack.switch_capex", "label": "Rack network switch price", - "share": 0.00014857698892560626, - "total_order_index": 0.00014874791407561608, + "share": 0.00017950931935342365, + "total_order_index": 0.00017977859816045334, "explain": "", "range": [ 5000.0, @@ -387,8 +376,8 @@ { "variable": "econ.node.cpu_capex", "label": "Server CPU price", - "share": 0.00014855368341520472, - "total_order_index": 0.00014872458175421312, + "share": 0.00017945711075607095, + "total_order_index": 0.00017972631124589202, "explain": "", "range": [ 5000.0, @@ -397,34 +386,22 @@ "nominal": 10000.0 }, { - "variable": "thermal.facility.floor_area", - "label": "Floor area of the building", - "share": 0.00012678422590524775, - "total_order_index": 0.00012693008034063804, - "explain": "", - "range": [ - 640.0, - 2560.0 - ], - "nominal": 1280.0 - }, - { - "variable": "econ.facility.building_shell_unit_cost", - "label": "Building construction cost", - "share": 0.00012671088418791775, - "total_order_index": 0.00012685665424991933, + "variable": "econ.network.transit_price_per_gb", + "label": "Price of sending data out of the datacenter", + "share": 0.0001296339264243205, + "total_order_index": 0.00012982838802210185, "explain": "", "range": [ - 400.0, - 1600.0 + 0.04, + 0.16 ], - "nominal": 800.0 + "nominal": 0.08 }, { "variable": "econ.network.egress_bytes_per_s", "label": "Data sent out of the datacenter", - "share": 0.00011101283141703856, - "total_order_index": 0.0001111405422085964, + "share": 0.00012938393145975569, + "total_order_index": 0.0001295780180444399, "explain": "", "range": [ 64000000.0, @@ -432,23 +409,11 @@ ], "nominal": 128000000.0 }, - { - "variable": "econ.network.transit_price_per_gb", - "label": "Price of sending data out of the datacenter", - "share": 0.00011069132261870489, - "total_order_index": 0.00011081866354181958, - "explain": "", - "range": [ - 0.04, - 0.16 - ], - "nominal": 0.08 - }, { "variable": "econ.node.dram_capex", "label": "Server memory price", - "share": 9.487293637645592e-05, - "total_order_index": 9.498207959573421e-05, + "share": 0.00011458982215544471, + "total_order_index": 0.00011476171635413537, "explain": "", "range": [ 4000.0, @@ -456,11 +421,35 @@ ], "nominal": 8000.0 }, + { + "variable": "thermal.facility.cooling_design_capacity", + "label": "Size of the building's cooling plant", + "share": 0.00010957697488635686, + "total_order_index": 0.0001097413494000679, + "explain": "", + "range": [ + 960000.0, + 3840000.0 + ], + "nominal": 1920000.0 + }, + { + "variable": "econ.facility.cooling_infra_unit_cost", + "label": "Cooling equipment cost per watt", + "share": 0.00010955978552021939, + "total_order_index": 0.00010972413424846133, + "explain": "", + "range": [ + 0.5, + 2.0 + ], + "nominal": 1.0 + }, { "variable": "econ.node.nic_capex", "label": "Network card price", - "share": 4.671086377804766e-05, - "total_order_index": 4.676460064171737e-05, + "share": 5.644987325858711e-05, + "total_order_index": 5.653455273140112e-05, "explain": "", "range": [ 2800.0, @@ -471,8 +460,8 @@ { "variable": "econ.cluster.spine_network_capex", "label": "Datacenter-wide network price", - "share": 3.7246331634715846e-05, - "total_order_index": 3.728918036161505e-05, + "share": 4.5025729785403424e-05, + "total_order_index": 4.5093272099339686e-05, "explain": "", "range": [ 320000.0, @@ -483,8 +472,8 @@ { "variable": "econ.node.storage_capex", "label": "Server storage price", - "share": 3.432078037340408e-05, - "total_order_index": 3.436026350316864e-05, + "share": 4.147228634226227e-05, + "total_order_index": 4.153449819750891e-05, "explain": "", "range": [ 2400.0, @@ -493,46 +482,46 @@ "nominal": 4800.0 }, { - "variable": "cluster.node.n_cpus", - "label": "CPUs per server", - "share": 1.8916419422281597e-05, - "total_order_index": 1.8938181148984922e-05, + "variable": "econ.facility.building_shell_unit_cost", + "label": "Building construction cost", + "share": 3.169643335500167e-05, + "total_order_index": 3.1743980623253116e-05, "explain": "", "range": [ - 1.0, - 4.0 + 400.0, + 1600.0 ], - "nominal": 2.0 + "nominal": 800.0 }, { - "variable": "cluster.node.cpu.power_per_cpu", - "label": "Server CPU power", - "share": 1.5698660542031382e-05, - "total_order_index": 1.5716720511663983e-05, + "variable": "thermal.facility.floor_area", + "label": "Floor area of the building", + "share": 3.151988691689247e-05, + "total_order_index": 3.156716935090349e-05, "explain": "", "range": [ - 175.0, - 700.0 + 640.0, + 2560.0 ], - "nominal": 350.0 + "nominal": 1280.0 }, { - "variable": "econ.power.capacity_charge_kw_month", - "label": "Utility charge for reserving power capacity", - "share": 1.5506376230726807e-05, - "total_order_index": 1.5524214993663862e-05, + "variable": "cluster.node.n_cpus", + "label": "CPUs per server", + "share": 1.7911006004200655e-05, + "total_order_index": 1.7937873992708912e-05, "explain": "", "range": [ - 4.0, - 16.0 + 1.0, + 4.0 ], - "nominal": 8.0 + "nominal": 2.0 }, { "variable": "econ.rack.power_distribution_capex", "label": "Rack power distribution price", - "share": 1.3423401287014326e-05, - "total_order_index": 1.3438843764986294e-05, + "share": 1.6225466952930337e-05, + "total_order_index": 1.624980649363119e-05, "explain": "", "range": [ 1500.0, @@ -540,11 +529,35 @@ ], "nominal": 3000.0 }, + { + "variable": "cluster.node.cpu.power_per_cpu", + "label": "Server CPU power", + "share": 1.4766729904301326e-05, + "total_order_index": 1.4788881218933248e-05, + "explain": "", + "range": [ + 175.0, + 700.0 + ], + "nominal": 350.0 + }, + { + "variable": "thermal.water.latent_heat", + "label": "Heat carried off by evaporating water", + "share": 1.4381265725348875e-05, + "total_order_index": 1.4402838811871889e-05, + "explain": "", + "range": [ + 1227000.0, + 4908000.0 + ], + "nominal": 2454000.0 + }, { "variable": "econ.water.price_per_liter", "label": "Water price", - "share": 1.232928658321927e-05, - "total_order_index": 1.2343470375568204e-05, + "share": 1.4247434948039666e-05, + "total_order_index": 1.42688072773419e-05, "explain": "", "range": [ 0.0025, @@ -555,8 +568,8 @@ { "variable": "thermal.water.density", "label": "Density of water", - "share": 1.2298477747085729e-05, - "total_order_index": 1.2312626096497128e-05, + "share": 1.423750365041759e-05, + "total_order_index": 1.4258861081953035e-05, "explain": "", "range": [ 0.499, @@ -564,23 +577,11 @@ ], "nominal": 0.998 }, - { - "variable": "thermal.water.latent_heat", - "label": "Heat carried off by evaporating water", - "share": 1.2075034097222106e-05, - "total_order_index": 1.2088925393777286e-05, - "explain": "", - "range": [ - 1227000.0, - 4908000.0 - ], - "nominal": 2454000.0 - }, { "variable": "cluster.node.ram", "label": "Server memory size", - "share": 3.0902574849706314e-06, - "total_order_index": 3.0938125625637842e-06, + "share": 2.902525146673372e-06, + "total_order_index": 2.906879174150525e-06, "explain": "", "range": [ 1000000000000.0, @@ -591,8 +592,8 @@ { "variable": "cluster.node.ram.power_per_byte", "label": "Server memory power", - "share": 3.079648764479221e-06, - "total_order_index": 3.0831916376445248e-06, + "share": 2.8983028295403645e-06, + "total_order_index": 2.902650523193042e-06, "explain": "", "range": [ 7.8125e-11, @@ -603,8 +604,8 @@ { "variable": "thermal.water.cycles_of_concentration", "label": "How often cooling water is reused", - "share": 1.6283371113482347e-06, - "total_order_index": 1.6302103742743201e-06, + "share": 1.9008172458194077e-06, + "total_order_index": 1.903668628701251e-06, "explain": "", "range": [ 2.25, @@ -615,8 +616,8 @@ { "variable": "cluster.node.nic.count", "label": "Network cards per server", - "share": 1.2737089194381789e-06, - "total_order_index": 1.275174212884345e-06, + "share": 1.20131907129155e-06, + "total_order_index": 1.203121149130988e-06, "explain": "", "range": [ 4.0, @@ -627,8 +628,8 @@ { "variable": "cluster.node.nic.power_per_nic", "label": "Network card power", - "share": 1.2605128231110884e-06, - "total_order_index": 1.2619629355742467e-06, + "share": 1.1883499261756746e-06, + "total_order_index": 1.1901325492261492e-06, "explain": "", "range": [ 12.45, @@ -639,8 +640,8 @@ { "variable": "cluster.node.misc.power_per_gpu", "label": "Extra power per GPU slot", - "share": 9.438838532610958e-07, - "total_order_index": 9.449697111074362e-07, + "share": 8.910336195870758e-07, + "total_order_index": 8.92370243618463e-07, "explain": "", "range": [ 12.5, @@ -651,8 +652,8 @@ { "variable": "cluster.node.misc.fixed_power", "label": "Fans and motherboard power per server", - "share": 9.410483086216545e-07, - "total_order_index": 9.421309044157998e-07, + "share": 8.873460062631547e-07, + "total_order_index": 8.886770985699482e-07, "explain": "", "range": [ 100.0, @@ -663,8 +664,8 @@ { "variable": "cluster.node.local_ssd.count", "label": "Drives per server", - "share": 1.6757716085000364e-07, - "total_order_index": 1.6776994407682518e-07, + "share": 1.5802014823642576e-07, + "total_order_index": 1.58257191511711e-07, "explain": "", "range": [ 4.0, @@ -675,8 +676,8 @@ { "variable": "cluster.node.local_ssd.power_per_drive", "label": "Power per drive", - "share": 1.6417422305012912e-07, - "total_order_index": 1.6436309148733131e-07, + "share": 1.5517839351728803e-07, + "total_order_index": 1.5541117392575723e-07, "explain": "", "range": [ 4.5, @@ -687,8 +688,8 @@ { "variable": "thermal.water.drift_fraction", "label": "Water lost as droplets from cooling towers", - "share": 3.593344818063084e-11, - "total_order_index": 3.597478654712392e-11, + "share": 4.217048189903961e-11, + "total_order_index": 4.2233741105294425e-11, "explain": "", "range": [ 0.001, @@ -699,8 +700,8 @@ { "variable": "arch.tokens_per_step", "label": "Tokens per training step", - "share": 2.8592311182971093e-31, - "total_order_index": 2.8625204197652683e-31, + "share": 2.891323789297396e-31, + "total_order_index": 2.89566101381322e-31, "explain": "", "range": [ 2097152.0, @@ -711,8 +712,8 @@ { "variable": "training.total_tokens", "label": "How much data you train on", - "share": 2.599614866068248e-31, - "total_order_index": 2.6026055011872095e-31, + "share": 2.5873765927799647e-31, + "total_order_index": 2.591257871394077e-31, "explain": "", "range": [ 1000000000000.0, @@ -763,44 +764,38 @@ "label": "How the chip is made (lithography, transistor physics)", "why": "Once you know which chip you have, how it was printed no longer changes the bill. Its speed and power are on the datasheet, and the cost formula only uses those.", "share": 0.0, - "evidence": "96 of the graph's 117 chip-physics inputs sit upstream of the cost; 0 appear in the cost formula." + "evidence": "59 of the graph's 81 chip-physics inputs sit upstream of the cost; 0 appear in the cost formula." }, { "label": "Electricity price", "why": "Electricity is a small slice of the bill, so even a doubled price barely moves it.", - "share": 0.0008150173412931503, - "evidence": "0.08% of the variation in cost per token." + "share": 0.0009566468680866446, + "evidence": "0.10% of the variation in cost per token." }, { "label": "Datacenter overhead for cooling and power (PUE)", "why": "Cooling losses add to a bill that is already mostly hardware.", - "share": 0.0010453540822963994, + "share": 0.0009704001868067582, "evidence": "0.10% of the variation in cost per token." }, { "label": "Power one GPU draws", "why": "How hungry the chip is matters little because power is a small slice of the cost.", - "share": 0.0007278016471192393, + "share": 0.0006865573960419475, "evidence": "0.07% of the variation in cost per token." }, { "label": "Water price", "why": "Cooling water is a tiny cost next to the chips.", - "share": 1.232928658321927e-05, + "share": 1.4247434948039666e-05, "evidence": "0.00% of the variation in cost per token." }, { "label": "Price of sending data out of the datacenter", "why": "Sending data out of the datacenter is a rounding error for a training run.", - "share": 0.00011069132261870489, + "share": 0.0001296339264243205, "evidence": "0.01% of the variation in cost per token." }, - { - "label": "Utility charge for reserving power capacity", - "why": "The utility's reservation charge is small next to the hardware.", - "share": 1.5506376230726807e-05, - "evidence": "0.00% of the variation in cost per token." - }, { "label": "Carbon emitted per unit of electricity", "why": "Carbon is not priced in this scenario, so it does not change the bill.", @@ -811,7 +806,7 @@ "label": "How many GPUs you use", "why": "More GPUs finish sooner but the bill per token stays about the same, because each GPU is bought and powered per hour of work.", "share": 0.0, - "evidence": "Doubling the cluster changes cost per token by -1.7%." + "evidence": "Doubling the cluster changes cost per token by -1.9%." } ], "note": "This rerun uses the corrected presets: the H100 speed is the dense BF16 datasheet value, not the FP32 figure the first run carried." diff --git a/experiments/e003-heatwave/cost_audit/CORRECTION.md b/experiments/e003-heatwave/cost_audit/CORRECTION.md new file mode 100644 index 0000000..7997f4f --- /dev/null +++ b/experiments/e003-heatwave/cost_audit/CORRECTION.md @@ -0,0 +1,116 @@ +# Cost correction (T10): what changed, why, and the numbers + +Branch `hw-costaudit`. Every figure below is copied from a result file produced by a committed script; the file and script are named next to it. +Source URLs and access dates (2026-10-26) are in `sources.json` (rendered in `SOURCES.md`). Nothing here is measured on a real cluster; +prices are published list, market, filing and tariff figures, many of them from secondary aggregators (marked in `sources.json`). + +## Before and after for the README example + +Example: 7e9 parameters, 2e12 tokens, 1,024 H100-SXM, MFU 40%, PUE 1.2 (`gpu-stack estimate --params 7e9 --tokens 2e12 --gpu H100-SXM --gpus 1024`). +"Before" is the code of the base commit 8b8a654 run in a separate process; "after" is this branch. Script `run_before_after.py`, result +`../results/cost-audit-correction-v1.json` (table in `../results/cost-audit-correction-v1.md`). + +| quantity | before | after | +|---|---|---| +| electricity cost (USD) | 4,026 | 5,766 | +| hardware cost (USD) | 67,090 | 71,910 | +| total cost (USD) | 71,116 | 77,675 | +| cost per million tokens (USD) | 0.03556 | 0.03884 | +| energy (MWh) | 49.52 | 67.75 | +| electricity share of total cost | 5.66% | 7.42% | + +The README sentence says "about 6 percent"; the base commit gives 5.66% and this branch gives 7.42%. The README text was not edited. + +The ladder in the same result applies the changes one at a time, cumulatively (first row reproduces the base commit exactly, last row equals the new default): + +| step | electricity share | +|---|---| +| base-commit inputs | 5.66% | +| electricity 0.0813 to 0.0851 USD/kWh | 5.91% | +| H100 price 30,000 to 37,500 USD | 4.79% | +| server life 4 to 5.5 years | 6.46% | +| servers, network, storage 0.40 to 0.49 of card price | 6.10% | +| extra server power 0.25 to 0.71 | 8.16% | +| facility 11.3 USD/W over 13 years added | 7.42% | + +The two changes that move the share most are the measured node power (more kWh per GPU-hour: 5,766 versus 4,215 USD of electricity at the same price) and +the card price (a bigger denominator). + +## What changed in the code + +Calculator (`gpu_stack/calculator.py`, new `gpu_stack/calculator_cost_basis.py`, which holds each default next to its URL): + +| default | before | after | source | +|---|---|---|---| +| electricity price | 0.0813 USD/kWh (EIA 2024 industrial) | 0.0851 USD/kWh | EIA Electric Power Annual 2025, Table 2.10, https://www.eia.gov/electricity/annual/html/epa_02_10.html | +| H100 price | 30,000 USD | 37,500 USD (range 35,000-40,000) | IntuitionLabs, Data Center GPU Pricing 2026, https://intuitionlabs.ai/articles/data-center-gpu-pricing-2026 | +| A100 price | 15,000 USD (no citation) | 12,500 USD (range 10,000-15,000) | Jarvislabs, https://jarvislabs.ai/blog/a100-price | +| server life | 4 years | 5.5 years (median of eight operators, range 4-6) | operator rows `life.*` in `sources.json` | +| servers, network, storage share of card price | 0.40, which included the building | 0.49, building excluded | system prices (IntuitionLabs, AMCompute) and the 24,576-GPU H100 cluster breakdown, https://www.amcompute.com/blog/ai-cluster-cost-breakdown-capex | +| extra server power | 0.25 | 0.71, so an 8-GPU node draws the measured average 7.67 kW | arXiv 2506.14551 Table 2, https://arxiv.org/pdf/2506.14551 | +| facility (building, power, cooling plant) | inside the 0.40 share, on the server life | separate term, 11.3 USD per W of provisioned power, 13-year life | JLL via https://www.datacentres.com/news/data-centre-construction-costs-11m-per-mw ; Amazon FY2025 10-K equipment lives via https://app.edgar.tools/companies/AMZN/disclosures/property-plant-equipment | + +The facility term uses the graph's own variables (`econ.cluster.facility_capex`, new `econ.facility.useful_life`, new `econ.cluster.facility_capex_rate` +and `econ.eq.cluster_facility_capex_rate`, all unit-checked) rather than a hand-typed formula; per-GPU facility capex is 11.3 USD/W times the chip rating +times (1 + extra server power). PUE already entered the calculator's site power (`thermal.dc.total_power = PUE x GPUs x TDP x load x (1 + server extra)`). + +Economics scope (`gpu_stack/scopes/economics_capex*.py`): the cluster capex rate is now the IT rate (after residual value, over the IT life) plus the facility +rate (over `econ.facility.useful_life`); residual value applies to IT capex only. + +Presets (`gpu_stack/presets/dgx_h100_tco.py`, `economics.py`, `scenarios.py`), the three ROADMAP item 4 problems: + +| ROADMAP item 4 problem | change | +|---|---| +| facility overhead (PUE) never enters site power | new preset `dgx_h100_site_power_with_pue` (10.2 kW system maximum x PUE 1.2) is part of the full-TCO pack; PUE sources: Google 1.09, AWS 1.14, Microsoft 1.17, Uptime 2026 capacity-weighted 1.36 and per-facility mean 1.52 (rows `pue.*`) | +| demand charge counted twice | `econ.power.capacity_charge_kw_month` is 0 in the full-TCO pack: the EIA average price is revenue divided by sales and already contains demand charges | +| 4-year life applied to the whole facility | `econ.facility.useful_life` = 13 years in the pack; the IT life stays at 4 years there | + +Effect on the DGX H100 full-TCO pack (Pythia-70M, single node; same result file): run power cost 9.216 to 11.059 USD, demand charge 1.260 to 0 USD, +hardware 122.75 to 112.46 USD, run total 189.79 to 180.09 USD, cost per token 6.329e-10 to 6.005e-10 USD; electricity (energy plus demand charge) share of run total 5.52% to 6.14%. +The other packs (`pythia_70m_dgx_h100_us_2024_industrial_power`, `scenarios_cited_2026`) still pin site power to the 10.2 kW system maximum and are IT-power packs. + +Also: the 2025 EIA flat-tariff presets (`us_2025_*`, `california_2025_*`) were added next to the 2024 ones, which the scenario packs still name; +`docs/data/calculator-model.json` and `docs/data/drivers.json` were regenerated with `scripts/build_site_data.py` (the website reads them); README registry counts were +regenerated by the repo tooling (counts only). Tests that pinned old counts or old values were updated (`tests/test_calculator.py`, `test_cli_resolve.py`, +`test_cli_scenarios.py`, `test_dgx_h100_tco_packs.py`, `test_scenario_audit_text_family_index.py`, `test_import_registry.py`); `tests/test_calculator_cost_basis.py` is new. + +## Where the share becomes larger (from `../results/cost-audit-v1.json`) + +Table: `../results/cost-audit-v1.md`; script `run_cost_audit.py`. Cases call `gpu_stack.calculator.estimate` for the README workload. + +- Calculator defaults: H100 7.42%; with the same method for other chips (single-run defaults for each chip) the shares are H200 6.97%, B200 8.10%, GB200 9.39%. +- Regions (H100, purchase, 5-year life, PUE 1.2): New Mexico 4.8%, Texas 5.2%, U.S. 6.9%, Virginia 7.6%, California PG&E E-20 energy plus demand charge 9.9%, Germany 14.6%, California industrial average 15.3%. +- Server life 4 / 5 / 6 years, H100 at the U.S. price: 5.7% / 6.9% / 8.0% (grid, PUE 1.2). +- PUE 1.09 / 1.36 / 1.52 (H100, U.S. price): 6.79% / 8.33% / 9.22%. +- Grid maxima (chip x life x region x PUE, purchase): H100 21.3%, H200 20.2%, B200 22.9%, GB200 25.9%. Cases at or above 20%: H100 2 of 84, H200 1, B200 5, GB200 11; at or above 30%: none. +- Rental basis (lessor's electricity as a share of the rental price, U.S. price, PUE 1.2): H100 2.5% on-demand, 4.5% one-year committed, 6.7% three-year committed. +- U.S.-average price at which the share reaches 10% (H100, 5.5-year life): 0.118 USD/kWh; 20%: 0.265 USD/kWh; B200: 0.107 and 0.241; GB200: 0.091 and 0.205. +- Residual value: 0% gives 7.11%; 45% (used-market share at age 3, an unstable figure in its source) gives 11.45%. + +## What the e003 timing savings are worth per token + +e003 (`cost-v1.json`, policy timestral_28M, flexible fraction 0.3, window 24 h, lambda 0.5) saves 8.61% of electricity cost in CAISO and 12.62% in DE-LU, on wholesale +day-ahead prices (35.19 USD/MWh and 139.36 USD/MWh). The share of a retail price that follows wholesale is the wholesale mean divided by the retail price (0.168 for the +California EIA industrial average, 0.704 for the German industry average, 0.276 for the E-20 tariff). + +- H100, 5-year life, PUE 1.2, region-matched: California average, total cost per token changes by -0.222%; Germany -1.298%; California E-20 -0.236%. +- Upper bound if the whole electricity bill moved with wholesale (the convention of the cost-v1 headline): at the calculator default (7.42%) -0.639% (CAISO saving) and -0.937% (DE-LU saving); + across the whole grid the DE-LU saving gives between -0.106% and -3.263%. +- The e003 headline totals (0.2% and 1.2%) are close to the region-matched figures here, because the saving is dollars on the wholesale part only. + +Demand charges would rise if shifting raises peak load; that is not modelled. + +## Limits and judgement calls + +- Prices for cards and systems are from secondary aggregators (IntuitionLabs, AMCompute, Wing VC, Jarvislabs), with ranges kept in `sources.json`. NVIDIA product pages were not on the fetch allowlist; partner-hosted datasheets were used for power and FLOP/s. +- The JLL 11.3 million USD per MW does not say whether the MW is IT load or total; it is read as IT load. Sweeping 0 to 15 USD/W moves the H100 share between 8.16% and 7.21%. +- The 0.49 share of servers, network and storage adds the Meta cluster's "everything else" (3.5k USD per GPU) to a system price that already contains chassis, CPUs and memory, so some overlap is possible; the one-at-a-time rows show 0.42 and 0.56. +- GPU load (80% of rating) is still an uncited calculator assumption; extra server power is set so the node matches measured power at that load. +- No operator's residual value was found (filing notes could not be read); the calculator's 5% is unchanged and uncited. +- Not in the calculator and not added, because no consistent cited basis was found: financing cost, maintenance, staff, software, insurance and network transit. They lower the electricity share of a complete cost. The only worked example found (Introl, 100 H100, April 2026) is internally inconsistent in its power lines; see the cross-check table in `../results/cost-audit-v1.md`. +- B200 and GB200 are not in `calculator.GPUS`; the audit builds them from cited rows inside the script only. A per-GPU B200 TDP (1,000 W, HGX B200 summary) is now citable, so the docstring remark that B200 was left out for lack of a figure is out of date. +- The ROADMAP item 4 list was not edited. + +## Checks run + +`python -m pytest -q`, `python -m gpu_stack.cli audit --fail-on-issues`, `python -m gpu_stack.docs_stats_check`, `ruff check .`, `node tests/test_calculator_web.mjs` (see the final report for results). diff --git a/experiments/e003-heatwave/cost_audit/SOURCES.md b/experiments/e003-heatwave/cost_audit/SOURCES.md new file mode 100644 index 0000000..3f6d307 --- /dev/null +++ b/experiments/e003-heatwave/cost_audit/SOURCES.md @@ -0,0 +1,143 @@ +# Cost audit sources (generated) + +Generated by `run_cost_audit.py` from `sources.json`. Access date for web sources: 2026-10-26. +Column `gpu_stack before` is what the calculator or presets used before the T10 correction, with where. + +## Fetch notes + +- nvidia.com product pages (H100, H200, DGX B200, GB200 NVL72) returned URL_NOT_ON_ALLOWLIST from this session's fetch tool and were not retrieved; NVIDIA datasheets hosted by PNY, HPE, Supermicro, and NVIDIA's images.nvidia.com and docs.nvidia.com were retrieved instead. +- eia.gov Electric Power Monthly state tables (5.6.A) could not be read (data is behind spreadsheet links); the Electric Power Annual Table 2.10 (2025 and 2024 by state) was read instead. +- sec.gov filings (CoreWeave, Alphabet, Oracle, Microsoft, Meta, Amazon 10-Ks) are reachable but the fetch tool cuts the text before the financial-statement notes, so useful lives were read from company press releases and third-party renderings of the filings; those rows are marked. +- nectarclimate.com returned a client error and was not used; PG&E and Dominion tariff figures come from OpenEI's U.S. Utility Rate Database pages. + +## Sources + +| id | publisher | title | published | kind | URL | +|---|---|---|---|---|---| +| INTUITION_PRICING | IntuitionLabs | Data Center GPU Pricing 2026 | 2026-07-20 (updated 2026-10-11) | secondary aggregator; cites CloudZero, DeployBase, Spheron, GPUaaS, GigaEnergy | https://intuitionlabs.ai/articles/data-center-gpu-pricing-2026 | +| AMCOMPUTE_VALUES | AMCompute | GPU server values (October 2026 Rack Report, data through August 2026) | 2026-10-17 | secondary; vendor price index, values described as indicative, not an appraisal | https://www.amcompute.com/gpu-server-values | +| AMCOMPUTE_CAPEX | AMCompute | AI Cluster Cost Breakdown: CapEx (2026) | 2026-03-02 (cites a B300 BOM dated October 2026) | secondary; vendor blog with its own per-GPU arithmetic | https://www.amcompute.com/blog/ai-cluster-cost-breakdown-capex | +| WING_NVL72 | Wing VC | How Much Does an NVIDIA NVL72 Cost? | 2026-08 | secondary; cites Tae Kim (Barron's), Tom's Hardware, Data Gravity purchase-order review | https://www.wing.vc/content/how-much-does-an-nvidia-nvl72-cost | +| JARVIS_A100 | Jarvislabs | NVIDIA A100 GPU Price in 2026 | 2026-03 snapshot (updated 2026-10) | secondary; GPU cloud vendor blog | https://jarvislabs.ai/blog/a100-price | +| HOSTINGER_GPU | Hostinger | Cloud GPU pricing compared: what GPUs cost per hour in 2026 | 2026-10-17 (rates verified 2026-09) | secondary; list rates, on-demand | https://www.hostinger.com/tutorials/?p=158956 | +| LAMBDA_PRICING | Lambda | Lambda pricing | undated (page footer 2026) | primary vendor price page | https://www.lambda.ai/pricing | +| SPHERON_CW | Spheron | CoreWeave GPU Pricing 2026 | 2026-08-12 (updated 2026-10-19; CoreWeave rate card read 2026-08-12) | secondary; reads CoreWeave's rate card | https://www.spheron.network/blog/coreweave-gpu-pricing-2026/ | +| SPHERON_RESERVE | Spheron | GPU Cluster Reservation Contract Negotiation 2026 | 2026-07-13 (updated 2026-10-19) | secondary; cites Compute Exchange discount ranges | https://www.spheron.network/blog/gpu-cluster-reservation-contract-negotiation-2026/ | +| PNY_H200 | NVIDIA via PNY | NVIDIA H200 NVL datasheet (NVIDIA datasheet, PNY-hosted) | undated | primary datasheet, third-party host | https://www.pny.com/File Library/Company/Support/linecards/data-center-gpus/h200-nvl-datasheet.pdf | +| NVIDIA_HGX_B200 | NVIDIA | HGX B200 PCF Summary | undated | primary | https://images.nvidia.com/aem-dam/Solutions/documents/HGX-B200-PCF-Summary.pdf | +| NVIDIA_DGX_B200_UG | NVIDIA | Introduction to NVIDIA DGX B200 Systems | undated | primary | https://docs.nvidia.com/dgx/dgxb200-user-guide/introduction-to-dgxb200.html | +| NVIDIA_DGX_B200_DS | NVIDIA via gpusolutions.hrp.hu | DGX B200 datasheet (NVIDIA datasheet, third-party host) | 2026-05 upload | primary datasheet, third-party host | https://gpusolutions.hrp.hu/wp-content/uploads/2026/05/dgx-scale-ai-infrastructure-dgx-b200-datasheet-nvidia-web.pdf | +| HPE_GB200 | HPE | HPE NVIDIA GB200 NVL72 by HPE QuickSpecs | undated | primary vendor spec | https://www.hpe.com/us/en/collaterals/collateral.a50009224enw.html | +| SUPERMICRO_GB200 | Supermicro | SuperCluster SRS-GB200-NVL72 | undated | primary vendor spec | https://supermicro.com/en/products/system/gpu/48u/srs-gb200-nvl72 | +| KLOCKWOOD_B200 | Glenn K. Lockwood | NVIDIA B200 (Glenn K. Lockwood, digital garden) | undated | secondary; cites NVIDIA Blackwell Architecture Technical Brief | https://glennklockwood.com/garden/processors/b200 | +| CYFUTURE_GB200 | Cyfuture | NVIDIA GB200 GPU server | undated | secondary reseller page | https://cyfuture.ai/nvidia-gb200-gpu-server | +| REPO_HARDWARE | gpu_stack repository | gpu_stack/presets/hardware.py and scenarios.py (cite NVIDIA H100 product page accessed 2026-05-06 and DGX H100/H200 User Guide Table 3 accessed 2026-06-10) | n/a | primary NVIDIA text as already cited in the repo; not re-fetched here (nvidia.com not on this session's allowlist) | https://www.nvidia.com/en-us/data-center/h100/ | +| META_Q424 | Meta Platforms | Meta Reports Fourth Quarter and Full Year 2024 Results | 2025-01-29 | primary press release | https://investor.atmeta.com/investor-news/press-release-details/2025/Meta-Reports-Fourth-Quarter-and-Full-Year-2024-Results/default.aspx | +| ALPHABET_Q422 | Alphabet | Alphabet Q4 2022 earnings release | 2023-02-02 | primary press release | https://abc.xyz/investor/static/pdf/2022Q4_alphabet_earnings_release.pdf | +| EDGAR_AMZN | edgar.tools rendering of the SEC filing | Amazon 10-K FY2025, property and equipment, estimated useful lives as of 2025-12-31 | FY2025 10-K (filed 2026) | primary filing text rendered by a third party | https://app.edgar.tools/companies/AMZN/disclosures/property-plant-equipment | +| HUDSON_MSFT | Hudson Labs | Microsoft server useful life | undated | secondary; summarizes Microsoft 10-Ks | https://www.hudson-labs.com/research/msft-server-useful-life | +| GDCH_LIVES | Global Data Center Hub | How long does an AI chip last | undated | secondary; cites annual reports and earnings releases | https://www.globaldatacenterhub.com/p/how-long-does-an-ai-chip-last-and | +| CUBE_LIVES | theCUBE Research | Resetting GPU depreciation | 2025-2026 (undated in fetch) | secondary | https://thecuberesearch.com/298-breaking-analysis-resetting-gpu-depreciation-why-ai-factories-bend-but-dont-break-useful-life-assumptions/ | +| BLOCKSPACE_CRWV | Blockspace Media | CoreWeave A100 contract challenges GPU depreciation model | 2026 | secondary; cites CNBC 2025-11-14 | https://blockspace.media/insight/coreweave-a100-contract-challenges-gpu-depreciation-model/ | +| REMIO_BERNSTEIN | remio.ai | Bernstein: AI data center costs hit $39.5 billion per GW | 2026-10-11 | secondary; summarizes Bernstein | https://www.remio.ai/post/bernstein-ai-data-center-costs-hit-39-5-billion-per-gw-but-depreciation-is-the-b | +| INVESTING_BERNSTEIN | Investing.com (Bernstein analysis) | How much does a GW of data center capacity actually cost | 2025-11-01 | secondary; summarizes Bernstein | https://www.investing.com/news/stock-market-news/how-much-does-a-gw-of-data-center-capacity-actually-cost-4314046 | +| SILICONDATA | Silicon Data | H100 GPU market value trends | data window 2024-06 to 2025-12 | secondary index vendor; text and table disagree | https://www.silicondata.com/use-cases/h100-gpu-market-value-trends | +| UPTIME | Uptime Institute Intelligence | UII Update 518: growing PUE advantage of larger data centers | 2026-08-06 | primary survey summary | https://intelligence.uptimeinstitute.com/resource/growing-pue-advantage-larger-data-centers | +| GOOGLE_PUE | Google Data Centers | Growing the internet while reducing energy consumption | page current through 2026 Q2 | primary operator report | https://www.datacenters.google/efficiency | +| MSFT_PUE | Microsoft | Measuring energy and water efficiency for Microsoft datacenters | FY25 | primary operator report | https://datacenters.microsoft.com/sustainability/efficiency/ | +| AWS_PUE | Amazon Web Services | AWS sustainability | 2025 data | primary operator report | https://aws.amazon.com/sustainability/ | +| DCMAG_META | Data Centre Magazine | Data Centre Magazine, February 2026, issue 38, page 41 | 2026-02-03 | secondary; no measurement year | https://publication.datacentremagazine.com/data-centre-magazine-february-2026-issue-38/0739753001769685783/p41 | +| JLL_COST | datacentres.com (JLL forecast) | Data Centre Construction Costs Rise to $11.3M per MW in 2026 | 2026 | secondary; cites JLL | https://www.datacentres.com/news/data-centre-construction-costs-11m-per-mw | +| CW_IREI | IREI | Cushman & Wakefield releases 2026 Data Center Development Cost Guide | 2026-08-20 | secondary; paywalled after the first paragraph | https://irei.com/news/cushman-wakefield-releases-2026-data-center-development-cost-guide-citing-21-rise-in-per-mw-construction-costs/ | +| EIA_EPA_210 | U.S. Energy Information Administration | Electric Power Annual, Table 2.10, Average Price of Electricity to Ultimate Customers by End-Use Sector, by State, 2025 and 2024 (cents per kWh) | 2026 | primary | https://www.eia.gov/electricity/annual/html/epa_02_10.html | +| OPENEI_PGE | OpenEI U.S. Utility Rate Database | PG&E E-20, maximum demand 1000 kW or more, Option R (Transmission), effective 2026-03-27 | rate effective 2026-03-27 | primary tariff data entered into a public database | https://apps.openei.org/USURDB/rate/view/69de826508c252b4010e1d40 | +| ENERGYMAG_4CP | TheEnergyMag (cites LoD estimate) | Miner Weekly: Texas' Peak Game Is Getting Harder as AI Load Floods ERCOT | 2026-06-11 | secondary | https://theenergymag.com/news/2026-06-11/texas-4cp-ai-bitcoin | +| WAVY_GS5 | WAVY | State Corporation Commission approves new Dominion rate changes for big electricity users | 2025-11-25 | secondary news | https://www.wavy.com/news/virginia/state-corporation-commission-approves-new-dominion-rate-changes-for-big-electricity-users/amp/ | +| BDEW | BDEW | BDEW-Strompreisanalyse Herbst 2026 | 2026-08-21 | primary industry association | https://www.bdew.de/service/daten-und-grafiken/bdew-strompreisanalyse/ | +| DCD_HQ | DatacenterDynamics | Canadian utility Hydro-Quebec proposes electricity tariff for data centers | 2026-02-20 | secondary news | https://www.datacenterdynamics.com/en/news/canadian-utility-hydro-qu%C3%A9bec-proposes-electricity-tariff-for-data-centers/ | +| ARXIV_NODE_POWER | arXiv 2506.14551 | Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation | 2025 | primary paper (measurements by BNL and public MLPerf Power data) | https://arxiv.org/pdf/2506.14551 | +| LBNL_NODE_POWER | Lawrence Berkeley National Laboratory | Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System | 2025-03-25 (paper), posted 2026-07 | primary measurement paper | https://eta-publications.lbl.gov/sites/default/files/2026-07/single-node_power_demand_during_ai_training.pdf | +| REPO_COST_V1 | gpu_stack repository, e003 | experiments/e003-heatwave/results/cost-v1.json (public CAISO NP15 day-ahead LMP, DE-LU day-ahead price, ECB FX; test period 2026-04-27 to 2026-10-25) | 2026-10-26 | repository result built from public series | experiments/e003-heatwave/results/cost-v1.json | +| INTROL_TCO | Introl | GPU Infrastructure TCO Model: 5-Year Cost Analysis for Enterprise AI | 2026-04-04 | secondary; worked example of 100 H100; its own power arithmetic is inconsistent (cross-check only, not an input) | https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model | + +## Rows + +| id | item | value | low-high | unit | source | basis | gpu_stack before | +|---|---|---|---|---|---|---|---| +| price.h100.gpu_sxm5 | H100 SXM5 80GB, new, per GPU | 37500 | 35000 - 40000 | USD/GPU | INTUITION_PRICING | midpoint of cited range | 30000 (gpu_stack/calculator.py GPUS['H100-SXM'].price_usd; presets/dgx_h100_tco.py econ.gpu.capex (2024 channel range 27,000-40,000, midpoint-ish 30,000)) | +| price.h100.sys_dgx | DGX H100 8-GPU system, new | 380000 | 300000 - 460000 | USD/system | INTUITION_PRICING | midpoint of cited range | | +| price.h100.sys_hgx_new | HGX H100 8-GPU server, new (estimated from GPU list price, not a quoted server price) | 339000 | | USD/system | AMCOMPUTE_VALUES | quoted estimate | | +| price.h100.sys_hgx_used | HGX H100 8-GPU server, used fair market value | 195000 | | USD/system | AMCOMPUTE_VALUES | quoted | 0.05 (calculator.py INPUTS residual_fraction (resale value at end of life, share of price)) | +| price.h200.sys_hgx_new | HGX H200 8-GPU server, new (Rack Report midpoint) | 340000 | | USD/system | AMCOMPUTE_VALUES | quoted midpoint | | +| price.h200.sys_dgx | DGX H200 8-GPU system, new | 450000 | 400000 - 500000 | USD/system | INTUITION_PRICING | midpoint of cited range | | +| price.b200.gpu | B200 new card, per GPU | 40000 | 30000 - 50000 | USD/GPU | INTUITION_PRICING | midpoint of cited range | | +| price.b200.sys_hgx | HGX B200 8-GPU server, new | 415000 | 360000 - 470000 | USD/system | AMCOMPUTE_VALUES | midpoint of cited range | | +| price.gb200.rack | GB200 NVL72 rack, 72 GPUs, new | 3e+06 | 2.8e+06 - 3.4e+06 | USD/rack | WING_NVL72 | Tae Kim (Barron's) point value; Tom's Hardware range | | +| price.gb200.rack_bernstein | GB200 NVL72 rack, all-in per Bernstein: compute hardware part and physical infrastructure part | 5.9e+06 | | USD/rack | REMIO_BERNSTEIN | quoted | | +| price.a100.sxm80 | A100 80GB SXM, new, per GPU (March 2026) | 12500 | 10000 - 15000 | USD/GPU | JARVIS_A100 | midpoint of cited range | 15000 (calculator.py GPUS['A100-80GB-SXM'].price_usd ('Rough assumption, no citation')) | +| power.h100.tdp | H100 SXM max TDP | 700 | | W | REPO_HARDWARE | quoted | 700 (calculator.py GPUS['H100-SXM'].tdp_w; presets/hardware.py gpu.tdp) | +| power.h200.tdp | H200 SXM max TDP (NVL variant 600 W) | 700 | | W | PNY_H200 | quoted | | +| power.b200.tdp | HGX B200 per-GPU max TDP | 1000 | | W | NVIDIA_HGX_B200 | quoted | | +| power.b200.dgx_system | DGX B200 system max power (8 GPUs) | 14300 | | W | NVIDIA_DGX_B200_UG | quoted | | +| power.gb200.gpu | B200 GPU inside a GB200 superchip, max power | 1200 | | W | KLOCKWOOD_B200 | quoted from secondary | | +| power.gb200.rack | GB200 NVL72 rack TDP, 72 GPUs | 132000 | | W | HPE_GB200 | quoted | | +| power.dgx_h100.system | DGX H100 system max power (8 GPUs) | 10200 | | W | REPO_HARDWARE | quoted | 10200 (presets/scenarios.py and scenarios_cited_2026.py thermal.dc.total_power (used directly as site power)) | +| power.h100.node_measured_avg | Measured average power of one 8-GPU H100 node during LLM training; median of the five listed averages (7.79, 7.67, 7.73, 5.91, 6.78 kW) | 7670 | | W | ARXIV_NODE_POWER | median of the five averages in Table 2 (re-read 2026-10-26; an earlier pass listed four and left out the 1-node SMC Llama-70B row at 6.78 kW) | 5600 (calculator.py pins node power = n_gpus x tdp_w x gpu_power_fraction (0.80) x (1 + server_power_fraction 0.25) = 700 W per GPU = 5,600 W per 8 GPUs) | +| power.h100.node_measured_peak | Maximum observed power of one 8-GPU H100 node in a single-node measurement | 8400 | | W | LBNL_NODE_POWER | quoted | | +| flops.b200.bf16_sparse_hgx8 | HGX B200 FP16/BF16 Tensor Core, 8 GPUs, with sparsity | 3.6e+16 | | FLOP/s | NVIDIA_HGX_B200 | quoted; dense is half (same convention as presets/hardware.py) | | +| flops.gb200.bf16_dense | B200 inside GB200, dense BF16 | 2.5e+15 | | FLOP/s | KLOCKWOOD_B200 | quoted from secondary | | +| flops.h200.bf16_sparse | H200 SXM BFLOAT16 Tensor Core with sparsity (same die as H100: dense is half) | 1.979e+15 | | FLOP/s | PNY_H200 | quoted; dense is half | | +| life.msft | Microsoft: servers and network equipment useful life | 6 | 2 - 6 | years | HUDSON_MSFT | upper end of disclosed range; extended from 4 to 6 years effective FY2023 | | +| life.goog | Alphabet: servers and certain network equipment useful life | 6 | | years | ALPHABET_Q422 | quoted | | +| life.meta | Meta: most servers and network assets useful life | 5.5 | | years | META_Q424 | quoted | | +| life.amzn | Amazon: servers and networking equipment useful life (a subset moved from six back to five years effective 2025-01-01) | 5 | 5 - 6 | years | EDGAR_AMZN | lower end of range; AI-related subset is at five | | +| life.orcl | Oracle: servers and networking equipment useful life | 5.5 | 5 - 6 | years | GDCH_LIVES | midpoint of range | | +| life.crwv | CoreWeave: GPU infrastructure depreciation period | 6 | | years | BLOCKSPACE_CRWV | reported by secondary sources | | +| life.nbis | Nebius: depreciation period (as listed by theCUBE Research) | 4 | | years | CUBE_LIVES | reported by secondary source | | +| life.lambda | Lambda: depreciation period (as listed by theCUBE Research) | 5 | | years | CUBE_LIVES | reported by secondary source | | +| life.bernstein_model | Bernstein model: hardware life assumed in a Vera Rubin cost model | 6 | | years | REMIO_BERNSTEIN | reported by secondary source | | +| life.gpu_stack_default | gpu_stack calculator default useful life | 4 | | years | REPO_HARDWARE | repository default | 4 (calculator.py INPUTS useful_life_years; presets/dgx_h100_tco.py econ.asset.useful_life = 126,230,400 s) | +| life.facility.heavy_equipment_amzn | Amazon: heavy equipment useful life (10 years before 2025-01-01) | 13 | 10 - 13 | years | EDGAR_AMZN | upper end of range | | +| life.facility.buildings_amzn | Amazon: buildings useful life | 40 | | years | EDGAR_AMZN | upper bound (lesser of forty years or remaining life) | | +| resid.silicondata_h100 | Used H100 price as a share of contemporaneous new price, about 3 years old (2025) | 0.57 | 0.45 - 0.69 | fraction | SILICONDATA | midpoint; the source's table says about 0.69, its text says 0.45-0.55 | 0.05 (calculator.py INPUTS residual_fraction; presets/dgx_h100_tco.py econ.asset.residual_fraction 0.05) | +| resid.accounting | Residual (salvage) value assumed in operators' depreciation | | | fraction | GDCH_LIVES | not found: none of the sources reached states a residual value; the filing notes could not be read (see fetch_notes) | | +| rent.h100.lambda_sxm | Lambda on-demand H100 SXM | 3.99 | | USD/GPU-hour | LAMBDA_PRICING | quoted | | +| rent.h100.runpod_sxm | RunPod on-demand H100 SXM | 3.29 | | USD/GPU-hour | HOSTINGER_GPU | quoted | | +| rent.h100.aws_p5 | AWS on-demand single H100 (p5.4xlarge) | 6.88 | | USD/GPU-hour | HOSTINGER_GPU | quoted | | +| rent.h100.coreweave | CoreWeave on-demand HGX H100 (8-GPU node $49.24/h) | 6.16 | | USD/GPU-hour | SPHERON_CW | quoted | | +| rent.h100.spheron_1y_reserved | One-year reserved H100 market-wide rate (Compute Exchange benchmark cited by Spheron; not a Spheron price), March 2026 | 2.35 | | USD/GPU-hour | SPHERON_RESERVE | quoted | | +| rent.h100.spheron_ondemand | Spheron on-demand H100 | 2.64 | | USD/GPU-hour | SPHERON_RESERVE | quoted | | +| rent.discount.committed_1y | Committed discount vs on-demand, one-year term (Compute Exchange via Spheron) | 0.45 | 0.4 - 0.5 | fraction | SPHERON_RESERVE | midpoint of range | | +| rent.discount.committed_3y | Committed discount vs on-demand, three-year term | 0.635 | 0.55 - 0.72 | fraction | SPHERON_RESERVE | midpoint of range | | +| rent.h200.runpod | RunPod on-demand H200 | 4.59 | | USD/GPU-hour | HOSTINGER_GPU | quoted | | +| rent.h200.coreweave | CoreWeave on-demand HGX H200 | 6.31 | | USD/GPU-hour | SPHERON_CW | quoted | | +| rent.b200.lambda | Lambda on-demand B200 SXM6 | 6.69 | | USD/GPU-hour | LAMBDA_PRICING | quoted | | +| rent.b200.lambda_1click_256 | Lambda 1-Click Cluster HGX B200, 256+ GPUs, two weeks to one year | 8.87 | | USD/GPU-hour | LAMBDA_PRICING | quoted | | +| rent.b200.coreweave | CoreWeave on-demand HGX B200 | 8.6 | | USD/GPU-hour | SPHERON_CW | quoted | | +| rent.gb200.coreweave | CoreWeave on-demand GB200 NVL72 (4-GPU instance $42.00/h) | 10.5 | | USD/GPU-hour | SPHERON_CW | quoted | | +| pue.uptime_mean | Uptime Institute Global Data Center Survey 2026: industry-wide per-facility average PUE | 1.52 | | x | UPTIME | quoted | 1.2 (calculator.py INPUTS pue default 1.2 (labelled assumption)) | +| pue.uptime_weighted | Uptime Institute survey 2026: capacity-weighted average PUE | 1.36 | | x | UPTIME | quoted | | +| pue.google | Google fleet-wide trailing-twelve-month PUE, 2025 and 2026 Q1-Q2 | 1.09 | | x | GOOGLE_PUE | quoted | | +| pue.msft | Microsoft fleet average PUE, FY25 (July 2024 to June 2025) | 1.17 | | x | MSFT_PUE | quoted | | +| pue.aws | AWS average global PUE, 2025 | 1.14 | | x | AWS_PUE | quoted | | +| pue.meta | Meta PUE, 'low 1.1 range' (no year, no value stated) | 1.1 | | x | DCMAG_META | approximate, from qualitative wording | | +| fac.jll_per_mw_2026 | Global average data centre construction cost, 2026 forecast | 1.13e+07 | | USD/MW | JLL_COST | quoted forecast | 2.5e+06 (presets/dgx_h100_tco.py: power_infra 1.50 USD/W + cooling_infra 1.00 USD/W (single-node pilot) plus shell 800 USD/m2 x 10 m2; calculator.py: building share of other_hardware_fraction (about 19% of GPU price, 0.40 total)) | +| fac.cw_2026_growth | Cushman & Wakefield 2026 Data Center Development Cost Guide: change in per-MW construction cost since Q4 2024 | 0.21 | | fraction | CW_IREI | quoted; dollar figures per market not reachable (paywalled) | | +| fac.bernstein_shares | Bernstein GB200-era split of about $35B per GW: GPUs 39%, networking 13%, CPUs 3%, storage 1-1.5%, mechanical and electrical about one third | | | fraction of total | INVESTING_BERNSTEIN | quoted shares | | +| fac.bernstein_vr | Bernstein latest estimate per GW, Vera Rubin architecture | 3.95e+10 | | USD/GW | REMIO_BERNSTEIN | quoted | | +| net.amcompute_h100_meta | Meta 24,576 x H100 cluster, May 2024 estimate: GPU boards, network (switches, transceivers, adapters), everything else, total | | | USD | AMCOMPUTE_CAPEX | quoted totals | | +| net.amcompute_b200_576 | 576 x B200 cluster, 2026 pricing: GPU servers, networking, storage, everything else, total | | | USD | AMCOMPUTE_CAPEX | quoted totals | | +| elec.eia2025.us_industrial | U.S. total, industrial, 2025 average retail price | 0.0851 | | USD/kWh | EIA_EPA_210 | quoted (8.51 cents/kWh) | 0.0813 (calculator.py INPUTS electricity_price; presets/economics.py us_2024_industrial_flat_power_tariff) | +| elec.eia2025.ca_industrial | California industrial, 2025 | 0.2096 | | USD/kWh | EIA_EPA_210 | quoted (20.96 cents/kWh) | 0.2153 (presets/economics.py california_2024_industrial_flat_power_tariff) | +| elec.eia2025.tx_industrial | Texas industrial, 2025 | 0.0635 | | USD/kWh | EIA_EPA_210 | quoted (6.35 cents/kWh) | | +| elec.eia2025.va_industrial | Virginia industrial, 2025 | 0.0949 | | USD/kWh | EIA_EPA_210 | quoted (9.49 cents/kWh) | | +| elec.eia2025.nm_industrial | New Mexico industrial, 2025, the lowest state value in the table | 0.0585 | | USD/kWh | EIA_EPA_210 | quoted (5.85 cents/kWh) | | +| elec.eia2025.wa_industrial | Washington industrial, 2025 | 0.07 | | USD/kWh | EIA_EPA_210 | quoted (7.00 cents/kWh) | | +| elec.de.bdew_industry | Germany industrial electricity price, small to medium industrial customers, 2026 year to date | 0.172 | | EUR/kWh | BDEW | quoted (17.2 ct/kWh); customer band not stated; 2025 value 17.6 ct is derived from a stated decrease of 0.4 ct | | +| elec.pge_e20_transmission | PG&E E-20 Option R (transmission voltage) tariff: energy charges by period and demand charge | | | USD/kWh and USD/kW-month | OPENEI_PGE | quoted tariff entries; period-to-month map not shown; riders and non-bypassable charges not itemized | | +| elec.tx_4cp | ERCOT four-coincident-peak transmission cost exposure for a continuously running load | 50 | | USD/kW-year | ENERGYMAG_4CP | estimate by LoD quoted by TheEnergyMag | | +| elec.va_gs5 | Dominion GS-5 large-load class (25 MW and up, effective 2027-01-01): minimum billing demand | | | fraction of contracted demand | WAVY_GS5 | quoted structure; no $/kW or c/kWh values in the sources reached | | +| elec.hq_data_centre | Proposed Hydro-Quebec tariff for data centres above 5 MW (not in force) | 13 | | CAD cents/kWh | DCD_HQ | quoted proposal; 'roughly double the current high-power rate' (so about 6.5 CAD cents, inferred and not stated) | | +| elec.wholesale.caiso | Mean day-ahead LMP over the e003 test period; used only to split a retail price into its wholesale-linked part | | | USD/MWh | REPO_COST_V1 | read from cost-v1.json at run time (pairs.caiso.mean_price_test) | | +| elec.wholesale.de | Mean day-ahead price over the e003 test period and the mean ECB USD per EUR rate used there | | | EUR/MWh | REPO_COST_V1 | read from cost-v1.json at run time (pairs.de.mean_price_test and pairs.de.usd_per_price_unit) | | +| xcheck.introl_100_h100_5yr | Worked 5-year TCO of a 100-H100 cluster: one-time and annual items (cross-check on cost structure; not used as an input) | | | USD | INTROL_TCO | quoted items; the article states 400 kW for 100 GPUs (4 kW per GPU, about three times an H100 server share) and its $420,000 power figure omits the PUE it states, so its power lines are not consistent with its inputs | | diff --git a/experiments/e003-heatwave/cost_audit/run_before_after.py b/experiments/e003-heatwave/cost_audit/run_before_after.py new file mode 100644 index 0000000..a56acec --- /dev/null +++ b/experiments/e003-heatwave/cost_audit/run_before_after.py @@ -0,0 +1,182 @@ +"""Before and after of the T10 cost correction, for the README example and the DGX H100 full-TCO pack. + +"Before" is computed by the code of the base commit (the commit this branch was cut from), extracted with +``git archive`` into a temporary directory and run in a separate process; "after" is the code in this +working tree. The step ladder applies the changes one at a time, cumulatively, with the working-tree +calculator, and checks that its first row equals the base-commit result exactly. + +Writes ``experiments/e003-heatwave/results/cost-audit-correction-v1.json`` (with provenance) and +``experiments/e003-heatwave/results/cost-audit-correction-v1.md``. + + PYTHONPATH=: python experiments/e003-heatwave/cost_audit/run_before_after.py +""" + +from __future__ import annotations + +import ast +import subprocess +import sys +import tempfile +from pathlib import Path +from typing import Any, Dict, List + +HERE = Path(__file__).resolve().parent +REPO = HERE.parents[2] +sys.path.insert(0, str(REPO)) + +from gpu_stack import calculator # noqa: E402 +from gpu_stack import calculator_cost_basis as basis # noqa: E402 + +OUT = HERE.parent / "results" / "cost-audit-correction-v1.json" +OUT_MD = HERE.parent / "results" / "cost-audit-correction-v1.md" +SOURCES = HERE / "sources.json" +BASE_REF = "heatwave" # the branch this one was cut from; merge-base is recorded +PACK = "pythia_70m_dgx_h100_us_2024_industrial_full_tco_assumption" +PACK_VARS = ["econ.cost.per_token", "econ.run.power_cost", "econ.run.capacity_charge_cost", "econ.run.hw_cost", "econ.run.total_cost", + "econ.job.dc_power", "econ.cluster.capex_rate"] + +CHILD = r""" +import ast, sys +from gpu_stack import calculator as c +import gpu_stack +from gpu_stack.presets import scenarios +e = c.estimate(7e9, 2e12, "H100-SXM", 1024) +out = {"file": gpu_stack.__file__, "estimate": {"electricity_cost": e.electricity_cost, "hardware_cost": e.hardware_cost, + "total_cost": e.total_cost, "cost_per_million_tokens": e.cost_per_million_tokens, "energy_mwh": e.energy_mwh, + "inputs": dict(e.inputs)}, "pack": {}} +p = getattr(scenarios, sys.argv[1]) +for v in sys.argv[2:]: + out["pack"][v] = float(p.resolve(v).value) +print(repr(out)) +""" + + +def git(*a: str) -> str: + return subprocess.run(["git", *a], cwd=REPO, capture_output=True, text=True, check=True).stdout.strip() + + +def before() -> Dict[str, Any]: + base = git("merge-base", "HEAD", BASE_REF) + with tempfile.TemporaryDirectory() as td: + arc = subprocess.run(["git", "archive", base, "gpu_stack"], cwd=REPO, capture_output=True, check=True).stdout + subprocess.run(["tar", "-x", "-C", td], input=arc, check=True) + # run from the extracted tree so that its gpu_stack is the one imported + r = subprocess.run([sys.executable, "-c", CHILD, PACK, *PACK_VARS], cwd=td, capture_output=True, text=True, check=True, + env={"PYTHONPATH": td, "PATH": "/usr/bin:/bin:/usr/local/bin"}) + res = ast.literal_eval(r.stdout.strip().splitlines()[-1]) + assert res["file"].startswith(td), res["file"] + res["base_commit"] = base + return res + + +def est(**kw: float) -> Dict[str, float]: + e = calculator.estimate(7e9, 2e12, "H100-SXM", 1024, **kw) + return {"electricity_cost": e.electricity_cost, "hardware_cost": e.hardware_cost, "total_cost": e.total_cost, + "cost_per_million_tokens": e.cost_per_million_tokens, "energy_mwh": e.energy_mwh} + + +def ladder() -> List[Dict[str, Any]]: + L = basis.LEGACY_DEFAULTS + steps: List[Dict[str, Any]] = [] + cur: Dict[str, float] = {"electricity_price": L["electricity_price"], "useful_life_years": L["useful_life_years"], + "other_hardware_fraction": L["other_hardware_fraction"], "server_power_fraction": L["server_power_fraction"], + "gpu_price": L["h100_price"]} + + def record(name: str, fac: float) -> None: + saved = basis.FACILITY_USD_PER_W + basis.FACILITY_USD_PER_W = fac + calculator.build_model.cache_clear() + try: + r = est(**cur) + finally: + basis.FACILITY_USD_PER_W = saved + calculator.build_model.cache_clear() + r["step"] = name + r["electricity_share"] = r["electricity_cost"] / r["total_cost"] + r["inputs"] = dict(cur) + r["facility_usd_per_w"] = fac + steps.append(r) + + record("base-commit inputs (facility term off, as before)", 0.0) + cur["electricity_price"] = basis.ELECTRICITY_USD_PER_KWH + record("electricity 0.0813 -> 0.0851 USD/kWh (EIA 2025 industrial)", 0.0) + cur["gpu_price"] = basis.H100_PRICE_USD + record("H100 price 30,000 -> 37,500 USD", 0.0) + cur["useful_life_years"] = basis.USEFUL_LIFE_YEARS + record("server life 4 -> 5.5 years", 0.0) + cur["other_hardware_fraction"] = basis.OTHER_HARDWARE_FRACTION + record("servers, network, storage 0.40 -> 0.49 of card price (building moved out)", 0.0) + cur["server_power_fraction"] = basis.SERVER_POWER_FRACTION + record("extra server power 0.25 -> 0.71 (measured node power)", 0.0) + record("facility 11.3 USD/W over 13 years added (= new defaults)", basis.FACILITY_USD_PER_W) + return steps + + +def main() -> None: + from heatwave.provenance import write_result + + b = before() + a = est() + a_pack = {} + from gpu_stack.presets import scenarios + + p = getattr(scenarios, PACK) + for v in PACK_VARS: + a_pack[v] = float(p.resolve(v).value) + lad = ladder() + anchor_err = abs(lad[0]["total_cost"] - b["estimate"]["total_cost"]) / b["estimate"]["total_cost"] + last_err = abs(lad[-1]["total_cost"] - a["total_cost"]) / a["total_cost"] + assert anchor_err < 1e-12 and last_err < 1e-12, (anchor_err, last_err) + be = b["estimate"] + res: Dict[str, Any] = { + "description": "README example (7e9 parameters, 2e12 tokens, 1,024 H100-SXM) and the DGX H100 full-TCO pack, before and after the T10 correction.", + "base_commit": b["base_commit"], + "readme_example": { + "before": {k: be[k] for k in ("electricity_cost", "hardware_cost", "total_cost", "cost_per_million_tokens", "energy_mwh")}, + "after": a, + "before_electricity_share": be["electricity_cost"] / be["total_cost"], + "after_electricity_share": a["electricity_cost"] / a["total_cost"], + "before_inputs": be["inputs"], + }, + "step_ladder": lad, + "ladder_check": {"first_row_vs_base_commit_rel_err": anchor_err, "last_row_vs_new_default_rel_err": last_err}, + "dgx_h100_full_tco_pack": { + "pack": PACK, "before": b["pack"], "after": a_pack, + "before_power_share_of_run_total": (b["pack"]["econ.run.power_cost"] + b["pack"]["econ.run.capacity_charge_cost"]) / b["pack"]["econ.run.total_cost"], + "after_power_share_of_run_total": (a_pack["econ.run.power_cost"] + a_pack["econ.run.capacity_charge_cost"]) / a_pack["econ.run.total_cost"], + }, + } + write_result(OUT, res, script=__file__, data_kind="public", inputs=[SOURCES], + notes="Base-commit numbers come from the code of that commit run in a separate process; the working-tree numbers from this tree. " + "Published list prices; nothing measured on a cluster.") + OUT_MD.write_text(markdown(res)) + print("wrote", OUT, OUT_MD) + + +def markdown(r: Dict[str, Any]) -> str: + ex = r["readme_example"] + L = ["# Cost correction: before and after (generated)", "", + f"Base commit {r['base_commit']}. README example: 7e9 parameters, 2e12 tokens, 1,024 H100-SXM, MFU 40%, PUE 1.2.", "", + "| quantity | before | after |", "|---|---|---|", + f"| electricity cost (USD) | {ex['before']['electricity_cost']:,.0f} | {ex['after']['electricity_cost']:,.0f} |", + f"| hardware cost (USD) | {ex['before']['hardware_cost']:,.0f} | {ex['after']['hardware_cost']:,.0f} |", + f"| total cost (USD) | {ex['before']['total_cost']:,.0f} | {ex['after']['total_cost']:,.0f} |", + f"| cost per million tokens (USD) | {ex['before']['cost_per_million_tokens']:.5f} | {ex['after']['cost_per_million_tokens']:.5f} |", + f"| energy (MWh) | {ex['before']['energy_mwh']:,.2f} | {ex['after']['energy_mwh']:,.2f} |", + f"| electricity share of total | {100 * ex['before_electricity_share']:.2f}% | {100 * ex['after_electricity_share']:.2f}% |", + "", "## Step ladder (changes applied one at a time, cumulative)", "", + "| step | electricity (USD) | hardware (USD) | total (USD) | electricity share | USD per million tokens |", "|---|---|---|---|---|---|"] + for s in r["step_ladder"]: + L.append(f"| {s['step']} | {s['electricity_cost']:,.0f} | {s['hardware_cost']:,.0f} | {s['total_cost']:,.0f} | " + f"{100 * s['electricity_share']:.2f}% | {s['cost_per_million_tokens']:.5f} |") + p = r["dgx_h100_full_tco_pack"] + L += ["", f"## DGX H100 full-TCO pack ({p['pack']}), Pythia-70M single node", "", + "| quantity | before | after |", "|---|---|---|"] + for v in PACK_VARS: + L.append(f"| {v} | {p['before'][v]:.6g} | {p['after'][v]:.6g} |") + L.append(f"| electricity (energy + demand charge) share of run total | {100 * p['before_power_share_of_run_total']:.2f}% | {100 * p['after_power_share_of_run_total']:.2f}% |") + return "\n".join(L) + "\n" + + +if __name__ == "__main__": + main() diff --git a/experiments/e003-heatwave/cost_audit/run_cost_audit.py b/experiments/e003-heatwave/cost_audit/run_cost_audit.py new file mode 100644 index 0000000..cc4d79e --- /dev/null +++ b/experiments/e003-heatwave/cost_audit/run_cost_audit.py @@ -0,0 +1,627 @@ +"""Electricity's share of cost per token: sensitivity audit (e003, T10). + +Reads the cited inputs in ``sources.json`` (next to this file), runs ``gpu_stack.calculator.estimate`` +for the README example and for one-at-a-time and grid variations, and translates the e003 timing +savings (``results/cost-v1.json``) into a change in total cost per token. Writes + + experiments/e003-heatwave/results/cost-audit-v1.json (with provenance) + experiments/e003-heatwave/results/cost-audit-v1.md (generated tables) + experiments/e003-heatwave/cost_audit/SOURCES.md (generated sources table) + +Run from anywhere: PYTHONPATH=: python experiments/e003-heatwave/cost_audit/run_cost_audit.py + +Every price, power, life and rate used here is read from ``sources.json`` (each row has a URL and +an access date) or from ``gpu_stack.calculator_cost_basis``; the only numbers typed in this file are +the grid axes (which rows to sweep) and the load factors named below, all labelled as assumptions. +Nothing is measured on a real cluster. The workload is the README example: 7e9 parameters, +2e12 tokens, 1,024 GPUs. +""" + +from __future__ import annotations + +import contextlib +import itertools +import json +import statistics +import subprocess +import sys +from dataclasses import replace +from pathlib import Path +from typing import Any, Dict, Iterator, List, Optional, Tuple + +HERE = Path(__file__).resolve().parent +REPO = HERE.parents[2] +sys.path.insert(0, str(REPO)) + +from gpu_stack import calculator # noqa: E402 +from gpu_stack import calculator_cost_basis as basis # noqa: E402 +from gpu_stack.presets.hardware import H100_SXM_BF16_DENSE_FLOPS # noqa: E402 + +SOURCES = HERE / "sources.json" +COST_V1 = HERE.parent / "results" / "cost-v1.json" +OUT = HERE.parent / "results" / "cost-audit-v1.json" +OUT_MD = HERE.parent / "results" / "cost-audit-v1.md" +SOURCES_MD = HERE / "SOURCES.md" + +PARAMS, TOKENS, N_GPUS = 7e9, 2e12, 1024 # the README example + +# Assumptions made in this file (not read from a source); each is labelled in the output. +ASSUMED_LOAD_FACTORS = (1.0, 0.7) # tariff demand charge spread over kWh; 0.7 is the e003 scenario utilization +HOURS_PER_MONTH = 8760.0 / 12.0 +MFU_RANGE = (0.30, 0.55) # the range the calculator's own help text gives for real runs (uncited there) +SHARE_LEVELS = (0.10, 0.20, 0.30, 0.50) # thresholds used to report where electricity becomes a given share +HEADLINE_LAMBDA = 0.5 +HEADLINE_KEY = "timestral_28M|f=0.3|W=24|rho=0.7|q=0.5" + + +# --------------------------------------------------------------------------- +# Sources +# --------------------------------------------------------------------------- + +SRC = json.loads(SOURCES.read_text()) +ROWS: Dict[str, Dict[str, Any]] = {r["id"]: r for r in SRC["rows"]} + + +def row(rid: str) -> Dict[str, Any]: + return ROWS[rid] + + +def val(rid: str) -> float: + v = ROWS[rid]["value"] + if v is None: + raise KeyError(f"{rid} has no value") + return float(v) + + +def lo_hi(rid: str) -> Tuple[float, float]: + r = ROWS[rid] + return float(r["low"]), float(r["high"]) + + +# --------------------------------------------------------------------------- +# e003 timing savings, read from the committed cost result +# --------------------------------------------------------------------------- + +COST = json.loads(COST_V1.read_text())["result"] + + +def timing_saving(pair: str, lam: float) -> Dict[str, float]: + c = COST["pairs"][pair]["curves"][HEADLINE_KEY] + i = COST["scenario"]["lambdas"].index(lam) + return { + "saving_electricity_frac": c["saving_pct_electricity"][i] / 100.0, + "saving_total_frac_in_cost_v1": c["saving_pct_total"][i] / 100.0, + "wholesale_mean_usd_per_kwh": COST["pairs"][pair]["mean_price_test"] * COST["pairs"][pair]["usd_per_price_unit"] / 1000.0, + } + + +TIMING = { + "caiso": {"lambda_0.5": timing_saving("caiso", 0.5), "lambda_0": timing_saving("caiso", 0.0)}, + "de": {"lambda_0.5": timing_saving("de", 0.5), "lambda_0": timing_saving("de", 0.0)}, +} +USD_PER_EUR = float(COST["pairs"]["de"]["usd_per_price_unit"]) + + +# --------------------------------------------------------------------------- +# Chips: every bundle field comes from a sources.json row +# --------------------------------------------------------------------------- + +NODE_RATED_RATIO = basis.H100_NODE_MEASURED_AVG_W / val("power.dgx_h100.system") # measured / rated, one H100 node + + +def _server_power_fraction(rated_w_per_gpu: float, tdp_w: float) -> float: + """Extra server power so a node draws the same share of its rated power as the measured H100 node does.""" + return round(NODE_RATED_RATIO * rated_w_per_gpu / (tdp_w * basis.GPU_POWER_FRACTION) - 1.0, 2) + + +def _addon_h100() -> float: + return basis.NETWORK_PER_GPU_USD + basis.OTHER_IT_PER_GPU_USD + + +_B200C = row("net.amcompute_b200_576") +_ADDON_B200 = (_B200C["network"] + _B200C["storage"] + _B200C["other"]) / _B200C["n_gpus"] + + +def build_chips() -> Dict[str, Dict[str, Any]]: + h100 = calculator.GPUS["H100-SXM"] + chips: Dict[str, Dict[str, Any]] = {} + chips["H100"] = { + "gpu": h100, "gpu_price": basis.H100_PRICE_USD, "other_hardware_fraction": basis.OTHER_HARDWARE_FRACTION, + "server_power_fraction": basis.SERVER_POWER_FRACTION, "tdp_w": 700.0, "peak_flops": H100_SXM_BF16_DENSE_FLOPS, + "price_range_ratio": (lo_hi("price.h100.gpu_sxm5")[0] / val("price.h100.gpu_sxm5"), + lo_hi("price.h100.gpu_sxm5")[1] / val("price.h100.gpu_sxm5")), + "price_basis": "H100 SXM5 card (IntuitionLabs) x (1 + servers, network, storage share from calculator_cost_basis)", + "rental_ids": ["rent.h100.lambda_sxm", "rent.h100.runpod_sxm", "rent.h100.aws_p5", "rent.h100.coreweave", + "rent.h100.spheron_ondemand"], + } + # H200: HGX system and DGX system midpoint, per GPU, plus the H100 cluster's network and other hardware per GPU. + h200_sys = (val("price.h200.sys_hgx_new") + val("price.h200.sys_dgx")) / 2.0 / 8.0 + chips["H200"] = { + "gpu": replace(h100, key="H200-SXM", label="NVIDIA H200 SXM"), "gpu_price": h200_sys, + "other_hardware_fraction": round(_addon_h100() / h200_sys, 2), + "server_power_fraction": _server_power_fraction(val("power.dgx_h100.system") / 8.0, val("power.h200.tdp")), + "tdp_w": val("power.h200.tdp"), "peak_flops": val("flops.h200.bf16_sparse") / 2.0, + "price_range_ratio": (((val("price.h200.sys_hgx_new") + lo_hi("price.h200.sys_dgx")[0]) / 2.0) / + ((val("price.h200.sys_hgx_new") + val("price.h200.sys_dgx")) / 2.0), + ((val("price.h200.sys_hgx_new") + lo_hi("price.h200.sys_dgx")[1]) / 2.0) / + ((val("price.h200.sys_hgx_new") + val("price.h200.sys_dgx")) / 2.0)), + "price_basis": "mean of HGX H200 (AMCompute) and DGX H200 (IntuitionLabs) system prices, per GPU, plus H100-cluster network and other IT per GPU", + "rental_ids": ["rent.h200.runpod", "rent.h200.coreweave"], + } + b200_sys = val("price.b200.sys_hgx") / 8.0 + chips["B200"] = { + "gpu": replace(h100, key="B200-SXM", label="NVIDIA B200 (HGX)"), "gpu_price": b200_sys, + "other_hardware_fraction": round(_ADDON_B200 / b200_sys, 2), + "server_power_fraction": _server_power_fraction(val("power.b200.dgx_system") / 8.0, val("power.b200.tdp")), + "tdp_w": val("power.b200.tdp"), "peak_flops": val("flops.b200.bf16_sparse_hgx8") / 8.0 / 2.0, + "price_range_ratio": (lo_hi("price.b200.sys_hgx")[0] / val("price.b200.sys_hgx"), + lo_hi("price.b200.sys_hgx")[1] / val("price.b200.sys_hgx")), + "price_basis": "HGX B200 8-GPU server (AMCompute) per GPU, plus 576-GPU B200 cluster network, storage and other per GPU", + "rental_ids": ["rent.b200.lambda", "rent.b200.coreweave"], + } + gb_rack = val("price.gb200.rack") / 72.0 + chips["GB200"] = { + "gpu": replace(h100, key="GB200-NVL72", label="NVIDIA GB200 (NVL72)"), "gpu_price": gb_rack, + "other_hardware_fraction": round(_ADDON_B200 / gb_rack, 2), + "server_power_fraction": _server_power_fraction(val("power.gb200.rack") / 72.0, val("power.gb200.gpu")), + "tdp_w": val("power.gb200.gpu"), "peak_flops": val("flops.gb200.bf16_dense"), + "price_range_ratio": (lo_hi("price.gb200.rack")[0] / val("price.gb200.rack"), + lo_hi("price.gb200.rack")[1] / val("price.gb200.rack")), + "price_basis": "GB200 NVL72 rack price (Wing VC) per GPU, plus 576-GPU B200 cluster network, storage and other per GPU", + "rental_ids": ["rent.gb200.coreweave"], + } + return chips + + +CHIPS = build_chips() + + +def rental_rate(chip: str, kind: str) -> float: + """USD per GPU-hour: median on-demand across listed providers; committed = on-demand x (1 - discount).""" + od = statistics.median(val(i) for i in CHIPS[chip]["rental_ids"]) + if kind == "on_demand": + return od + if kind == "committed_1y": + return od * (1.0 - val("rent.discount.committed_1y")) + if kind == "committed_3y": + return od * (1.0 - val("rent.discount.committed_3y")) + raise KeyError(kind) + + +# --------------------------------------------------------------------------- +# Electricity price cases +# --------------------------------------------------------------------------- + +_E20 = row("elec.pge_e20_transmission") +_E20_ENERGY = statistics.mean(_E20["energy_usd_per_kwh"]) + + +def tariff_built(load_factor: float) -> float: + """PG&E E-20 transmission: mean of the five listed energy rates plus the demand charge spread over kWh.""" + return _E20_ENERGY + _E20["demand_usd_per_kw_month"] / (HOURS_PER_MONTH * load_factor) + + +REGIONS: List[Dict[str, Any]] = [ + {"id": "NM", "label": "New Mexico industrial average (EIA 2025, lowest state)", "usd_per_kwh": val("elec.eia2025.nm_industrial"), + "source_ids": ["elec.eia2025.nm_industrial"]}, + {"id": "TX", "label": "Texas industrial average (EIA 2025)", "usd_per_kwh": val("elec.eia2025.tx_industrial"), + "source_ids": ["elec.eia2025.tx_industrial"]}, + {"id": "US", "label": "U.S. industrial average (EIA 2025), the calculator default", "usd_per_kwh": val("elec.eia2025.us_industrial"), + "source_ids": ["elec.eia2025.us_industrial"]}, + {"id": "VA", "label": "Virginia industrial average (EIA 2025)", "usd_per_kwh": val("elec.eia2025.va_industrial"), + "source_ids": ["elec.eia2025.va_industrial"]}, + {"id": "CA-E20", "label": f"California PG&E E-20 transmission tariff, energy + demand charge only, load factor {ASSUMED_LOAD_FACTORS[0]} " + "(riders and non-avoidable charges not itemised in the source)", "usd_per_kwh": tariff_built(ASSUMED_LOAD_FACTORS[0]), + "source_ids": ["elec.pge_e20_transmission"]}, + {"id": "DE", "label": "Germany industry average (BDEW 2026 year to date, EUR converted at the e003 mean ECB rate)", + "usd_per_kwh": val("elec.de.bdew_industry") * USD_PER_EUR, "source_ids": ["elec.de.bdew_industry"], "wholesale_pair": "de"}, + {"id": "CA", "label": "California industrial average (EIA 2025)", "usd_per_kwh": val("elec.eia2025.ca_industrial"), + "source_ids": ["elec.eia2025.ca_industrial"], "wholesale_pair": "caiso"}, +] +REGIONS_EXTRA = [ + {"id": "CA-E20-LF0.7", "label": f"California PG&E E-20 transmission, energy + demand only, load factor {ASSUMED_LOAD_FACTORS[1]}", + "usd_per_kwh": tariff_built(ASSUMED_LOAD_FACTORS[1]), "source_ids": ["elec.pge_e20_transmission"], "wholesale_pair": "caiso"}, +] +for _r in REGIONS: + if _r["id"] == "CA-E20": + _r["wholesale_pair"] = "caiso" + +PUE_CASES = [("Google fleet", val("pue.google")), ("calculator default", 1.2), ("Uptime capacity-weighted", val("pue.uptime_weighted")), + ("Uptime per-facility mean", val("pue.uptime_mean"))] +LIFE_CASES = [4.0, 5.0, 6.0] + + +# --------------------------------------------------------------------------- +# Running the calculator +# --------------------------------------------------------------------------- + + +@contextlib.contextmanager +def facility(usd_per_w: Optional[float] = None, life_years: Optional[float] = None) -> Iterator[None]: + """Temporarily change the facility constants the calculator reads from ``calculator_cost_basis``.""" + old = (basis.FACILITY_USD_PER_W, basis.FACILITY_USEFUL_LIFE_YEARS) + try: + if usd_per_w is not None: + basis.FACILITY_USD_PER_W = usd_per_w + if life_years is not None: + basis.FACILITY_USEFUL_LIFE_YEARS = life_years + calculator.build_model.cache_clear() + yield + finally: + basis.FACILITY_USD_PER_W, basis.FACILITY_USEFUL_LIFE_YEARS = old + calculator.build_model.cache_clear() + + +def run(chip: str = "H100", *, price_ratio: float = 1.0, basis_kind: str = "purchase", fac_usd_per_w: Optional[float] = None, + fac_life: Optional[float] = None, rental_override: Optional[float] = None, **kw: float) -> Dict[str, Any]: + c = CHIPS[chip] + args: Dict[str, Any] = { + "gpu_price": c["gpu_price"] * price_ratio, "other_hardware_fraction": c["other_hardware_fraction"], + "server_power_fraction": c["server_power_fraction"], "tdp_w": c["tdp_w"], "peak_flops": c["peak_flops"], + } + args.update(kw) + ctx = facility(fac_usd_per_w, fac_life) if (fac_usd_per_w is not None or fac_life is not None) else contextlib.nullcontext() + with ctx: + e = calculator.estimate(PARAMS, TOKENS, c["gpu"], N_GPUS, **args) + mtok = TOKENS / 1e6 + out: Dict[str, Any] = { + "chip": chip, "basis": basis_kind, "gpu_hours": e.gpu_hours, "training_days": e.training_days, "energy_mwh": e.energy_mwh, + "electricity_cost_usd": e.electricity_cost, "inputs": {k: e.inputs[k] for k in sorted(e.inputs)}, + } + if basis_kind == "purchase": + out["hardware_cost_usd"] = e.hardware_cost + out["total_cost_usd"] = e.total_cost + out["rental_usd_per_gpu_hour"] = None + else: + rate = rental_override if rental_override is not None else rental_rate(chip, basis_kind) + out["rental_usd_per_gpu_hour"] = rate + out["hardware_cost_usd"] = None + out["total_cost_usd"] = rate * e.gpu_hours # price paid to the lessor; electricity is inside it + out["total_usd_per_mtoken"] = out["total_cost_usd"] / mtok + out["electricity_usd_per_mtoken"] = e.electricity_cost / mtok + out["electricity_share"] = e.electricity_cost / out["total_cost_usd"] + out["electricity_usd_per_kwh"] = e.inputs["electricity_price"] + return out + + +def timing_columns(case: Dict[str, Any], region: Optional[Dict[str, Any]]) -> Dict[str, float]: + """Change in total cost per token if electricity cost falls by the e003 savings (negative = cheaper).""" + share = case["electricity_share"] + out: Dict[str, float] = {} + for pair, tag in (("caiso", "caiso"), ("de", "de")): + s = TIMING[pair]["lambda_0.5"]["saving_electricity_frac"] + out[f"delta_total_pct_if_whole_bill_follows_wholesale_{tag}"] = -100.0 * share * s + if region is not None and region.get("wholesale_pair"): + pair = region["wholesale_pair"] + s = TIMING[pair]["lambda_0.5"]["saving_electricity_frac"] + frac = min(1.0, TIMING[pair]["lambda_0.5"]["wholesale_mean_usd_per_kwh"] / case["electricity_usd_per_kwh"]) + out["wholesale_linked_fraction_of_price"] = frac + out["delta_electricity_pct_region_matched"] = -100.0 * s * frac + out["delta_total_pct_region_matched"] = -100.0 * share * s * frac + out["region_matched_pair"] = pair # type: ignore[assignment] + return out + + +# --------------------------------------------------------------------------- +# Analyses +# --------------------------------------------------------------------------- + + +def base_case() -> Dict[str, Any]: + return run("H100") + + +def defaults_by_chip() -> List[Dict[str, Any]]: + rows = [] + for chip in CHIPS: + for kind in ("purchase", "on_demand", "committed_1y", "committed_3y"): + rows.append(run(chip, basis_kind="purchase" if kind == "purchase" else kind)) + return rows + + +def one_at_a_time(chip: str) -> List[Dict[str, Any]]: + base = run(chip) + out = [] + + def add(name: str, setting: str, source: str, case: Dict[str, Any], region: Optional[Dict[str, Any]] = None) -> None: + d = {"parameter": name, "setting": setting, "source": source, "electricity_share": case["electricity_share"], + "total_usd_per_mtoken": case["total_usd_per_mtoken"], "electricity_usd_per_mtoken": case["electricity_usd_per_mtoken"], + "base_electricity_share": base["electricity_share"]} + d.update(timing_columns(case, region)) + out.append(d) + + lo_r, hi_r = CHIPS[chip]["price_range_ratio"] + for tag, r in (("low", lo_r), ("high", hi_r)): + add("chip purchase price", f"{tag} end of cited range (x{r:.3f})", CHIPS[chip]["price_basis"], run(chip, price_ratio=r)) + for y in (4.0, 6.0): + add("server useful life", f"{y} years", "operators' reported server lives, 4 to 6 years (sources.json life.*)", run(chip, useful_life_years=y)) + for f, tag in ((0.0, "no residual"), (0.45, "45% resale (used-market share at age 3, Silicon Data text)")): + add("residual value", f"{f:.0%} ({tag})", row("resid.silicondata_h100")["source"], run(chip, residual_fraction=f)) + for name, p in PUE_CASES: + if abs(p - 1.2) > 1e-12: + add("PUE", f"{p} ({name})", "pue.* rows", run(chip, pue=p)) + for reg in REGIONS + REGIONS_EXTRA: + if reg["id"] != "US": + add("electricity price", f"{reg['id']}: {reg['usd_per_kwh']:.4f} USD/kWh", ", ".join(reg["source_ids"]), + run(chip, electricity_price=reg["usd_per_kwh"]), reg) + for m in MFU_RANGE: + add("MFU", f"{m}", "calculator help text (uncited range)", run(chip, mfu=m)) + if chip == "H100": + for w in (5910.0, 7790.0): + spf = w / (8 * 700.0 * basis.GPU_POWER_FRACTION) - 1.0 + add("node power", f"{w / 1000:.2f} kW average per node (extra server power {spf:.2f})", + "arXiv 2506.14551 Table 2 (min and max of listed averages)", run(chip, server_power_fraction=spf)) + lo_o = (basis.SERVER_NON_GPU_PER_GPU_USD * 0 + (basis.HGX_H100_SYSTEM_USD - 8 * basis.H100_PRICE_USD) / 8 + basis.NETWORK_PER_GPU_USD + basis.OTHER_IT_PER_GPU_USD) / basis.H100_PRICE_USD + hi_o = ((basis.DGX_H100_SYSTEM_USD - 8 * basis.H100_PRICE_USD) / 8 + basis.NETWORK_PER_GPU_USD + basis.OTHER_IT_PER_GPU_USD) / basis.H100_PRICE_USD + add("servers, network, storage share of card price", f"{lo_o:.2f} (HGX server)", "price.h100.sys_hgx_new, net.amcompute_h100_meta", + run(chip, other_hardware_fraction=lo_o)) + add("servers, network, storage share of card price", f"{hi_o:.2f} (DGX server)", "price.h100.sys_dgx, net.amcompute_h100_meta", + run(chip, other_hardware_fraction=hi_o)) + add("facility cost", "0 USD/W (term removed)", "ablation", run(chip, fac_usd_per_w=0.0)) + add("facility cost", f"{row('fac.jll_per_mw_2026')['emerging_markets_lt'] / 1e6:.0f} USD/W (JLL emerging markets, below)", "fac.jll_per_mw_2026", + run(chip, fac_usd_per_w=row("fac.jll_per_mw_2026")["emerging_markets_lt"] / 1e6)) + add("facility cost", f"{row('fac.jll_per_mw_2026')['premium_markets_gt'] / 1e6:.0f} USD/W (JLL premium markets, above)", "fac.jll_per_mw_2026", + run(chip, fac_usd_per_w=row("fac.jll_per_mw_2026")["premium_markets_gt"] / 1e6)) + for y in (lo_hi("life.facility.heavy_equipment_amzn")[0], val("life.facility.buildings_amzn")): + add("facility life", f"{y:.0f} years", "life.facility.* rows (Amazon FY2025 10-K)", run(chip, fac_life=y)) + for kind in ("on_demand", "committed_1y", "committed_3y"): + add("cost basis", f"rental, {kind.replace('_', ' ')} (lessor's electricity as a share of the rental price)", + "rent.* rows", run(chip, basis_kind=kind)) + rids = CHIPS[chip]["rental_ids"] + rates = sorted(val(i) for i in rids) + if len(rates) > 1: + add("cost basis", f"rental on-demand, cheapest listed ({rates[0]} USD/GPU-hour)", "rent.* rows", run(chip, basis_kind="on_demand", rental_override=rates[0])) + add("cost basis", f"rental on-demand, dearest listed ({rates[-1]} USD/GPU-hour)", "rent.* rows", run(chip, basis_kind="on_demand", rental_override=rates[-1])) + return out + + +def grid() -> List[Dict[str, Any]]: + cases = [] + for chip, life, reg, (pname, pue) in itertools.product(CHIPS, LIFE_CASES, REGIONS, PUE_CASES): + c = run(chip, useful_life_years=life, pue=pue, electricity_price=reg["usd_per_kwh"]) + cases.append({"chip": chip, "basis": "purchase", "life_years": life, "region": reg["id"], "pue": pue, "pue_label": pname, + "electricity_usd_per_kwh": reg["usd_per_kwh"], "electricity_share": c["electricity_share"], + "total_usd_per_mtoken": c["total_usd_per_mtoken"], "electricity_usd_per_mtoken": c["electricity_usd_per_mtoken"], + **timing_columns(c, reg)}) + for chip, kind, reg, (pname, pue) in itertools.product(CHIPS, ("on_demand", "committed_1y", "committed_3y"), REGIONS, PUE_CASES): + c = run(chip, basis_kind=kind, pue=pue, electricity_price=reg["usd_per_kwh"]) + cases.append({"chip": chip, "basis": f"rental_{kind}", "life_years": None, "region": reg["id"], "pue": pue, "pue_label": pname, + "electricity_usd_per_kwh": reg["usd_per_kwh"], "electricity_share": c["electricity_share"], + "total_usd_per_mtoken": c["total_usd_per_mtoken"], "electricity_usd_per_mtoken": c["electricity_usd_per_mtoken"], + "rental_usd_per_gpu_hour": c["rental_usd_per_gpu_hour"], **timing_columns(c, reg)}) + return cases + + +def breakeven_prices() -> List[Dict[str, Any]]: + """Electricity price (USD/kWh) at which the share equals each level; the share is E/(E+H) and E is linear in price.""" + out = [] + for chip in CHIPS: + for life in (4.0, 5.5, 6.0): + c = run(chip, useful_life_years=life) + e_per_price = c["electricity_cost_usd"] / c["electricity_usd_per_kwh"] + h = c["hardware_cost_usd"] + rec = {"chip": chip, "life_years": life, "hardware_cost_usd": h, "energy_kwh": e_per_price} + for s in SHARE_LEVELS: + rec[f"price_for_share_{int(s * 100)}pct_usd_per_kwh"] = s / (1 - s) * h / e_per_price + out.append(rec) + return out + + +def self_checks() -> Dict[str, Any]: + """Consistency checks that must hold; their values are recorded in the result.""" + h = run("H100") + direct = calculator.estimate(PARAMS, TOKENS, "H100-SXM", N_GPUS) + checks: Dict[str, Any] = { + "h100_bundle_matches_calculator_default": abs(h["electricity_cost_usd"] - direct.electricity_cost) / direct.electricity_cost, + "h100_bundle_total_matches_calculator_default": abs(h["total_cost_usd"] - direct.total_cost) / direct.total_cost, + } + with facility(basis.FACILITY_USD_PER_W, basis.FACILITY_USEFUL_LIFE_YEARS): + again = calculator.estimate(PARAMS, TOKENS, "H100-SXM", N_GPUS) + checks["facility_context_identity"] = abs(again.total_cost - direct.total_cost) / direct.total_cost + s = TIMING["caiso"]["lambda_0.5"]["saving_electricity_frac"], TIMING["de"]["lambda_0.5"]["saving_electricity_frac"] + checks["e003_saving_caiso_pct_at_lambda_0.5"] = 100 * s[0] + checks["e003_saving_de_pct_at_lambda_0.5"] = 100 * s[1] + for k in ("h100_bundle_matches_calculator_default", "h100_bundle_total_matches_calculator_default", "facility_context_identity"): + assert checks[k] < 1e-12, (k, checks[k]) + assert round(100 * s[0], 1) == 8.6 and round(100 * s[1], 1) == 12.6, s + return checks + + +# --------------------------------------------------------------------------- +# Markdown +# --------------------------------------------------------------------------- + + +def pct(x: float, nd: int = 1) -> str: + return f"{100 * x:.{nd}f}%" + + +def sources_markdown() -> str: + lines = [ + "# Cost audit sources (generated)", + "", + "Generated by `run_cost_audit.py` from `sources.json`. Access date for web sources: " + SRC["access_date"] + ".", + "Column `gpu_stack before` is what the calculator or presets used before the T10 correction, with where.", + "", + "## Fetch notes", + "", + ] + [f"- {n}" for n in SRC["fetch_notes"]] + ["", "## Sources", "", "| id | publisher | title | published | kind | URL |", "|---|---|---|---|---|---|"] + for sid, s in SRC["sources"].items(): + lines.append(f"| {sid} | {s['publisher']} | {s['title']} | {s['published']} | {s['kind']} | {s['url']} |") + lines += ["", "## Rows", "", "| id | item | value | low-high | unit | source | basis | gpu_stack before |", "|---|---|---|---|---|---|---|---|"] + for r in SRC["rows"]: + v = "" if r.get("value") is None else f"{r['value']:g}" + rng = "" if r.get("low") is None else f"{r['low']:g} - {r['high']:g}" + cur = r.get("current") + cur_s = "" if not cur else f"{cur['value']:g} ({cur['where']})" + lines.append(f"| {r['id']} | {r['item']} | {v} | {rng} | {r['unit']} | {r['source']} | {r.get('basis', '')} | {cur_s} |") + return "\n".join(lines) + "\n" + + +def results_markdown(res: Dict[str, Any]) -> str: + L: List[str] = [] + b = res["base_case"] + L += [ + "# Electricity share of cost per token: audit results (generated)", + "", + "Generated by `experiments/e003-heatwave/cost_audit/run_cost_audit.py`. All cases call `gpu_stack.calculator.estimate` for the", + "README example (7e9 parameters, 2e12 tokens, 1,024 GPUs, MFU 40%) with the inputs in `cost_audit/sources.json`.", + "Nothing here is measured on a real cluster; prices are published list or market figures.", + "", + f"Base case (H100, calculator defaults): electricity {b['electricity_cost_usd']:,.0f} USD, hardware {b['hardware_cost_usd']:,.0f} USD, " + f"electricity share {pct(b['electricity_share'], 2)}, total {b['total_usd_per_mtoken']:.5f} USD per million tokens.", + "", + "## Defaults by chip and cost basis", + "", + "Rental basis: total = rental rate x calculator GPU-hours; electricity is the lessor's electricity cost for the same run, as a share of the rental price.", + "", + "| chip | basis | USD/GPU-hour (rental) | GPU price in model (USD) | TDP (W) | facility W/GPU | total USD/Mtoken | electricity USD/Mtoken | electricity share |", + "|---|---|---|---|---|---|---|---|---|", + ] + for c in res["defaults_by_chip"]: + ch = CHIPS[c["chip"]] + fac_w = c["energy_mwh"] * 1e6 / c["gpu_hours"] + rate = "" if c["rental_usd_per_gpu_hour"] is None else f"{c['rental_usd_per_gpu_hour']:.2f}" + L.append(f"| {c['chip']} | {c['basis']} | {rate} | {ch['gpu_price'] * (1 + ch['other_hardware_fraction']):,.0f} (all-in) | {ch['tdp_w']:.0f} | {fac_w:.0f} | " + f"{c['total_usd_per_mtoken']:.5f} | {c['electricity_usd_per_mtoken']:.5f} | {pct(c['electricity_share'], 2)} |") + for chip, rows_ in res["one_at_a_time"].items(): + L += ["", f"## One at a time, {chip} (other inputs at their defaults for that chip)", "", + "`d total (whole bill)` = change in total cost per token if electricity cost falls by the e003 saving at lambda 0.5 " + f"(CAISO {pct(TIMING['caiso']['lambda_0.5']['saving_electricity_frac'])}, DE-LU {pct(TIMING['de']['lambda_0.5']['saving_electricity_frac'])}) " + "on the whole electricity bill. `d total (matched)` = same, only on the wholesale-linked part of a retail price, with the CAISO saving " + "for California prices and the DE-LU saving for the German price.", "", + "| parameter | setting | source | electricity share | total USD/Mtoken | d total, CAISO saving (whole bill) | d total, DE-LU saving (whole bill) | d total (matched) |", + "|---|---|---|---|---|---|---|---|"] + for r in rows_: + m = r.get("delta_total_pct_region_matched") + L.append(f"| {r['parameter']} | {r['setting']} | {r['source']} | {pct(r['electricity_share'], 2)} | {r['total_usd_per_mtoken']:.5f} | " + f"{r['delta_total_pct_if_whole_bill_follows_wholesale_caiso']:.3f}% | {r['delta_total_pct_if_whole_bill_follows_wholesale_de']:.3f}% | " + f"{'' if m is None else f'{m:.3f}%'} |") + L += ["", "## Grid summary", "", + "Grid: chip (4) x server life (4, 5, 6 years) x electricity price (7 regions) x PUE (4 cited values) for purchase; " + "chip x rental basis (3) x region x PUE for rental.", "", + "| chip | basis | cases | min share | median share | max share | cases at or above 10% | at or above 20% | at or above 30% |", + "|---|---|---|---|---|---|---|---|---|"] + for g in res["grid_summary"]: + L.append(f"| {g['chip']} | {g['basis']} | {g['n']} | {pct(g['min'])} | {pct(g['median'])} | {pct(g['max'])} | {g['n_ge_10']} | {g['n_ge_20']} | {g['n_ge_30']} |") + L += ["", "## Grid: purchase basis, electricity share by region (PUE 1.2, server life 5 years)", "", + "| chip | " + " | ".join(r["id"] for r in REGIONS) + " |", "|---|" + "---|" * len(REGIONS)] + for chip in CHIPS: + cells = [] + for reg in REGIONS: + m = [g for g in res["grid"] if g["chip"] == chip and g["basis"] == "purchase" and g["life_years"] == 5.0 and g["region"] == reg["id"] and g["pue"] == 1.2][0] + cells.append(pct(m["electricity_share"], 1)) + L.append(f"| {chip} | " + " | ".join(cells) + " |") + L += ["", "Region prices used (USD/kWh): " + "; ".join(f"{r['id']} {r['usd_per_kwh']:.4f}" for r in REGIONS) + ".", ""] + L += ["## Rental basis: lessor's electricity share of the rental price, by region (PUE 1.2)", "", + "| chip | basis | " + " | ".join(r["id"] for r in REGIONS) + " |", "|---|---|" + "---|" * len(REGIONS)] + for chip in CHIPS: + for kind in ("rental_on_demand", "rental_committed_1y", "rental_committed_3y"): + cells = [] + for reg in REGIONS: + m = [g for g in res["grid"] if g["chip"] == chip and g["basis"] == kind and g["region"] == reg["id"] and g["pue"] == 1.2][0] + cells.append(pct(m["electricity_share"], 1)) + L.append(f"| {chip} | {kind} | " + " | ".join(cells) + " |") + L += ["", "## Electricity price at which the share reaches a level (purchase basis, PUE 1.2)", "", + "| chip | life (years) | hardware cost (USD) | energy (kWh) | price for 10% | price for 20% | price for 30% | price for 50% (USD/kWh) |", "|---|---|---|---|---|---|---|---|"] + for r in res["breakeven"]: + L.append(f"| {r['chip']} | {r['life_years']} | {r['hardware_cost_usd']:,.0f} | {r['energy_kwh']:,.0f} | " + f"{r['price_for_share_10pct_usd_per_kwh']:.3f} | {r['price_for_share_20pct_usd_per_kwh']:.3f} | " + f"{r['price_for_share_30pct_usd_per_kwh']:.3f} | {r['price_for_share_50pct_usd_per_kwh']:.3f} |") + x = res["crosscheck_introl"] + L += ["", "## Cost-structure cross-check (not an input)", "", + "A published worked example of a 100-H100 cluster (Introl, 2026-04-04, see `xcheck.introl_100_h100_5yr`) lists costs the calculator does not model " + "(staff, space, software, maintenance, insurance, bandwidth). Shares of its gross five-year cost (hardware and installation plus five years of annual items, before the residual credit):", "", + "| item | share of gross 5-year cost |", "|---|---|"] + for k, v in x["shares"].items(): + L.append(f"| {k} | {pct(v)} |") + L += ["", "The article's power lines are not consistent with its own inputs (see the row's basis), so this cross-check shows the kinds and rough size of omitted terms, " + "not a reference value for electricity's share. Omitted terms lower the electricity share of a complete cost per token.", ""] + L += ["", "## e003 timing savings read from cost-v1.json", "", + f"Headline policy {HEADLINE_KEY}, lambda {HEADLINE_LAMBDA}: electricity saving CAISO {pct(TIMING['caiso']['lambda_0.5']['saving_electricity_frac'], 2)} " + f"(wholesale mean {TIMING['caiso']['lambda_0.5']['wholesale_mean_usd_per_kwh'] * 1000:.2f} USD/MWh), DE-LU {pct(TIMING['de']['lambda_0.5']['saving_electricity_frac'], 2)} " + f"(wholesale mean {TIMING['de']['lambda_0.5']['wholesale_mean_usd_per_kwh'] * 1000:.2f} USD/MWh at {USD_PER_EUR:.4f} USD per EUR). " + f"At lambda 0: CAISO {pct(TIMING['caiso']['lambda_0']['saving_electricity_frac'], 2)}, DE-LU {pct(TIMING['de']['lambda_0']['saving_electricity_frac'], 2)}.", + "The savings are on wholesale day-ahead prices. A retail price has a wholesale-linked part and fixed parts (delivery, demand charges, taxes); " + "the matched columns apply the saving to the wholesale-linked part only. Demand charges would rise if shifting raises the peak load; that is not modelled.", ""] + return "\n".join(L) + "\n" + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + + +def summarize_grid(cases: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + out = [] + keys = sorted({(c["chip"], c["basis"]) for c in cases}, key=lambda k: (list(CHIPS).index(k[0]), k[1])) + for chip, bs in keys: + sh = sorted(c["electricity_share"] for c in cases if c["chip"] == chip and c["basis"] == bs) + out.append({"chip": chip, "basis": bs, "n": len(sh), "min": sh[0], "median": statistics.median(sh), "max": sh[-1], + "n_ge_10": sum(s >= 0.10 for s in sh), "n_ge_20": sum(s >= 0.20 for s in sh), "n_ge_30": sum(s >= 0.30 for s in sh)}) + return out + + +def crosscheck_introl() -> Dict[str, Any]: + r = row("xcheck.introl_100_h100_5yr") + yrs = r["years"] + items = { + "hardware": r["year0_hardware"], "installation and training": r["year0_installation_training"], + "power": yrs * r["annual_power"], "cooling": yrs * r["annual_cooling"], "space": yrs * r["annual_space"], + "network bandwidth": yrs * r["annual_network_bandwidth"], "software": yrs * r["annual_software"], + "maintenance": yrs * r["annual_maintenance"], "insurance": yrs * r["annual_insurance"], "personnel": yrs * r["annual_personnel"], + } + gross = sum(items.values()) + return {"gross_usd": gross, "items_usd": items, "shares": {k: v / gross for k, v in items.items()}, + "power_plus_cooling_share": (items["power"] + items["cooling"]) / gross} + + +def main() -> None: + from heatwave.provenance import write_result + + checks = self_checks() + g = grid() + res: Dict[str, Any] = { + "description": "Electricity's share of total cost per token for the README example across cited inputs; calculator equations only.", + "workload": {"params": PARAMS, "tokens": TOKENS, "n_gpus": N_GPUS, "gpu_default": "H100-SXM"}, + "self_checks": checks, + "assumptions_in_script": {"load_factors_for_tariff_demand_charge": list(ASSUMED_LOAD_FACTORS), "mfu_range": list(MFU_RANGE), + "node_power_ratio_measured_over_rated": NODE_RATED_RATIO, "note": "chip bundles derive server_power_fraction from this ratio"}, + "chips": {k: {kk: vv for kk, vv in v.items() if kk != "gpu"} for k, v in CHIPS.items()}, + "regions": [{k: v for k, v in r.items()} for r in REGIONS + REGIONS_EXTRA], + "e003_timing": {"policy": HEADLINE_KEY, "lambda": HEADLINE_LAMBDA, **{k: v for k, v in TIMING.items()}, "usd_per_eur": USD_PER_EUR}, + "calculator_cost_basis": { + "electricity_usd_per_kwh": basis.ELECTRICITY_USD_PER_KWH, "useful_life_years": basis.USEFUL_LIFE_YEARS, + "other_hardware_fraction": basis.OTHER_HARDWARE_FRACTION, "server_power_fraction": basis.SERVER_POWER_FRACTION, + "facility_usd_per_w": basis.FACILITY_USD_PER_W, "facility_useful_life_years": basis.FACILITY_USEFUL_LIFE_YEARS, + "h100_price_usd": basis.H100_PRICE_USD, "legacy_defaults": basis.LEGACY_DEFAULTS, + }, + "base_case": base_case(), + "defaults_by_chip": defaults_by_chip(), + "one_at_a_time": {chip: one_at_a_time(chip) for chip in ("H100", "B200")}, + "grid": g, + "grid_summary": summarize_grid(g), + "breakeven": breakeven_prices(), + "crosscheck_introl": crosscheck_introl(), + } + res["sources_table_sha256"] = __import__("hashlib").sha256(SOURCES.read_bytes()).hexdigest() + res["gpu_stack_git"] = gpu_stack_git() + SOURCES_MD.write_text(sources_markdown()) + OUT_MD.write_text(results_markdown(res)) + write_result(OUT, res, script=__file__, data_kind="public", inputs=[SOURCES, COST_V1], + notes="Published list, market and tariff figures (sources.json); modeled workload from the calculator; nothing measured on a cluster. " + "The git field is the heatwave repository; the gpu_stack state is in result.gpu_stack_git.") + print("wrote", OUT, OUT_MD, SOURCES_MD) + + +def gpu_stack_git() -> Dict[str, str]: + def git(*a: str) -> str: + return subprocess.run(["git", *a], cwd=REPO, capture_output=True, text=True, check=True).stdout.strip() + + dirty = bool(git("status", "--porcelain", "--", ".", ":(exclude)experiments/e003-heatwave/results", + ":(exclude)experiments/e003-heatwave/cost_audit/SOURCES.md")) + return {"repo": "gpu_stack", "commit": git("rev-parse", "HEAD"), "branch": git("rev-parse", "--abbrev-ref", "HEAD"), "dirty": str(dirty).lower()} + + +if __name__ == "__main__": + main() diff --git a/experiments/e003-heatwave/cost_audit/sources.json b/experiments/e003-heatwave/cost_audit/sources.json index 23d4359..c2663bc 100644 --- a/experiments/e003-heatwave/cost_audit/sources.json +++ b/experiments/e003-heatwave/cost_audit/sources.json @@ -52,7 +52,8 @@ "DCD_HQ": {"title": "Canadian utility Hydro-Quebec proposes electricity tariff for data centers", "publisher": "DatacenterDynamics", "url": "https://www.datacenterdynamics.com/en/news/canadian-utility-hydro-qu%C3%A9bec-proposes-electricity-tariff-for-data-centers/", "published": "2026-02-20", "kind": "secondary news"}, "ARXIV_NODE_POWER": {"title": "Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation", "publisher": "arXiv 2506.14551", "url": "https://arxiv.org/pdf/2506.14551", "published": "2025", "kind": "primary paper (measurements by BNL and public MLPerf Power data)"}, "LBNL_NODE_POWER": {"title": "Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System", "publisher": "Lawrence Berkeley National Laboratory", "url": "https://eta-publications.lbl.gov/sites/default/files/2026-07/single-node_power_demand_during_ai_training.pdf", "published": "2025-03-25 (paper), posted 2026-07", "kind": "primary measurement paper"}, - "REPO_COST_V1": {"title": "experiments/e003-heatwave/results/cost-v1.json (public CAISO NP15 day-ahead LMP, DE-LU day-ahead price, ECB FX; test period 2026-04-27 to 2026-10-25)", "publisher": "gpu_stack repository, e003", "url": "experiments/e003-heatwave/results/cost-v1.json", "published": "2026-10-26", "kind": "repository result built from public series"} + "REPO_COST_V1": {"title": "experiments/e003-heatwave/results/cost-v1.json (public CAISO NP15 day-ahead LMP, DE-LU day-ahead price, ECB FX; test period 2026-04-27 to 2026-10-25)", "publisher": "gpu_stack repository, e003", "url": "experiments/e003-heatwave/results/cost-v1.json", "published": "2026-10-26", "kind": "repository result built from public series"}, + "INTROL_TCO": {"title": "GPU Infrastructure TCO Model: 5-Year Cost Analysis for Enterprise AI", "publisher": "Introl", "url": "https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model", "published": "2026-04-04", "kind": "secondary; worked example of 100 H100; its own power arithmetic is inconsistent (cross-check only, not an input)"} }, "rows": [ {"id": "price.h100.gpu_sxm5", "group": "price", "chip": "H100", "item": "H100 SXM5 80GB, new, per GPU", "unit": "USD/GPU", "low": 35000, "high": 40000, "value": 37500, "basis": "midpoint of cited range", "source": "INTUITION_PRICING", "evidence": "extract: Table 2, H100 SXM5 80GB per GPU new $35,000 to $40,000, verified Q1 to Q2 2026, citing CloudZero", "current": {"value": 30000, "where": "gpu_stack/calculator.py GPUS['H100-SXM'].price_usd; presets/dgx_h100_tco.py econ.gpu.capex (2024 channel range 27,000-40,000, midpoint-ish 30,000)"}}, @@ -74,7 +75,7 @@ {"id": "power.gb200.gpu", "group": "power", "chip": "GB200", "item": "B200 GPU inside a GB200 superchip, max power", "unit": "W", "value": 1200, "basis": "quoted from secondary", "source": "KLOCKWOOD_B200", "evidence": "extract: the GB200 variant of B200 is rated at 1200 W maximum; the HGX variant is configured at 1,000 W (cites NVIDIA Blackwell Architecture Technical Brief)", "current": null}, {"id": "power.gb200.rack", "group": "power", "chip": "GB200", "item": "GB200 NVL72 rack TDP, 72 GPUs", "unit": "W", "value": 132000, "n_gpus": 72, "basis": "quoted", "source": "HPE_GB200", "evidence": "verbatim: The rack's TDP (thermal design power) is 132 kW nominal; extract: electrical design power peak about 192 kW; Supermicro lists total power 132kW (8x 1U 33kW shelves)", "current": null}, {"id": "power.dgx_h100.system", "group": "power", "chip": "H100", "item": "DGX H100 system max power (8 GPUs)", "unit": "W", "value": 10200, "basis": "quoted", "source": "REPO_HARDWARE", "evidence": "verbatim (as quoted in the repo): Table 3 lists 10.2 kW max for 200-240 V AC input", "current": {"value": 10200, "where": "presets/scenarios.py and scenarios_cited_2026.py thermal.dc.total_power (used directly as site power)"}}, - {"id": "power.h100.node_measured_avg", "group": "power", "chip": "H100", "item": "Measured average power of one 8-GPU H100 node during LLM training; median of four listed averages (7.79, 7.67, 7.73, 5.91 kW)", "unit": "W", "value": 7700, "samples_w": [7790, 7670, 7730, 5910], "peaks_w": [8420, 8450, 8730, 8760], "basis": "median of the four averages in Table 2", "source": "ARXIV_NODE_POWER", "evidence": "extract: Table 2 averages BNL Llama-13B 7.79 kW (76.4% of 10.2 kW), SMC GPT3-175B 64 nodes 7.67 kW, SMC Llama-70B 8 nodes 7.73 kW, SMC Llama-70B 64 nodes 5.91 kW; peaks 8.42-8.76 kW; 'never approached the manufacturer rated maximum of 10.2 kW'", "current": {"value": 5600, "where": "calculator.py pins node power = n_gpus x tdp_w x gpu_power_fraction (0.80) x (1 + server_power_fraction 0.25) = 700 W per GPU = 5,600 W per 8 GPUs"}}, + {"id": "power.h100.node_measured_avg", "group": "power", "chip": "H100", "item": "Measured average power of one 8-GPU H100 node during LLM training; median of the five listed averages (7.79, 7.67, 7.73, 5.91, 6.78 kW)", "unit": "W", "value": 7670, "samples_w": [7790, 7670, 7730, 5910, 6780], "peaks_w": [8420, 8450, 8730, 8760, 7320], "basis": "median of the five averages in Table 2 (re-read 2026-10-26; an earlier pass listed four and left out the 1-node SMC Llama-70B row at 6.78 kW)", "source": "ARXIV_NODE_POWER", "evidence": "extract: Table 2 averages BNL Llama-13B 7.79 kW (76.4% of 10.2 kW), SMC GPT3-175B 64 nodes 7.67 kW, SMC Llama-70B 8 nodes 7.73 kW, SMC Llama-70B 64 nodes 5.91 kW, SMC Llama-70B 1 node 6.78 kW; peaks 7.32-8.76 kW; 'never approached the manufacturer rated maximum of 10.2 kW'", "current": {"value": 5600, "where": "calculator.py pins node power = n_gpus x tdp_w x gpu_power_fraction (0.80) x (1 + server_power_fraction 0.25) = 700 W per GPU = 5,600 W per 8 GPUs"}}, {"id": "power.h100.node_measured_peak", "group": "power", "chip": "H100", "item": "Maximum observed power of one 8-GPU H100 node in a single-node measurement", "unit": "W", "value": 8400, "basis": "quoted", "source": "LBNL_NODE_POWER", "evidence": "verbatim: The maximum observed power draw was approximately 8.4 kW, 18% lower than the manufacturer-rated 10.2 kW.; median for Llama2-13b training 7.92 kW", "current": null}, {"id": "flops.b200.bf16_sparse_hgx8", "group": "flops", "chip": "B200", "item": "HGX B200 FP16/BF16 Tensor Core, 8 GPUs, with sparsity", "unit": "FLOP/s", "value": 36e15, "n_gpus": 8, "basis": "quoted; dense is half (same convention as presets/hardware.py)", "source": "NVIDIA_HGX_B200", "evidence": "verbatim: FP16/BF16 Tensor Core*: 36 PFLOPS; * With sparcity (sic)", "current": null}, @@ -101,7 +102,7 @@ {"id": "rent.h100.runpod_sxm", "group": "rental", "chip": "H100", "item": "RunPod on-demand H100 SXM", "unit": "USD/GPU-hour", "value": 3.29, "basis": "quoted", "source": "HOSTINGER_GPU", "evidence": "extract: RunPod $3.29 (SXM); Nebius $3.85; DigitalOcean $4.41; AWS $6.88 (p5.4xlarge, single H100); Azure ND H100 v5 $12.29 ($98.32 for 8 GPUs); GCP from $11.06 (A3, 8-GPU minimum); Vast.ai from $1.33; verified September 2026", "current": null}, {"id": "rent.h100.aws_p5", "group": "rental", "chip": "H100", "item": "AWS on-demand single H100 (p5.4xlarge)", "unit": "USD/GPU-hour", "value": 6.88, "basis": "quoted", "source": "HOSTINGER_GPU", "evidence": "extract: AWS $6.88 p5.4xlarge single H100 SXM5", "current": null}, {"id": "rent.h100.coreweave", "group": "rental", "chip": "H100", "item": "CoreWeave on-demand HGX H100 (8-GPU node $49.24/h)", "unit": "USD/GPU-hour", "value": 6.16, "basis": "quoted", "source": "SPHERON_CW", "evidence": "extract: H100 (HGX) 8 GPUs $49.24/h = $6.16 per GPU-hour; H200 $6.31; B200 $8.60; GB200 NVL72 $42.00 per 4-GPU instance = $10.50; CoreWeave publishes no reserved rate", "current": null}, - {"id": "rent.h100.spheron_1y_reserved", "group": "rental", "chip": "H100", "item": "One-year reserved H100 market rate, March 2026", "unit": "USD/GPU-hour", "value": 2.35, "basis": "quoted", "source": "SPHERON_RESERVE", "evidence": "extract: H100 one-year reserved rates rose roughly 40%, from about $1.70/hr (October 2025) to $2.35/hr (March 2026); Spheron on-demand H100 $2.64/hr as of 2026-10-26", "current": null}, + {"id": "rent.h100.spheron_1y_reserved", "group": "rental", "chip": "H100", "item": "One-year reserved H100 market-wide rate (Compute Exchange benchmark cited by Spheron; not a Spheron price), March 2026", "unit": "USD/GPU-hour", "value": 2.35, "basis": "quoted", "source": "SPHERON_RESERVE", "evidence": "extract: H100 one-year reserved rates rose roughly 40%, from about $1.70/hr (October 2025) to $2.35/hr (March 2026); Spheron on-demand H100 $2.64/hr as of 2026-10-26", "current": null}, {"id": "rent.h100.spheron_ondemand", "group": "rental", "chip": "H100", "item": "Spheron on-demand H100", "unit": "USD/GPU-hour", "value": 2.64, "basis": "quoted", "source": "SPHERON_RESERVE", "evidence": "extract: Spheron's on-demand H100 is $2.64/hr with no contract, as of 26 Oct 2026", "current": null}, {"id": "rent.discount.committed_1y", "group": "rental", "item": "Committed discount vs on-demand, one-year term (Compute Exchange via Spheron)", "unit": "fraction", "low": 0.40, "high": 0.50, "value": 0.45, "basis": "midpoint of range", "source": "SPHERON_RESERVE", "evidence": "extract: discount vs on-demand: 6 months 20-30%, 1 year 40-50%, 3 years 55-72%; not tied to a GPU model; 2-3 year terms typically need about 20% prepayment", "current": null}, {"id": "rent.discount.committed_3y", "group": "rental", "item": "Committed discount vs on-demand, three-year term", "unit": "fraction", "low": 0.55, "high": 0.72, "value": 0.635, "basis": "midpoint of range", "source": "SPHERON_RESERVE", "evidence": "extract: 3 years 55-72%", "current": null}, @@ -133,11 +134,12 @@ {"id": "elec.eia2025.nm_industrial", "group": "electricity", "region": "New Mexico (lowest-priced state)", "item": "New Mexico industrial, 2025, the lowest state value in the table", "unit": "USD/kWh", "value": 0.0585, "basis": "quoted (5.85 cents/kWh)", "source": "EIA_EPA_210", "evidence": "extract: six lowest industrial 2025: New Mexico 5.85, Oklahoma 6.14, Louisiana 6.18, Texas 6.35, Tennessee 6.59, Kentucky 6.78; highest: Hawaii 31.45, Rhode Island 21.40, California 20.96, Alaska 19.86", "current": null}, {"id": "elec.eia2025.wa_industrial", "group": "electricity", "region": "Washington", "item": "Washington industrial, 2025", "unit": "USD/kWh", "value": 0.0700, "basis": "quoted (7.00 cents/kWh)", "source": "EIA_EPA_210", "evidence": "extract: Table 2.10 Washington industrial 2025 7.00 (2024 6.61)", "current": null}, {"id": "elec.de.bdew_industry", "group": "electricity", "region": "Germany", "item": "Germany industrial electricity price, small to medium industrial customers, 2026 year to date", "unit": "EUR/kWh", "value": 0.172, "basis": "quoted (17.2 ct/kWh); customer band not stated; 2025 value 17.6 ct is derived from a stated decrease of 0.4 ct", "source": "BDEW", "evidence": "extract: liegt im Jahr 2026 bisher bei 17,2 ct/kWh; the page gives no breakdown for industry; medium industry is 20-70 GWh per year, large 70-150 GWh per year", "current": null}, - {"id": "elec.pge_e20_transmission", "group": "electricity", "region": "California (PG&E)", "item": "PG&E E-20 Option R (transmission voltage) tariff: energy charges by period and demand charge", "unit": "USD/kWh and USD/kW-month", "energy_usd_per_kwh": [0.10058, 0.10121, 0.10354, 0.10897, 0.10897], "demand_usd_per_kw_month": 16.55, "basis": "quoted tariff entries; period-to-month map not shown; riders and non-bypassable charges not itemized", "source": "OPENEI_PGE", "evidence": "extract: effective 2026-03-27; energy rates $0.10058, 0.10121, 0.10354, 0.10897, 0.10897 per kWh in periods 1-5; seasonal/monthly demand charge $16.55 per kW, all months", "current": null}, + {"id": "elec.pge_e20_transmission", "group": "electricity", "region": "California (PG&E)", "item": "PG&E E-20 Option R (transmission voltage) tariff: energy charges by period and demand charge", "unit": "USD/kWh and USD/kW-month", "energy_usd_per_kwh": [0.10058, 0.10121, 0.10354, 0.10897, 0.10897], "demand_usd_per_kw_month": 16.55, "basis": "quoted tariff entries; period-to-month map not shown; riders and non-avoidable charges not itemized", "source": "OPENEI_PGE", "evidence": "extract: effective 2026-03-27; energy rates $0.10058, 0.10121, 0.10354, 0.10897, 0.10897 per kWh in periods 1-5; seasonal/monthly demand charge $16.55 per kW, all months", "current": null}, {"id": "elec.tx_4cp", "group": "electricity", "region": "Texas (ERCOT)", "item": "ERCOT four-coincident-peak transmission cost exposure for a continuously running load", "unit": "USD/kW-year", "value": 50, "basis": "estimate by LoD quoted by TheEnergyMag", "source": "ENERGYMAG_4CP", "evidence": "extract: roughly $50,000 per MW per year; about $5.70/MWh for a continuously running load, before site-specific tariffs, hedges or curtailment", "current": null}, {"id": "elec.va_gs5", "group": "electricity", "region": "Virginia (Dominion)", "item": "Dominion GS-5 large-load class (25 MW and up, effective 2027-01-01): minimum billing demand", "unit": "fraction of contracted demand", "distribution_transmission_min": 0.85, "generation_min": 0.60, "basis": "quoted structure; no $/kW or c/kWh values in the sources reached", "source": "WAVY_GS5", "evidence": "extract: minimum demand charges at least 85% of contracted distribution and transmission demand and 60% of generation demand; the article gives no per-kWh or per-kW rates", "current": null}, {"id": "elec.hq_data_centre", "group": "electricity", "region": "Quebec (Hydro-Quebec)", "item": "Proposed Hydro-Quebec tariff for data centres above 5 MW (not in force)", "unit": "CAD cents/kWh", "value": 13, "basis": "quoted proposal; 'roughly double the current high-power rate' (so about 6.5 CAD cents, inferred and not stated)", "source": "DCD_HQ", "evidence": "extract: 13 CAD cents per kWh for data centers above 5 MW, filed with the Regie de l'energie, would apply in H2 2026 if approved", "current": null}, {"id": "elec.wholesale.caiso", "group": "electricity", "region": "California (CAISO NP15)", "item": "Mean day-ahead LMP over the e003 test period; used only to split a retail price into its wholesale-linked part", "unit": "USD/MWh", "value": null, "basis": "read from cost-v1.json at run time (pairs.caiso.mean_price_test)", "source": "REPO_COST_V1", "evidence": "repository result", "current": null}, - {"id": "elec.wholesale.de", "group": "electricity", "region": "Germany (DE-LU)", "item": "Mean day-ahead price over the e003 test period and the mean ECB USD per EUR rate used there", "unit": "EUR/MWh", "value": null, "basis": "read from cost-v1.json at run time (pairs.de.mean_price_test and pairs.de.usd_per_price_unit)", "source": "REPO_COST_V1", "evidence": "repository result", "current": null} + {"id": "elec.wholesale.de", "group": "electricity", "region": "Germany (DE-LU)", "item": "Mean day-ahead price over the e003 test period and the mean ECB USD per EUR rate used there", "unit": "EUR/MWh", "value": null, "basis": "read from cost-v1.json at run time (pairs.de.mean_price_test and pairs.de.usd_per_price_unit)", "source": "REPO_COST_V1", "evidence": "repository result", "current": null}, + {"id": "xcheck.introl_100_h100_5yr", "group": "crosscheck", "item": "Worked 5-year TCO of a 100-H100 cluster: one-time and annual items (cross-check on cost structure; not used as an input)", "unit": "USD", "value": null, "year0_hardware": 5300000, "year0_installation_training": 400000, "annual_power": 420000, "annual_cooling": 126000, "annual_space": 240000, "annual_network_bandwidth": 120000, "annual_software": 200000, "annual_maintenance": 265000, "annual_insurance": 53000, "annual_personnel": 900000, "years": 5, "residual_fraction": 0.30, "electricity_price_usd_per_kwh": 0.12, "pue_stated": 1.5, "basis": "quoted items; the article states 400 kW for 100 GPUs (4 kW per GPU, about three times an H100 server share) and its $420,000 power figure omits the PUE it states, so its power lines are not consistent with its inputs", "source": "INTROL_TCO", "evidence": "extract: power consumption $420,000 annually at $0.12 per kWh; cooling $126,000 (30% of power); personnel 5 FTEs $900,000; 30% residual value; 100 H100 at $3 million; total 5-year TCO $15.73M", "current": null} ] } diff --git a/gpu_stack/calculator.py b/gpu_stack/calculator.py index 0007542..2c42c9e 100644 --- a/gpu_stack/calculator.py +++ b/gpu_stack/calculator.py @@ -52,9 +52,11 @@ * NVIDIA A100 datasheet, A100 80GB SXM: "BFLOAT16 Tensor Core 312 TFLOPS | 624 TFLOPS* (*with sparsity)", "Max TDP SXM 400W", 80 GB memory. https://www.nvidia.com/en-us/data-center/a100/ -* B200 is left out on purpose: NVIDIA's public pages list the 8-GPU system - power (about 14.3 kW for DGX B200) but no per-GPU TDP, and the datasheet - could not be read. Add it when a per-GPU figure can be cited. +* B200 is not in the table. The HGX B200 summary now gives 1,000 W per GPU + (https://images.nvidia.com/aem-dam/Solutions/documents/HGX-B200-PCF-Summary.pdf, + accessed 2026-10-26), but a price and FLOP/s row would also need to be added; + the cost audit builds B200 and GB200 from cited rows inside its own script + (``experiments/e003-heatwave/cost_audit/run_cost_audit.py``). * Cost defaults come from ``gpu_stack.calculator_cost_basis`` (each with its URL and access date): U.S. industrial electricity 8.51 cents/kWh (EIA Electric Power Annual 2025, Table 2.10); H100 $37,500 and A100 $12,500 per card @@ -236,8 +238,8 @@ class InputSpec: advanced=True, lo=0.2, hi=1.0), InputSpec("server_power_fraction", "Extra server power (CPUs, fans, network)", "fraction of GPU power", basis.SERVER_POWER_FRACTION, TAG_ASSUMPTION, - "Power drawn by the rest of the server, as a share of the GPUs' own power. Default 72% makes " - "an 8-GPU H100 node draw 7.7 kW, the measured average during LLM training (the node is rated " + "Power drawn by the rest of the server, as a share of the GPUs' own power. Default 71% makes " + "an 8-GPU H100 node draw 7.7 kW (median 7.67 kW), the measured average during LLM training (the node is rated " "10.2 kW).", advanced=True, lo=0.0, hi=2.0), ] @@ -431,10 +433,10 @@ def build_model() -> CalculatorModel: **pins, } - def run(target: str): - res = resolve(target, assignments=assignments, variants=GRAPH_VARIANTS) + def run(goal: str): + res = resolve(goal, assignments=assignments, variants=GRAPH_VARIANTS) if res.missing: - raise RuntimeError(f"{target}: graph left {sorted(res.missing)} unresolved") + raise RuntimeError(f"{goal}: graph left {sorted(res.missing)} unresolved") return res wall = run("training.wallclock") @@ -657,9 +659,9 @@ def estimate( mfu : fraction of the chip's peak speed that training reaches. Default 0.40 (assumption; real runs reach 30-55%). pue : datacenter overhead, total power / computer power. Default 1.2 (assumption). - electricity_price : USD per kWh. Default 0.0813 (EIA 2024 U.S. industrial average). + electricity_price : USD per kWh. Default 0.0851 (EIA 2025 U.S. industrial average). gpu_price : USD per GPU. Default depends on the GPU (assumption). - useful_life_years : years the hardware is used before replacement. Default 4 (assumption). + useful_life_years : years the hardware is used before replacement. Default 5.5 (median of eight operators' reported server lives). more : optional ``residual_fraction``, ``other_hardware_fraction``, ``gpu_power_fraction``, ``server_power_fraction``, ``peak_flops``, ``tdp_w``. diff --git a/gpu_stack/calculator_cost_basis.py b/gpu_stack/calculator_cost_basis.py index 3a5185c..be9019c 100644 --- a/gpu_stack/calculator_cost_basis.py +++ b/gpu_stack/calculator_cost_basis.py @@ -132,9 +132,10 @@ # --------------------------------------------------------------------------- # Measured average power of one 8-GPU H100 node during LLM training: median of -# four listed averages (7.79, 7.67, 7.73 and 5.91 kW), against the 10.2 kW -# rating. https://arxiv.org/pdf/2506.14551 (Table 2) -H100_NODE_MEASURED_AVG_W: float = statistics.median([7_790.0, 7_670.0, 7_730.0, 5_910.0]) +# the five listed averages (7.79, 7.67, 7.73, 5.91 and 6.78 kW), against the +# 10.2 kW rating. https://arxiv.org/pdf/2506.14551 (Table 2, re-read 2026-10-26) +H100_NODE_MEASURED_SAMPLES_W: Tuple[float, ...] = (7_790.0, 7_670.0, 7_730.0, 5_910.0, 6_780.0) +H100_NODE_MEASURED_AVG_W: float = statistics.median(H100_NODE_MEASURED_SAMPLES_W) H100_TDP_W: float = 700.0 # The calculator's GPU load (an uncited assumption, kept at its earlier value). GPU_POWER_FRACTION: float = 0.80 @@ -159,7 +160,7 @@ __all__ = [ "A100_PRICE_RANGE_USD", "A100_PRICE_USD", "ACCESSED", "DGX_H100_SYSTEM_USD", "ELECTRICITY_USD_PER_KWH", "FACILITY_USD_PER_W", "FACILITY_USEFUL_LIFE_YEARS", - "GPUS_PER_SYSTEM", "GPU_POWER_FRACTION", "H100_NODE_MEASURED_AVG_W", + "GPUS_PER_SYSTEM", "GPU_POWER_FRACTION", "H100_NODE_MEASURED_AVG_W", "H100_NODE_MEASURED_SAMPLES_W", "H100_PRICE_RANGE_USD", "H100_PRICE_USD", "H100_TDP_W", "HGX_H100_SYSTEM_USD", "LEGACY_DEFAULTS", "META_CLUSTER_GPUS", "NETWORK_PER_GPU_USD", "OPERATOR_SERVER_LIVES", "OTHER_HARDWARE_FRACTION", "OTHER_HARDWARE_FRACTION_EXACT", "OTHER_IT_PER_GPU_USD", diff --git a/gpu_stack/cli_estimate.py b/gpu_stack/cli_estimate.py index 513e0d2..95f8b59 100644 --- a/gpu_stack/cli_estimate.py +++ b/gpu_stack/cli_estimate.py @@ -20,9 +20,9 @@ def add_estimate_parser(subparsers) -> None: p.add_argument("--gpus", type=float, default=1024, help="number of GPUs (default 1024)") p.add_argument("--mfu", type=float, help="share of peak speed reached (default 0.40; real runs 0.30-0.55)") p.add_argument("--pue", type=float, help="datacenter overhead (default 1.2)") - p.add_argument("--electricity-price", type=float, dest="electricity_price", help="USD per kWh (default 0.0813)") + p.add_argument("--electricity-price", type=float, dest="electricity_price", help="USD per kWh (default 0.0851)") p.add_argument("--gpu-price", type=float, dest="gpu_price", help="USD per GPU (default depends on the GPU)") - p.add_argument("--life-years", type=float, dest="useful_life_years", help="years of use (default 4)") + p.add_argument("--life-years", type=float, dest="useful_life_years", help="years of use (default 5.5)") p.add_argument("--explain", action="store_true", help="print what each number is made of") p.add_argument("--json", action="store_true", help="print the results as JSON") p.set_defaults(func=cmd_estimate) diff --git a/gpu_stack/presets/__init__.py b/gpu_stack/presets/__init__.py index a91dece..a8d7393 100644 --- a/gpu_stack/presets/__init__.py +++ b/gpu_stack/presets/__init__.py @@ -5,7 +5,7 @@ Concrete, named scenario presets with provenance. A preset is a bundle of variable assignments: it fills in every input a -resolver call needs to evaluate a target, so callers do not have to +resolver call needs to evaluate a goal variable, so callers do not have to rediscover each number by hand. The machinery for building presets lives in `gpu_stack.core.presets`; this package holds the actual instances, grouped by domain: hardware, workload, economics, lithography exposure optics, and diff --git a/gpu_stack/presets/scenarios.py b/gpu_stack/presets/scenarios.py index 36eced8..2917bae 100644 --- a/gpu_stack/presets/scenarios.py +++ b/gpu_stack/presets/scenarios.py @@ -307,7 +307,7 @@ def _pythia_70m_dgx_h100_us_2024_industrial_full_tco_assumption() -> Preset: def scenario_targets_for(preset_or_name: Preset | str) -> ScenarioTargetSet: - """Look up the advertised (label, variable) targets for one scenario preset.""" + """Look up the advertised (label, variable) goals for one scenario preset.""" return targets_for(SCENARIO_TARGET_SETS, preset_or_name) diff --git a/tests/test_calculator.py b/tests/test_calculator.py index 28643d5..411a59e 100644 --- a/tests/test_calculator.py +++ b/tests/test_calculator.py @@ -10,6 +10,7 @@ import pytest from gpu_stack import calculator +from gpu_stack import calculator_cost_basis as basis from gpu_stack.core.resolver import resolve ROOT = Path(__file__).resolve().parents[1] @@ -23,7 +24,7 @@ def test_default_scenario_is_sensible(): assert e.total_cost == pytest.approx(e.electricity_cost + e.hardware_cost, rel=1e-9) assert e.cost_per_million_tokens == pytest.approx(e.total_cost / 2e12 * 1e6, rel=1e-9) # energy x price = electricity cost - assert e.energy_mwh * 1000 * 0.0813 == pytest.approx(e.electricity_cost, rel=1e-9) + assert e.energy_mwh * 1000 * e.inputs["electricity_price"] == pytest.approx(e.electricity_cost, rel=1e-9) def test_matches_a_direct_numeric_graph_resolve(): @@ -36,6 +37,10 @@ def test_matches_a_direct_numeric_graph_resolve(): "econ.asset.residual_fraction": v["residual_fraction"], "econ.asset.useful_life": life_s, }).value + facility = resolve("econ.cluster.facility_capex_rate", assignments={ + "econ.cluster.facility_capex": basis.FACILITY_USD_PER_W * v["tdp_w"] * (1 + v["server_power_fraction"]), + "econ.facility.useful_life": basis.FACILITY_USEFUL_LIFE_YEARS * calculator.SECONDS_PER_YEAR, + }).value assignments = { "arch.params_total_dense": v["params"], "training.total_tokens": v["tokens"], "par.n_gpus": v["n_gpus"], "arch.tokens_per_step": 4_194_304, "training.recompute_overhead": 1, @@ -46,7 +51,7 @@ def test_matches_a_direct_numeric_graph_resolve(): "gpu.peak_flops_power_limited": v["peak_flops"] * v["mfu"], "thermal.dc.total_power": v["pue"] * v["n_gpus"] * v["tdp_w"] * v["gpu_power_fraction"] * (1 + v["server_power_fraction"]), - "econ.job.capex_rate": v["n_gpus"] * float(amort), + "econ.job.capex_rate": v["n_gpus"] * (float(amort) + float(facility)), } variants = calculator.GRAPH_VARIANTS wall = float(resolve("training.wallclock", assignments=assignments, variants=variants).value) diff --git a/tests/test_calculator_cost_basis.py b/tests/test_calculator_cost_basis.py new file mode 100644 index 0000000..629ca10 --- /dev/null +++ b/tests/test_calculator_cost_basis.py @@ -0,0 +1,137 @@ +"""The calculator's cited cost defaults agree with the audit's sources table, and the audit results are current. + +``gpu_stack.calculator_cost_basis`` holds each default with its URL; ``experiments/e003-heatwave/cost_audit/sources.json`` +is the table of every row the audit read. These tests keep the two from drifting apart and check that every row of the +table names a known source with a URL. +""" + +from __future__ import annotations + +import json +import statistics +from pathlib import Path + +import pytest + +from gpu_stack import calculator +from gpu_stack import calculator_cost_basis as basis + +ROOT = Path(__file__).resolve().parents[1] +AUDIT = ROOT / "experiments" / "e003-heatwave" / "cost_audit" +TABLE = json.loads((AUDIT / "sources.json").read_text()) +ROWS = {r["id"]: r for r in TABLE["rows"]} + + +def test_every_row_has_a_known_source_with_a_location(): + assert len(ROWS) == len(TABLE["rows"]), "row ids must be unique" + for r in TABLE["rows"]: + assert r["source"] in TABLE["sources"], r["id"] + src = TABLE["sources"][r["source"]] + assert src["url"].startswith("https://") or src["url"].startswith("experiments/"), r["id"] + assert r.get("item") and r.get("unit"), r["id"] + + +def test_every_row_has_a_value_or_says_why_not(): + for r in TABLE["rows"]: + has_number = any(r.get(k) is not None for k in ("value", "low", "high")) or any( + isinstance(v, (int, float, list)) and not isinstance(v, bool) for k, v in r.items() + if k not in ("id", "group", "unit", "basis", "source", "evidence", "current", "item", "chip", "region")) + said_why = str(r.get("basis", "")).startswith(("not found", "read from cost-v1.json")) + assert has_number or said_why, r["id"] + + +def test_h100_price_and_range_match_table(): + r = ROWS["price.h100.gpu_sxm5"] + assert basis.H100_PRICE_RANGE_USD == (r["low"], r["high"]) + assert basis.H100_PRICE_USD == r["value"] + + +def test_a100_price_matches_table(): + r = ROWS["price.a100.sxm80"] + assert basis.A100_PRICE_RANGE_USD == (r["low"], r["high"]) + assert basis.A100_PRICE_USD == r["value"] + + +def test_electricity_matches_table(): + assert basis.ELECTRICITY_USD_PER_KWH == ROWS["elec.eia2025.us_industrial"]["value"] + + +def test_operator_lives_match_table_and_median(): + ids = ["life.msft", "life.goog", "life.meta", "life.amzn", "life.orcl", "life.crwv", "life.nbis", "life.lambda"] + assert [y for _, y, _ in basis.OPERATOR_SERVER_LIVES] == [ROWS[i]["value"] for i in ids] + assert basis.USEFUL_LIFE_YEARS == statistics.median(ROWS[i]["value"] for i in ids) + assert basis.USEFUL_LIFE_RANGE_YEARS == (min(ROWS[i]["value"] for i in ids), max(ROWS[i]["value"] for i in ids)) + assert all(url.startswith("https://") for _, _, url in basis.OPERATOR_SERVER_LIVES) + + +def test_facility_matches_table(): + assert basis.FACILITY_USD_PER_W == ROWS["fac.jll_per_mw_2026"]["value"] / 1e6 + assert basis.FACILITY_USEFUL_LIFE_YEARS == ROWS["life.facility.heavy_equipment_amzn"]["value"] + + +def test_node_power_matches_table(): + r = ROWS["power.h100.node_measured_avg"] + assert list(basis.H100_NODE_MEASURED_SAMPLES_W) == r["samples_w"] + assert basis.H100_NODE_MEASURED_AVG_W == r["value"] + assert basis.H100_TDP_W == ROWS["power.h100.tdp"]["value"] + # the default makes an 8-GPU node draw the measured average (to the rounding of the default) + node_w = 8 * basis.H100_TDP_W * basis.GPU_POWER_FRACTION * (1 + basis.SERVER_POWER_FRACTION) + assert node_w == pytest.approx(basis.H100_NODE_MEASURED_AVG_W, rel=0.005) + + +def test_other_hardware_fraction_matches_table(): + meta = ROWS["net.amcompute_h100_meta"] + assert basis.NETWORK_PER_GPU_USD == meta["network"] / meta["n_gpus"] + assert basis.OTHER_IT_PER_GPU_USD == meta["other"] / meta["n_gpus"] + assert basis.DGX_H100_SYSTEM_USD == ROWS["price.h100.sys_dgx"]["value"] + assert basis.HGX_H100_SYSTEM_USD == ROWS["price.h100.sys_hgx_new"]["value"] + assert basis.OTHER_HARDWARE_FRACTION == pytest.approx(basis.OTHER_HARDWARE_FRACTION_EXACT, abs=0.005) + + +def test_calculator_uses_the_cited_defaults(): + e = calculator.estimate(7e9, 2e12, "H100-SXM", 1024) + assert e.inputs["electricity_price"] == basis.ELECTRICITY_USD_PER_KWH + assert e.inputs["useful_life_years"] == basis.USEFUL_LIFE_YEARS + assert e.inputs["other_hardware_fraction"] == basis.OTHER_HARDWARE_FRACTION + assert e.inputs["server_power_fraction"] == basis.SERVER_POWER_FRACTION + assert e.inputs["gpu_price"] == basis.H100_PRICE_USD + + +def test_facility_term_enters_hardware_cost_and_scales_with_its_life(): + base = calculator.estimate(7e9, 2e12, "H100-SXM", 1024) + h = base.hardware_cost + try: + basis.FACILITY_USD_PER_W = 0.0 + calculator.build_model.cache_clear() + without = calculator.estimate(7e9, 2e12, "H100-SXM", 1024) + finally: + basis.FACILITY_USD_PER_W = ROWS["fac.jll_per_mw_2026"]["value"] / 1e6 + calculator.build_model.cache_clear() + assert without.hardware_cost < h + assert without.electricity_cost == pytest.approx(base.electricity_cost, rel=1e-12) + # the facility slice is facility $/W x chip rating x (1 + server power) per GPU, over the facility life + per_gpu = basis.FACILITY_USD_PER_W * 700.0 * (1 + basis.SERVER_POWER_FRACTION) + gpu_seconds = base.gpu_hours * 3600.0 + expected = per_gpu / (basis.FACILITY_USEFUL_LIFE_YEARS * calculator.SECONDS_PER_YEAR) * gpu_seconds + assert h - without.hardware_cost == pytest.approx(expected, rel=1e-9) + + +def _result(name): + return json.loads((ROOT / "experiments" / "e003-heatwave" / "results" / name).read_text())["result"] + + +def test_audit_result_base_case_is_current(): + """If the calculator's defaults move, the committed audit result must be re-generated.""" + res = _result("cost-audit-v1.json") + e = calculator.estimate(7e9, 2e12, "H100-SXM", 1024) + assert res["base_case"]["electricity_cost_usd"] == pytest.approx(e.electricity_cost, rel=1e-9) + assert res["base_case"]["total_cost_usd"] == pytest.approx(e.total_cost, rel=1e-9) + after = _result("cost-audit-correction-v1.json")["readme_example"]["after"] + assert after["total_cost"] == pytest.approx(e.total_cost, rel=1e-9) + + +def test_audit_result_records_the_sources_table_hash(): + import hashlib + + res = _result("cost-audit-v1.json") + assert res["sources_table_sha256"] == hashlib.sha256((AUDIT / "sources.json").read_bytes()).hexdigest() diff --git a/tests/test_cli_resolve.py b/tests/test_cli_resolve.py index 21603e5..561d995 100644 --- a/tests/test_cli_resolve.py +++ b/tests/test_cli_resolve.py @@ -91,7 +91,7 @@ def test_resolve_missing_exposes_mixed_cost_frontier_diagnostics(): assert "econ.cost.per_token =" in out assert "missing:" in out assert "unresolved inputs:" in out - assert out.count("kind=DERIVED reason=symbolic boundary") == 3 + assert out.count("kind=DERIVED reason=symbolic boundary") == 4 assert "missing families:" not in out for variable, expected in { @@ -156,7 +156,8 @@ def test_resolve_missing_families_groups_mixed_cost_frontier(): ) in out assert ( "family=econ.cluster boundary_category=symbolic-boundary " - "primitive_boundary=False count=1 names=econ.cluster.facility_capex" + "primitive_boundary=False count=2 " + "names=econ.cluster.facility_capex, econ.cluster.facility_capex_rate" ) in out assert ( "family=econ.network boundary_category=symbolic-boundary " diff --git a/tests/test_cli_scenarios.py b/tests/test_cli_scenarios.py index e9d3e9a..b3a9781 100644 --- a/tests/test_cli_scenarios.py +++ b/tests/test_cli_scenarios.py @@ -52,7 +52,7 @@ def test_scenario_report_missing_families_groups_cost_per_token_roots(): assert rc == 0 assert "tokens_per_second: ok target=training.tokens_per_sec" in out assert "cost_per_token: issues target=econ.cost.per_token" in out - assert "missing=33" in out + assert "missing=34" in out clean_slice = out[ out.index("tokens_per_second: ok"): @@ -78,7 +78,8 @@ def test_scenario_report_missing_families_groups_cost_per_token_roots(): ) in out assert ( "family=econ.cluster boundary_category=symbolic-boundary " - "primitive_boundary=False count=1 names=econ.cluster.facility_capex" + "primitive_boundary=False count=2 " + "names=econ.cluster.facility_capex, econ.cluster.facility_capex_rate" ) in out assert ( "family=econ.node boundary_category=primitive-root " @@ -263,7 +264,7 @@ def test_scenario_audit_missing_families_groups_selected_cost_per_token(): assert "pythia_70m_dgx_h100_us_2024_industrial_power: issues" in out assert ( "cost_per_token: issues target=econ.cost.per_token " - "missing=33 violated_constraints=0" + "missing=34 violated_constraints=0" ) in out assert "unresolved inputs:" not in out assert " missing families:" in out @@ -277,7 +278,8 @@ def test_scenario_audit_missing_families_groups_selected_cost_per_token(): ) in out assert ( "family=econ.cluster boundary_category=symbolic-boundary " - "primitive_boundary=False count=1 names=econ.cluster.facility_capex" + "primitive_boundary=False count=2 " + "names=econ.cluster.facility_capex, econ.cluster.facility_capex_rate" ) in out assert ( "family=thermal.water boundary_category=primitive-root " diff --git a/tests/test_dgx_h100_tco_packs.py b/tests/test_dgx_h100_tco_packs.py index f0099b9..2bb74d7 100644 --- a/tests/test_dgx_h100_tco_packs.py +++ b/tests/test_dgx_h100_tco_packs.py @@ -138,8 +138,9 @@ def test_preset_assignments_are_all_root_inputs(self): assert non_roots == [] def test_preset_covers_all_34_missing_roots_of_original_pack(self): - # The original pack has 34 missing root inputs: the 33 it had before - # the facility got its own useful life, plus econ.facility.useful_life. + # The original pack has 34 missing variables: the 33 it had before the + # facility got its own useful life, plus econ.cluster.facility_capex_rate + # (a symbolic boundary above econ.facility.useful_life). # This assumption closure must cover all of them directly or via # upstream decomposition. original = scenarios.pythia_70m_dgx_h100_us_2024_industrial_power @@ -156,7 +157,7 @@ def test_preset_covers_all_34_missing_roots_of_original_pack(self): f"{sorted(full_result.missing)}" ) assert len(original_missing) == 34 - assert "econ.facility.useful_life" in original_missing + assert "econ.cluster.facility_capex_rate" in original_missing def test_water_latent_heat_is_physical_constant(self): # Water latent heat at 20 degC: NIST value ~2454 kJ/kg. @@ -222,14 +223,14 @@ def test_pack_source_cites_official_urls(self): assert "eia" in source.lower() or "energy information" in source.lower() def test_pack_advertised_targets_include_cost_per_token(self): - targets = dict(scenarios.scenario_targets_for(FULL_TCO_PACK)) - assert "cost_per_token" in targets - assert targets["cost_per_token"] == "econ.cost.per_token" + goals = dict(scenarios.scenario_targets_for(FULL_TCO_PACK)) + assert "cost_per_token" in goals + assert goals["cost_per_token"] == "econ.cost.per_token" def test_pack_advertised_targets_are_registered_variables(self): - targets = scenarios.scenario_targets_for(FULL_TCO_PACK) - for label, target in targets: - assert target in Registry.variables, (FULL_TCO_PACK_NAME, label, target) + goals = scenarios.scenario_targets_for(FULL_TCO_PACK) + for label, goal in goals: + assert goal in Registry.variables, (FULL_TCO_PACK_NAME, label, goal) def test_pack_resolves_cost_per_token_with_no_missing_inputs(self): result = FULL_TCO_PACK.resolve("econ.cost.per_token") @@ -263,8 +264,8 @@ def test_pack_cost_per_token_exceeds_energy_floor(self): def test_pack_resolves_all_advertised_targets_cleanly(self): failures = [] - for label, target in scenarios.scenario_targets_for(FULL_TCO_PACK): - result = FULL_TCO_PACK.resolve(target) + for label, goal in scenarios.scenario_targets_for(FULL_TCO_PACK): + result = FULL_TCO_PACK.resolve(goal) if result.missing: failures.append(f"{label}: missing {sorted(result.missing)}") continue @@ -365,8 +366,8 @@ def test_pack_is_in_scenario_target_sets(self): assert FULL_TCO_PACK_NAME in scenarios.SCENARIO_TARGET_SETS def test_pack_scenario_target_labels_are_stable(self): - targets = scenarios.scenario_targets_for(FULL_TCO_PACK) - labels = tuple(label for label, _ in targets) + goals = scenarios.scenario_targets_for(FULL_TCO_PACK) + labels = tuple(label for label, _ in goals) assert labels == ( "tokens_per_second", "job_dc_power", diff --git a/tests/test_import_registry.py b/tests/test_import_registry.py index 82b6265..d948578 100644 --- a/tests/test_import_registry.py +++ b/tests/test_import_registry.py @@ -6,7 +6,7 @@ point: the change must be seen and the snapshot updated deliberately. If a change legitimately moves the numbers, update the expectations here — never delete the assertions. The second test resets the registry to zero and -bootstraps it back, proving the full graph is reconstructible from code +bootstraps it back, proving the full graph is rebuildable from code alone. """ diff --git a/tests/test_scenario_audit_text_family_index.py b/tests/test_scenario_audit_text_family_index.py index 307492a..fca6539 100644 --- a/tests/test_scenario_audit_text_family_index.py +++ b/tests/test_scenario_audit_text_family_index.py @@ -36,11 +36,11 @@ def test_scenario_audit_missing_families_text_indexes_missing_roots_by_family(): assert "Scenario audit:" in out assert ( "pythia_70m_dgx_h100_us_2024_industrial_power: issues " - "targets=1 issues=33 sourced=True" + "targets=1 issues=34 sourced=True" ) in out assert ( "cost_per_token: issues target=econ.cost.per_token " - "missing=33 violated_constraints=0" + "missing=34 violated_constraints=0" ) in out assert "unresolved inputs:" not in out assert " missing families:" in out @@ -50,7 +50,8 @@ def test_scenario_audit_missing_families_text_indexes_missing_roots_by_family(): ) in out assert ( " family=econ.cluster boundary_category=symbolic-boundary " - "primitive_boundary=False count=1 names=econ.cluster.facility_capex" + "primitive_boundary=False count=2 " + "names=econ.cluster.facility_capex, econ.cluster.facility_capex_rate" ) in out assert ( " family=thermal.water boundary_category=primitive-root " From d5afee8821f3c5719de613d75634dded5ec34f09 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 18:00:30 -0700 Subject: [PATCH 31/52] Add cost-audit-v1 and cost-audit-correction-v1 results (sensitivity grid, one-at-a-time, e003 timing translation, before/after); regenerate sources table Public list, market and tariff figures with a modeled workload; nothing measured on a cluster. Co-Authored-By: Claude Sonnet --- .../e003-heatwave/cost_audit/SOURCES.md | 2 +- .../results/cost-audit-correction-v1.json | 202 + .../results/cost-audit-correction-v1.md | 37 + .../e003-heatwave/results/cost-audit-v1.json | 12689 ++++++++++++++++ .../e003-heatwave/results/cost-audit-v1.md | 198 + 5 files changed, 13127 insertions(+), 1 deletion(-) create mode 100644 experiments/e003-heatwave/results/cost-audit-correction-v1.json create mode 100644 experiments/e003-heatwave/results/cost-audit-correction-v1.md create mode 100644 experiments/e003-heatwave/results/cost-audit-v1.json create mode 100644 experiments/e003-heatwave/results/cost-audit-v1.md diff --git a/experiments/e003-heatwave/cost_audit/SOURCES.md b/experiments/e003-heatwave/cost_audit/SOURCES.md index 3f6d307..d65fecd 100644 --- a/experiments/e003-heatwave/cost_audit/SOURCES.md +++ b/experiments/e003-heatwave/cost_audit/SOURCES.md @@ -134,7 +134,7 @@ Column `gpu_stack before` is what the calculator or presets used before the T10 | elec.eia2025.nm_industrial | New Mexico industrial, 2025, the lowest state value in the table | 0.0585 | | USD/kWh | EIA_EPA_210 | quoted (5.85 cents/kWh) | | | elec.eia2025.wa_industrial | Washington industrial, 2025 | 0.07 | | USD/kWh | EIA_EPA_210 | quoted (7.00 cents/kWh) | | | elec.de.bdew_industry | Germany industrial electricity price, small to medium industrial customers, 2026 year to date | 0.172 | | EUR/kWh | BDEW | quoted (17.2 ct/kWh); customer band not stated; 2025 value 17.6 ct is derived from a stated decrease of 0.4 ct | | -| elec.pge_e20_transmission | PG&E E-20 Option R (transmission voltage) tariff: energy charges by period and demand charge | | | USD/kWh and USD/kW-month | OPENEI_PGE | quoted tariff entries; period-to-month map not shown; riders and non-bypassable charges not itemized | | +| elec.pge_e20_transmission | PG&E E-20 Option R (transmission voltage) tariff: energy charges by period and demand charge | | | USD/kWh and USD/kW-month | OPENEI_PGE | quoted tariff entries; period-to-month map not shown; riders and non-avoidable charges not itemized | | | elec.tx_4cp | ERCOT four-coincident-peak transmission cost exposure for a continuously running load | 50 | | USD/kW-year | ENERGYMAG_4CP | estimate by LoD quoted by TheEnergyMag | | | elec.va_gs5 | Dominion GS-5 large-load class (25 MW and up, effective 2027-01-01): minimum billing demand | | | fraction of contracted demand | WAVY_GS5 | quoted structure; no $/kW or c/kWh values in the sources reached | | | elec.hq_data_centre | Proposed Hydro-Quebec tariff for data centres above 5 MW (not in force) | 13 | | CAD cents/kWh | DCD_HQ | quoted proposal; 'roughly double the current high-power rate' (so about 6.5 CAD cents, inferred and not stated) | | diff --git a/experiments/e003-heatwave/results/cost-audit-correction-v1.json b/experiments/e003-heatwave/results/cost-audit-correction-v1.json new file mode 100644 index 0000000..b218911 --- /dev/null +++ b/experiments/e003-heatwave/results/cost-audit-correction-v1.json @@ -0,0 +1,202 @@ +{ + "provenance": { + "script": "/home/claude/wt/hw-costaudit/experiments/e003-heatwave/cost_audit/run_before_after.py", + "script_sha256": "b6df64c054078e0e16fb95128b8a5df20f566d6e821952288a073635569b9ca7", + "git": { + "commit": "2269683bc930e0cff9e49a3079c642b0bfbcc396", + "dirty": "false" + }, + "inputs": { + "/home/claude/wt/hw-costaudit/experiments/e003-heatwave/cost_audit/sources.json": "5777743e88a8c094e9369c652386e174b9350cfea52084d284d06e6d5e7a8e9f" + }, + "data_kind": "public", + "created_utc": "2026-10-27T00:58:09+00:00", + "notes": "Base-commit numbers come from the code of that commit run in a separate process; the working-tree numbers from this tree. Published list prices; nothing measured on a cluster." + }, + "result": { + "description": "README example (7e9 parameters, 2e12 tokens, 1,024 H100-SXM) and the DGX H100 full-TCO pack, before and after the T10 correction.", + "base_commit": "8b8a654651fa10cddd65dc6d7f69a21957e17646", + "readme_example": { + "before": { + "electricity_cost": 4026.3796240145543, + "hardware_cost": 67089.77354804653, + "total_cost": 71116.15317206085, + "cost_per_million_tokens": 0.035558076586030496, + "energy_mwh": 49.52496462502528 + }, + "after": { + "electricity_cost": 5765.53790175864, + "hardware_cost": 71909.51360337812, + "total_cost": 77675.05150513674, + "cost_per_million_tokens": 0.03883752575256845, + "energy_mwh": 67.75015160703458 + }, + "before_electricity_share": 0.05661694909555912, + "after_electricity_share": 0.07422638015730647, + "before_inputs": { + "params": 7000000000.0, + "tokens": 2000000000000.0, + "n_gpus": 1024.0, + "peak_flops": 989400000000000.0, + "tdp_w": 700.0, + "mfu": 0.4, + "pue": 1.2, + "electricity_price": 0.0813, + "gpu_price": 30000.0, + "useful_life_years": 4.0, + "residual_fraction": 0.05, + "other_hardware_fraction": 0.4, + "gpu_power_fraction": 0.8, + "server_power_fraction": 0.25 + } + }, + "step_ladder": [ + { + "electricity_cost": 4026.3796240145543, + "hardware_cost": 67089.77354804653, + "total_cost": 71116.15317206085, + "cost_per_million_tokens": 0.035558076586030496, + "energy_mwh": 49.52496462502528, + "step": "base-commit inputs (facility term off, as before)", + "electricity_share": 0.05661694909555912, + "inputs": { + "electricity_price": 0.0813, + "useful_life_years": 4.0, + "other_hardware_fraction": 0.4, + "server_power_fraction": 0.25, + "gpu_price": 30000.0 + }, + "facility_usd_per_w": 0.0 + }, + { + "electricity_cost": 4214.57448958965, + "hardware_cost": 67089.77354804653, + "total_cost": 71304.34803763594, + "cost_per_million_tokens": 0.035652174018818046, + "energy_mwh": 49.52496462502528, + "step": "electricity 0.0813 -> 0.0851 USD/kWh (EIA 2025 industrial)", + "electricity_share": 0.059106837178640334, + "inputs": { + "electricity_price": 0.0851, + "useful_life_years": 4.0, + "other_hardware_fraction": 0.4, + "server_power_fraction": 0.25, + "gpu_price": 30000.0 + }, + "facility_usd_per_w": 0.0 + }, + { + "electricity_cost": 4214.57448958965, + "hardware_cost": 83862.21693505815, + "total_cost": 88076.79142464751, + "cost_per_million_tokens": 0.044038395712323854, + "energy_mwh": 49.52496462502528, + "step": "H100 price 30,000 -> 37,500 USD", + "electricity_share": 0.04785113559904543, + "inputs": { + "electricity_price": 0.0851, + "useful_life_years": 4.0, + "other_hardware_fraction": 0.4, + "server_power_fraction": 0.25, + "gpu_price": 37500.0 + }, + "facility_usd_per_w": 0.0 + }, + { + "electricity_cost": 4214.57448958965, + "hardware_cost": 60990.703225496836, + "total_cost": 65205.27771508628, + "cost_per_million_tokens": 0.03260263885754321, + "energy_mwh": 49.52496462502528, + "step": "server life 4 -> 5.5 years", + "electricity_share": 0.06463548101129458, + "inputs": { + "electricity_price": 0.0851, + "useful_life_years": 5.5, + "other_hardware_fraction": 0.4, + "server_power_fraction": 0.25, + "gpu_price": 37500.0 + }, + "facility_usd_per_w": 0.0 + }, + { + "electricity_cost": 4214.57448958965, + "hardware_cost": 64911.53414713592, + "total_cost": 69126.10863672536, + "cost_per_million_tokens": 0.03456305431836275, + "energy_mwh": 49.52496462502528, + "step": "servers, network, storage 0.40 -> 0.49 of card price (building moved out)", + "electricity_share": 0.060969358361227466, + "inputs": { + "electricity_price": 0.0851, + "useful_life_years": 5.5, + "other_hardware_fraction": 0.49, + "server_power_fraction": 0.25, + "gpu_price": 37500.0 + }, + "facility_usd_per_w": 0.0 + }, + { + "electricity_cost": 5765.53790175864, + "hardware_cost": 64911.53414713592, + "total_cost": 70677.07204889435, + "cost_per_million_tokens": 0.035338536024447254, + "energy_mwh": 67.75015160703458, + "step": "extra server power 0.25 -> 0.71 (measured node power)", + "electricity_share": 0.08157578879003427, + "inputs": { + "electricity_price": 0.0851, + "useful_life_years": 5.5, + "other_hardware_fraction": 0.49, + "server_power_fraction": 0.71, + "gpu_price": 37500.0 + }, + "facility_usd_per_w": 0.0 + }, + { + "electricity_cost": 5765.53790175864, + "hardware_cost": 71909.51360337812, + "total_cost": 77675.05150513674, + "cost_per_million_tokens": 0.03883752575256845, + "energy_mwh": 67.75015160703458, + "step": "facility 11.3 USD/W over 13 years added (= new defaults)", + "electricity_share": 0.07422638015730647, + "inputs": { + "electricity_price": 0.0851, + "useful_life_years": 5.5, + "other_hardware_fraction": 0.49, + "server_power_fraction": 0.71, + "gpu_price": 37500.0 + }, + "facility_usd_per_w": 11.3 + } + ], + "ladder_check": { + "first_row_vs_base_commit_rel_err": 0.0, + "last_row_vs_new_default_rel_err": 0.0 + }, + "dgx_h100_full_tco_pack": { + "pack": "pythia_70m_dgx_h100_us_2024_industrial_full_tco_assumption", + "before": { + "econ.cost.per_token": 6.328731395033362e-10, + "econ.run.power_cost": 9.216023085751896, + "econ.run.capacity_charge_cost": 1.2595357504102633, + "econ.run.hw_cost": 122.74962854596676, + "econ.run.total_cost": 189.79405734656515, + "econ.job.dc_power": 10200.0, + "econ.cluster.capex_rate": 0.002761260362004715 + }, + "after": { + "econ.cost.per_token": 6.005001755667493e-10, + "econ.run.power_cost": 11.059227702902275, + "econ.run.capacity_charge_cost": 0.0, + "econ.run.hw_cost": 112.45754295185426, + "econ.run.total_cost": 180.0856406191928, + "econ.job.dc_power": 12240.0, + "econ.cluster.capex_rate": 0.002529739270413466 + }, + "before_power_share_of_run_total": 0.05519434582208084, + "after_power_share_of_run_total": 0.06141093573522612 + } + } +} diff --git a/experiments/e003-heatwave/results/cost-audit-correction-v1.md b/experiments/e003-heatwave/results/cost-audit-correction-v1.md new file mode 100644 index 0000000..eefbbbb --- /dev/null +++ b/experiments/e003-heatwave/results/cost-audit-correction-v1.md @@ -0,0 +1,37 @@ +# Cost correction: before and after (generated) + +Base commit 8b8a654651fa10cddd65dc6d7f69a21957e17646. README example: 7e9 parameters, 2e12 tokens, 1,024 H100-SXM, MFU 40%, PUE 1.2. + +| quantity | before | after | +|---|---|---| +| electricity cost (USD) | 4,026 | 5,766 | +| hardware cost (USD) | 67,090 | 71,910 | +| total cost (USD) | 71,116 | 77,675 | +| cost per million tokens (USD) | 0.03556 | 0.03884 | +| energy (MWh) | 49.52 | 67.75 | +| electricity share of total | 5.66% | 7.42% | + +## Step ladder (changes applied one at a time, cumulative) + +| step | electricity (USD) | hardware (USD) | total (USD) | electricity share | USD per million tokens | +|---|---|---|---|---|---| +| base-commit inputs (facility term off, as before) | 4,026 | 67,090 | 71,116 | 5.66% | 0.03556 | +| electricity 0.0813 -> 0.0851 USD/kWh (EIA 2025 industrial) | 4,215 | 67,090 | 71,304 | 5.91% | 0.03565 | +| H100 price 30,000 -> 37,500 USD | 4,215 | 83,862 | 88,077 | 4.79% | 0.04404 | +| server life 4 -> 5.5 years | 4,215 | 60,991 | 65,205 | 6.46% | 0.03260 | +| servers, network, storage 0.40 -> 0.49 of card price (building moved out) | 4,215 | 64,912 | 69,126 | 6.10% | 0.03456 | +| extra server power 0.25 -> 0.71 (measured node power) | 5,766 | 64,912 | 70,677 | 8.16% | 0.03534 | +| facility 11.3 USD/W over 13 years added (= new defaults) | 5,766 | 71,910 | 77,675 | 7.42% | 0.03884 | + +## DGX H100 full-TCO pack (pythia_70m_dgx_h100_us_2024_industrial_full_tco_assumption), Pythia-70M single node + +| quantity | before | after | +|---|---|---| +| econ.cost.per_token | 6.32873e-10 | 6.005e-10 | +| econ.run.power_cost | 9.21602 | 11.0592 | +| econ.run.capacity_charge_cost | 1.25954 | 0 | +| econ.run.hw_cost | 122.75 | 112.458 | +| econ.run.total_cost | 189.794 | 180.086 | +| econ.job.dc_power | 10200 | 12240 | +| econ.cluster.capex_rate | 0.00276126 | 0.00252974 | +| electricity (energy + demand charge) share of run total | 5.52% | 6.14% | diff --git a/experiments/e003-heatwave/results/cost-audit-v1.json b/experiments/e003-heatwave/results/cost-audit-v1.json new file mode 100644 index 0000000..be74049 --- /dev/null +++ b/experiments/e003-heatwave/results/cost-audit-v1.json @@ -0,0 +1,12689 @@ +{ + "provenance": { + "script": "/home/claude/wt/hw-costaudit/experiments/e003-heatwave/cost_audit/run_cost_audit.py", + "script_sha256": "91f1ff4fb8787812b48e2ec4da17d15db12ea8262466fe84700538ffc10846e5", + "git": { + "commit": "2269683bc930e0cff9e49a3079c642b0bfbcc396", + "dirty": "false" + }, + "inputs": { + "/home/claude/wt/hw-costaudit/experiments/e003-heatwave/cost_audit/sources.json": "5777743e88a8c094e9369c652386e174b9350cfea52084d284d06e6d5e7a8e9f", + "/home/claude/wt/hw-costaudit/experiments/e003-heatwave/results/cost-v1.json": "d2215883712be41cc169b548a8daec8c4e27cbf6d2ae601e056c1b05975f44dd" + }, + "data_kind": "public", + "created_utc": "2026-10-27T00:57:58+00:00", + "notes": "Published list, market and tariff figures (sources.json); modeled workload from the calculator; nothing measured on a cluster. The git field is the heatwave repository; the gpu_stack state is in result.gpu_stack_git." + }, + "result": { + "description": "Electricity's share of total cost per token for the README example across cited inputs; calculator equations only.", + "workload": { + "params": 7000000000.0, + "tokens": 2000000000000.0, + "n_gpus": 1024, + "gpu_default": "H100-SXM" + }, + "self_checks": { + "h100_bundle_matches_calculator_default": 0.0, + "h100_bundle_total_matches_calculator_default": 0.0, + "facility_context_identity": 0.0, + "e003_saving_caiso_pct_at_lambda_0.5": 8.605878085822122, + "e003_saving_de_pct_at_lambda_0.5": 12.618851109152851 + }, + "assumptions_in_script": { + "load_factors_for_tariff_demand_charge": [ + 1.0, + 0.7 + ], + "mfu_range": [ + 0.3, + 0.55 + ], + "node_power_ratio_measured_over_rated": 0.7519607843137255, + "note": "chip bundles derive server_power_fraction from this ratio" + }, + "chips": { + "H100": { + "gpu_price": 37500.0, + "other_hardware_fraction": 0.49, + "server_power_fraction": 0.71, + "tdp_w": 700.0, + "peak_flops": 989400000000000.0, + "price_range_ratio": [ + 0.9333333333333333, + 1.0666666666666667 + ], + "price_basis": "H100 SXM5 card (IntuitionLabs) x (1 + servers, network, storage share from calculator_cost_basis)", + "rental_ids": [ + "rent.h100.lambda_sxm", + "rent.h100.runpod_sxm", + "rent.h100.aws_p5", + "rent.h100.coreweave", + "rent.h100.spheron_ondemand" + ] + }, + "H200": { + "gpu_price": 49375.0, + "other_hardware_fraction": 0.22, + "server_power_fraction": 0.71, + "tdp_w": 700.0, + "peak_flops": 989500000000000.0, + "price_range_ratio": [ + 0.9367088607594937, + 1.0632911392405062 + ], + "price_basis": "mean of HGX H200 (AMCompute) and DGX H200 (IntuitionLabs) system prices, per GPU, plus H100-cluster network and other IT per GPU", + "rental_ids": [ + "rent.h200.runpod", + "rent.h200.coreweave" + ] + }, + "B200": { + "gpu_price": 51875.0, + "other_hardware_fraction": 0.36, + "server_power_fraction": 0.68, + "tdp_w": 1000.0, + "peak_flops": 2250000000000000.0, + "price_range_ratio": [ + 0.8674698795180723, + 1.1325301204819278 + ], + "price_basis": "HGX B200 8-GPU server (AMCompute) per GPU, plus 576-GPU B200 cluster network, storage and other per GPU", + "rental_ids": [ + "rent.b200.lambda", + "rent.b200.coreweave" + ] + }, + "GB200": { + "gpu_price": 41666.666666666664, + "other_hardware_fraction": 0.45, + "server_power_fraction": 0.44, + "tdp_w": 1200.0, + "peak_flops": 2500000000000000.0, + "price_range_ratio": [ + 0.9333333333333333, + 1.1333333333333333 + ], + "price_basis": "GB200 NVL72 rack price (Wing VC) per GPU, plus 576-GPU B200 cluster network, storage and other per GPU", + "rental_ids": [ + "rent.gb200.coreweave" + ] + } + }, + "regions": [ + { + "id": "NM", + "label": "New Mexico industrial average (EIA 2025, lowest state)", + "usd_per_kwh": 0.0585, + "source_ids": [ + "elec.eia2025.nm_industrial" + ] + }, + { + "id": "TX", + "label": "Texas industrial average (EIA 2025)", + "usd_per_kwh": 0.0635, + "source_ids": [ + "elec.eia2025.tx_industrial" + ] + }, + { + "id": "US", + "label": "U.S. industrial average (EIA 2025), the calculator default", + "usd_per_kwh": 0.0851, + "source_ids": [ + "elec.eia2025.us_industrial" + ] + }, + { + "id": "VA", + "label": "Virginia industrial average (EIA 2025)", + "usd_per_kwh": 0.0949, + "source_ids": [ + "elec.eia2025.va_industrial" + ] + }, + { + "id": "CA-E20", + "label": "California PG&E E-20 transmission tariff, energy + demand charge only, load factor 1.0 (riders and non-avoidable charges not itemised in the source)", + "usd_per_kwh": 0.12732523287671232, + "source_ids": [ + "elec.pge_e20_transmission" + ], + "wholesale_pair": "caiso" + }, + { + "id": "DE", + "label": "Germany industry average (BDEW 2026 year to date, EUR converted at the e003 mean ECB rate)", + "usd_per_kwh": 0.1978276, + "source_ids": [ + "elec.de.bdew_industry" + ], + "wholesale_pair": "de" + }, + { + "id": "CA", + "label": "California industrial average (EIA 2025)", + "usd_per_kwh": 0.2096, + "source_ids": [ + "elec.eia2025.ca_industrial" + ], + "wholesale_pair": "caiso" + }, + { + "id": "CA-E20-LF0.7", + "label": "California PG&E E-20 transmission, energy + demand only, load factor 0.7", + "usd_per_kwh": 0.13704147553816048, + "source_ids": [ + "elec.pge_e20_transmission" + ], + "wholesale_pair": "caiso" + } + ], + "e003_timing": { + "policy": "timestral_28M|f=0.3|W=24|rho=0.7|q=0.5", + "lambda": 0.5, + "caiso": { + "lambda_0.5": { + "saving_electricity_frac": 0.08605878085822122, + "saving_total_frac_in_cost_v1": 0.002179201513987245, + "wholesale_mean_usd_per_kwh": 0.035194378479853475 + }, + "lambda_0": { + "saving_electricity_frac": 0.08088634802123192, + "saving_total_frac_in_cost_v1": 0.0020482239036033125, + "wholesale_mean_usd_per_kwh": 0.035194378479853475 + } + }, + "de": { + "lambda_0.5": { + "saving_electricity_frac": 0.1261885110915285, + "saving_total_frac_in_cost_v1": 0.011770845499954297, + "wholesale_mean_usd_per_kwh": 0.1393627567260281 + }, + "lambda_0": { + "saving_electricity_frac": 0.121169896760223, + "saving_total_frac_in_cost_v1": 0.011302709903403787, + "wholesale_mean_usd_per_kwh": 0.1393627567260281 + } + }, + "usd_per_eur": 1.1501604651162791 + }, + "calculator_cost_basis": { + "electricity_usd_per_kwh": 0.0851, + "useful_life_years": 5.5, + "other_hardware_fraction": 0.49, + "server_power_fraction": 0.71, + "facility_usd_per_w": 11.3, + "facility_useful_life_years": 13.0, + "h100_price_usd": 37500.0, + "legacy_defaults": { + "electricity_price": 0.0813, + "useful_life_years": 4.0, + "other_hardware_fraction": 0.4, + "server_power_fraction": 0.25, + "h100_price": 30000.0, + "a100_price": 15000.0 + } + }, + "base_case": { + "chip": "H100", + "basis": "purchase", + "gpu_hours": 58958.291220268184, + "training_days": 2.3990190112413807, + "energy_mwh": 67.75015160703458, + "electricity_cost_usd": 5765.53790175864, + "inputs": { + "electricity_price": 0.0851, + "gpu_power_fraction": 0.8, + "gpu_price": 37500.0, + "mfu": 0.4, + "n_gpus": 1024.0, + "other_hardware_fraction": 0.49, + "params": 7000000000.0, + "peak_flops": 989400000000000.0, + "pue": 1.2, + "residual_fraction": 0.05, + "server_power_fraction": 0.71, + "tdp_w": 700.0, + "tokens": 2000000000000.0, + "useful_life_years": 5.5 + }, + "hardware_cost_usd": 71909.51360337812, + "total_cost_usd": 77675.05150513674, + "rental_usd_per_gpu_hour": null, + "total_usd_per_mtoken": 0.03883752575256837, + "electricity_usd_per_mtoken": 0.00288276895087932, + "electricity_share": 0.07422638015730647, + "electricity_usd_per_kwh": 0.0851 + }, + "defaults_by_chip": [ + { + "chip": "H100", + "basis": "purchase", + "gpu_hours": 58958.291220268184, + "training_days": 2.3990190112413807, + "energy_mwh": 67.75015160703458, + "electricity_cost_usd": 5765.53790175864, + "inputs": { + "electricity_price": 0.0851, + "gpu_power_fraction": 0.8, + "gpu_price": 37500.0, + "mfu": 0.4, + "n_gpus": 1024.0, + "other_hardware_fraction": 0.49, + "params": 7000000000.0, + "peak_flops": 989400000000000.0, + "pue": 1.2, + "residual_fraction": 0.05, + "server_power_fraction": 0.71, + "tdp_w": 700.0, + "tokens": 2000000000000.0, + "useful_life_years": 5.5 + }, + "hardware_cost_usd": 71909.51360337812, + "total_cost_usd": 77675.05150513674, + "rental_usd_per_gpu_hour": null, + "total_usd_per_mtoken": 0.03883752575256837, + "electricity_usd_per_mtoken": 0.00288276895087932, + "electricity_share": 0.07422638015730647, + "electricity_usd_per_kwh": 0.0851 + }, + { + "chip": "H100", + "basis": "on_demand", + "gpu_hours": 58958.291220268184, + "training_days": 2.3990190112413807, + "energy_mwh": 67.75015160703458, + "electricity_cost_usd": 5765.53790175864, + "inputs": { + "electricity_price": 0.0851, + "gpu_power_fraction": 0.8, + "gpu_price": 37500.0, + "mfu": 0.4, + "n_gpus": 1024.0, + "other_hardware_fraction": 0.49, + "params": 7000000000.0, + "peak_flops": 989400000000000.0, + "pue": 1.2, + 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"basis": "rental_committed_1y", + "n": 28, + "min": 0.020381749212200494, + "median": 0.041299851834235096, + "max": 0.1018342702895535, + "n_ge_10": 1, + "n_ge_20": 0, + "n_ge_30": 0 + }, + { + "chip": "B200", + "basis": "rental_committed_3y", + "n": 28, + "min": 0.030712224840302112, + "median": 0.0622326534488474, + "max": 0.15344890043631346, + "n_ge_10": 8, + "n_ge_20": 0, + "n_ge_30": 0 + }, + { + "chip": "B200", + "basis": "rental_on_demand", + "n": 28, + "min": 0.011209962066710271, + "median": 0.022714918508829302, + "max": 0.05600884865925442, + "n_ge_10": 0, + "n_ge_20": 0, + "n_ge_30": 0 + }, + { + "chip": "GB200", + "basis": "purchase", + "n": 84, + "min": 0.046482538598705, + "median": 0.10605952618384515, + "max": 0.2586109710150251, + "n_ge_10": 45, + "n_ge_20": 11, + "n_ge_30": 0 + }, + { + "chip": "GB200", + "basis": "rental_committed_1y", + "n": 28, + "min": 0.015263850389610393, + "median": 0.030929374753246752, + "max": 0.0762634771948052, + "n_ge_10": 0, + "n_ge_20": 0, + "n_ge_30": 0 + }, + { + "chip": "GB200", + "basis": "rental_committed_3y", + "n": 28, + "min": 0.023000322504892375, + "median": 0.046605907162426616, + "max": 0.11491756837573386, + "n_ge_10": 3, + "n_ge_20": 0, + "n_ge_30": 0 + }, + { + "chip": "GB200", + "basis": "rental_on_demand", + "n": 28, + "min": 0.008395117714285718, + "median": 0.017011156114285712, + "max": 0.04194491245714286, + "n_ge_10": 0, + "n_ge_20": 0, + "n_ge_30": 0 + } + ], + "breakeven": [ + { + "chip": "H100", + "life_years": 4.0, + "hardware_cost_usd": 96251.33890855401, + "energy_kwh": 67750.15160703455, + "price_for_share_10pct_usd_per_kwh": 0.15785342111250905, + "price_for_share_20pct_usd_per_kwh": 0.3551701975031454, + "price_for_share_30pct_usd_per_kwh": 0.6088631957196777, + "price_for_share_50pct_usd_per_kwh": 1.4206807900125815 + }, + { + "chip": "H100", + "life_years": 5.5, + "hardware_cost_usd": 71909.51360337812, + "energy_kwh": 67750.15160703455, + "price_for_share_10pct_usd_per_kwh": 0.11793251773478393, + "price_for_share_20pct_usd_per_kwh": 0.2653481649032638, + "price_for_share_30pct_usd_per_kwh": 0.45488256840559516, + "price_for_share_50pct_usd_per_kwh": 1.0613926596130552 + }, + { + "chip": "H100", + "life_years": 6.0, + "hardware_cost_usd": 66500.21909111681, + "energy_kwh": 67750.15160703455, + "price_for_share_10pct_usd_per_kwh": 0.10906120587306725, + "price_for_share_20pct_usd_per_kwh": 0.24538771321440128, + "price_for_share_30pct_usd_per_kwh": 0.420664651224688, + "price_for_share_50pct_usd_per_kwh": 0.9815508528576051 + }, + { + "chip": "H200", + "life_years": 4.0, + "hardware_cost_usd": 103209.4582720286, + "energy_kwh": 67743.30469934309, + "price_for_share_10pct_usd_per_kwh": 0.16928193327261992, + "price_for_share_20pct_usd_per_kwh": 0.38088434986339476, + "price_for_share_30pct_usd_per_kwh": 0.6529445997658196, + "price_for_share_50pct_usd_per_kwh": 1.523537399453579 + }, + { + "chip": "H200", + "life_years": 5.5, + "hardware_cost_usd": 76969.77117032451, + "energy_kwh": 67743.30469934309, + "price_for_share_10pct_usd_per_kwh": 0.1262441629421373, + "price_for_share_20pct_usd_per_kwh": 0.2840493666198089, + "price_for_share_30pct_usd_per_kwh": 0.48694177134824385, + "price_for_share_50pct_usd_per_kwh": 1.1361974664792356 + }, + { + "chip": "H200", + "life_years": 6.0, + "hardware_cost_usd": 71138.72959216805, + "energy_kwh": 67743.30469934309, + "price_for_share_10pct_usd_per_kwh": 0.11668021397980782, + "price_for_share_20pct_usd_per_kwh": 0.2625304814545676, + "price_for_share_30pct_usd_per_kwh": 0.4500522539221159, + "price_for_share_50pct_usd_per_kwh": 1.0501219258182704 + }, + { + "chip": "B200", + "life_years": 4.0, + "hardware_cost_usd": 53874.6203117067, + "energy_kwh": 41813.33333333333, + "price_for_share_10pct_usd_per_kwh": 0.1431617248929281, + "price_for_share_20pct_usd_per_kwh": 0.3221138810090882, + "price_for_share_30pct_usd_per_kwh": 0.5521952245870084, + "price_for_share_50pct_usd_per_kwh": 1.2884555240363529 + }, + { + "chip": "B200", + "life_years": 5.5, + "hardware_cost_usd": 40359.434803879194, + "energy_kwh": 41813.33333333333, + "price_for_share_10pct_usd_per_kwh": 0.10724764775690689, + "price_for_share_20pct_usd_per_kwh": 0.2413072074530405, + "price_for_share_30pct_usd_per_kwh": 0.41366949849092655, + "price_for_share_50pct_usd_per_kwh": 0.965228829812162 + }, + { + "chip": "B200", + "life_years": 6.0, + "hardware_cost_usd": 37356.06024658419, + "energy_kwh": 41813.33333333333, + "price_for_share_10pct_usd_per_kwh": 0.09926674172667993, + "price_for_share_20pct_usd_per_kwh": 0.22335016888502984, + "price_for_share_30pct_usd_per_kwh": 0.3828860038029083, + "price_for_share_50pct_usd_per_kwh": 0.8934006755401194 + }, + { + "chip": "GB200", + "life_years": 4.0, + "hardware_cost_usd": 42192.15290818638, + "energy_kwh": 38707.19999999999, + "price_for_share_10pct_usd_per_kwh": 0.1211148569206372, + "price_for_share_20pct_usd_per_kwh": 0.27250842807143366, + "price_for_share_30pct_usd_per_kwh": 0.46715730526531496, + "price_for_share_50pct_usd_per_kwh": 1.0900337122857346 + }, + { + "chip": "GB200", + "life_years": 5.5, + "hardware_cost_usd": 31775.59426909183, + "energy_kwh": 38707.19999999999, + "price_for_share_10pct_usd_per_kwh": 0.09121356195887713, + "price_for_share_20pct_usd_per_kwh": 0.20523051440747353, + "price_for_share_30pct_usd_per_kwh": 0.35182373898424035, + "price_for_share_50pct_usd_per_kwh": 0.8209220576298941 + }, + { + "chip": "GB200", + "life_years": 6.0, + "hardware_cost_usd": 29460.80346040415, + "energy_kwh": 38707.19999999999, + "price_for_share_10pct_usd_per_kwh": 0.08456882974515266, + "price_for_share_20pct_usd_per_kwh": 0.1902798669265935, + "price_for_share_30pct_usd_per_kwh": 0.326194057588446, + "price_for_share_50pct_usd_per_kwh": 0.761119467706374 + } + ], + "crosscheck_introl": { + "gross_usd": 17320000, + "items_usd": { + "hardware": 5300000, + "installation and training": 400000, + "power": 2100000, + "cooling": 630000, + "space": 1200000, + "network bandwidth": 600000, + "software": 1000000, + "maintenance": 1325000, + "insurance": 265000, + "personnel": 4500000 + }, + "shares": { + "hardware": 0.30600461893764436, + "installation and training": 0.023094688221709007, + "power": 0.12124711316397228, + "cooling": 0.036374133949191687, + "space": 0.06928406466512702, + "network bandwidth": 0.03464203233256351, + "software": 0.057736720554272515, + "maintenance": 0.07650115473441109, + "insurance": 0.015300230946882216, + "personnel": 0.25981524249422633 + }, + "power_plus_cooling_share": 0.15762124711316397 + }, + "sources_table_sha256": "5777743e88a8c094e9369c652386e174b9350cfea52084d284d06e6d5e7a8e9f", + "gpu_stack_git": { + "repo": "gpu_stack", + "commit": "fa26e66d833a4ed4a8c7d3766674b0c3956107f9", + "branch": "hw-costaudit", + "dirty": "false" + } + } +} diff --git a/experiments/e003-heatwave/results/cost-audit-v1.md b/experiments/e003-heatwave/results/cost-audit-v1.md new file mode 100644 index 0000000..40bcd78 --- /dev/null +++ b/experiments/e003-heatwave/results/cost-audit-v1.md @@ -0,0 +1,198 @@ +# Electricity share of cost per token: audit results (generated) + +Generated by `experiments/e003-heatwave/cost_audit/run_cost_audit.py`. All cases call `gpu_stack.calculator.estimate` for the +README example (7e9 parameters, 2e12 tokens, 1,024 GPUs, MFU 40%) with the inputs in `cost_audit/sources.json`. +Nothing here is measured on a real cluster; prices are published list or market figures. + +Base case (H100, calculator defaults): electricity 5,766 USD, hardware 71,910 USD, electricity share 7.42%, total 0.03884 USD per million tokens. + +## Defaults by chip and cost basis + +Rental basis: total = rental rate x calculator GPU-hours; electricity is the lessor's electricity cost for the same run, as a share of the rental price. + +| chip | basis | USD/GPU-hour (rental) | GPU price in model (USD) | TDP (W) | facility W/GPU | total USD/Mtoken | electricity USD/Mtoken | electricity share | +|---|---|---|---|---|---|---|---|---| +| H100 | purchase | | 55,875 (all-in) | 700 | 1149 | 0.03884 | 0.00288 | 7.42% | +| H100 | on_demand | 3.99 | 55,875 (all-in) | 700 | 1149 | 0.11762 | 0.00288 | 2.45% | +| H100 | committed_1y | 2.19 | 55,875 (all-in) | 700 | 1149 | 0.06469 | 0.00288 | 4.46% | +| H100 | committed_3y | 1.46 | 55,875 (all-in) | 700 | 1149 | 0.04293 | 0.00288 | 6.71% | +| H200 | purchase | | 60,238 (all-in) | 700 | 1149 | 0.04137 | 0.00288 | 6.97% | +| H200 | on_demand | 5.45 | 60,238 (all-in) | 700 | 1149 | 0.16065 | 0.00288 | 1.79% | +| H200 | committed_1y | 3.00 | 60,238 (all-in) | 700 | 1149 | 0.08835 | 0.00288 | 3.26% | +| H200 | committed_3y | 1.99 | 60,238 (all-in) | 700 | 1149 | 0.05864 | 0.00288 | 4.92% | +| B200 | purchase | | 70,550 (all-in) | 1000 | 1613 | 0.02196 | 0.00178 | 8.10% | +| B200 | on_demand | 7.64 | 70,550 (all-in) | 1000 | 1613 | 0.09910 | 0.00178 | 1.80% | +| B200 | committed_1y | 4.20 | 70,550 (all-in) | 1000 | 1613 | 0.05451 | 0.00178 | 3.26% | +| B200 | committed_3y | 2.79 | 70,550 (all-in) | 1000 | 1613 | 0.03617 | 0.00178 | 4.92% | +| GB200 | purchase | | 60,417 (all-in) | 1200 | 1659 | 0.01753 | 0.00165 | 9.39% | +| GB200 | on_demand | 10.50 | 60,417 (all-in) | 1200 | 1659 | 0.12250 | 0.00165 | 1.34% | +| GB200 | committed_1y | 5.78 | 60,417 (all-in) | 1200 | 1659 | 0.06738 | 0.00165 | 2.44% | +| GB200 | committed_3y | 3.83 | 60,417 (all-in) | 1200 | 1659 | 0.04471 | 0.00165 | 3.68% | + +## One at a time, H100 (other inputs at their defaults for that chip) + +`d total (whole bill)` = change in total cost per token if electricity cost falls by the e003 saving at lambda 0.5 (CAISO 8.6%, DE-LU 12.6%) on the whole electricity bill. `d total (matched)` = same, only on the wholesale-linked part of a retail price, with the CAISO saving for California prices and the DE-LU saving for the German price. + +| parameter | setting | source | electricity share | total USD/Mtoken | d total, CAISO saving (whole bill) | d total, DE-LU saving (whole bill) | d total (matched) | +|---|---|---|---|---|---|---|---| +| chip purchase price | low end of cited range (x0.933) | H100 SXM5 card (IntuitionLabs) x (1 + servers, network, storage share from calculator_cost_basis) | 7.86% | 0.03667 | -0.676% | -0.992% | | +| chip purchase price | high end of cited range (x1.067) | H100 SXM5 card (IntuitionLabs) x (1 + servers, network, storage share from calculator_cost_basis) | 7.03% | 0.04100 | -0.605% | -0.887% | | +| server useful life | 4.0 years | operators' reported server lives, 4 to 6 years (sources.json life.*) | 5.65% | 0.05101 | -0.486% | -0.713% | | +| server useful life | 6.0 years | operators' reported server lives, 4 to 6 years (sources.json life.*) | 7.98% | 0.03613 | -0.687% | -1.007% | | +| residual value | 0% (no residual) | SILICONDATA | 7.11% | 0.04055 | -0.612% | -0.897% | | +| residual value | 45% (45% resale (used-market share at age 3, Silicon Data text)) | SILICONDATA | 11.45% | 0.02517 | -0.986% | -1.445% | | +| PUE | 1.09 (Google fleet) | pue.* rows | 6.79% | 0.03857 | -0.584% | -0.857% | | +| PUE | 1.36 (Uptime capacity-weighted) | pue.* rows | 8.33% | 0.03922 | -0.717% | -1.051% | | +| PUE | 1.52 (Uptime per-facility mean) | pue.* rows | 9.22% | 0.03961 | -0.793% | -1.163% | | +| electricity price | NM: 0.0585 USD/kWh | elec.eia2025.nm_industrial | 5.22% | 0.03794 | -0.450% | -0.659% | | +| electricity price | TX: 0.0635 USD/kWh | elec.eia2025.tx_industrial | 5.64% | 0.03811 | -0.486% | -0.712% | | +| electricity price | VA: 0.0949 USD/kWh | elec.eia2025.va_industrial | 8.21% | 0.03917 | -0.706% | -1.036% | | +| electricity price | CA-E20: 0.1273 USD/kWh | elec.pge_e20_transmission | 10.71% | 0.04027 | -0.922% | -1.352% | -0.255% | +| electricity price | DE: 0.1978 USD/kWh | elec.de.bdew_industry | 15.71% | 0.04266 | -1.352% | -1.982% | -1.397% | +| electricity price | CA: 0.2096 USD/kWh | elec.eia2025.ca_industrial | 16.49% | 0.04305 | -1.419% | -2.081% | -0.238% | +| electricity price | CA-E20-LF0.7: 0.1370 USD/kWh | elec.pge_e20_transmission | 11.44% | 0.04060 | -0.984% | -1.443% | -0.253% | +| MFU | 0.3 | calculator help text (uncited range) | 7.42% | 0.05178 | -0.639% | -0.937% | | +| MFU | 0.55 | calculator help text (uncited range) | 7.42% | 0.02825 | -0.639% | -0.937% | | +| node power | 5.91 kW average per node (extra server power 0.32) | arXiv 2506.14551 Table 2 (min and max of listed averages) | 5.95% | 0.03738 | -0.512% | -0.751% | | +| node power | 7.79 kW average per node (extra server power 0.74) | arXiv 2506.14551 Table 2 (min and max of listed averages) | 7.53% | 0.03895 | -0.648% | -0.950% | | +| servers, network, storage share of card price | 0.42 (HGX server) | price.h100.sys_hgx_new, net.amcompute_h100_meta | 7.71% | 0.03741 | -0.663% | -0.973% | | +| servers, network, storage share of card price | 0.56 (DGX server) | price.h100.sys_dgx, net.amcompute_h100_meta | 7.14% | 0.04038 | -0.614% | -0.901% | | +| facility cost | 0 USD/W (term removed) | ablation | 8.16% | 0.03534 | -0.702% | -1.029% | | +| facility cost | 8 USD/W (JLL emerging markets, below) | fac.jll_per_mw_2026 | 7.62% | 0.03782 | -0.656% | -0.962% | | +| facility cost | 15 USD/W (JLL premium markets, above) | fac.jll_per_mw_2026 | 7.21% | 0.03998 | -0.620% | -0.910% | | +| facility life | 10 years | life.facility.* rows (Amazon FY2025 10-K) | 7.23% | 0.03989 | -0.622% | -0.912% | | +| facility life | 40 years | life.facility.* rows (Amazon FY2025 10-K) | 7.90% | 0.03648 | -0.680% | -0.997% | | +| cost basis | rental, on demand (lessor's electricity as a share of the rental price) | rent.* rows | 2.45% | 0.11762 | -0.211% | -0.309% | | +| cost basis | rental, committed 1y (lessor's electricity as a share of the rental price) | rent.* rows | 4.46% | 0.06469 | -0.383% | -0.562% | | +| cost basis | rental, committed 3y (lessor's electricity as a share of the rental price) | rent.* rows | 6.71% | 0.04293 | -0.578% | -0.847% | | +| cost basis | rental on-demand, cheapest listed (2.64 USD/GPU-hour) | rent.* rows | 3.70% | 0.07782 | -0.319% | -0.467% | | +| cost basis | rental on-demand, dearest listed (6.88 USD/GPU-hour) | rent.* rows | 1.42% | 0.20282 | -0.122% | -0.179% | | + +## One at a time, B200 (other inputs at their defaults for that chip) + +`d total (whole bill)` = change in total cost per token if electricity cost falls by the e003 saving at lambda 0.5 (CAISO 8.6%, DE-LU 12.6%) on the whole electricity bill. `d total (matched)` = same, only on the wholesale-linked part of a retail price, with the CAISO saving for California prices and the DE-LU saving for the German price. + +| parameter | setting | source | electricity share | total USD/Mtoken | d total, CAISO saving (whole bill) | d total, DE-LU saving (whole bill) | d total (matched) | +|---|---|---|---|---|---|---|---| +| chip purchase price | low end of cited range (x0.867) | HGX B200 8-GPU server (AMCompute) per GPU, plus 576-GPU B200 cluster network, storage and other per GPU | 9.09% | 0.01957 | -0.782% | -1.147% | | +| chip purchase price | high end of cited range (x1.133) | HGX B200 8-GPU server (AMCompute) per GPU, plus 576-GPU B200 cluster network, storage and other per GPU | 7.31% | 0.02435 | -0.629% | -0.922% | | +| server useful life | 4.0 years | operators' reported server lives, 4 to 6 years (sources.json life.*) | 6.20% | 0.02872 | -0.533% | -0.782% | | +| server useful life | 6.0 years | operators' reported server lives, 4 to 6 years (sources.json life.*) | 8.70% | 0.02046 | -0.748% | -1.097% | | +| residual value | 0% (no residual) | SILICONDATA | 7.77% | 0.02291 | -0.668% | -0.980% | | +| residual value | 45% (45% resale (used-market share at age 3, Silicon Data text)) | SILICONDATA | 12.38% | 0.01437 | -1.065% | -1.562% | | +| PUE | 1.09 (Google fleet) | pue.* rows | 7.41% | 0.02180 | -0.638% | -0.936% | | +| PUE | 1.36 (Uptime capacity-weighted) | pue.* rows | 9.08% | 0.02220 | -0.782% | -1.146% | | +| PUE | 1.52 (Uptime per-facility mean) | pue.* rows | 10.05% | 0.02243 | -0.865% | -1.268% | | +| electricity price | NM: 0.0585 USD/kWh | elec.eia2025.nm_industrial | 5.71% | 0.02140 | -0.492% | -0.721% | | +| electricity price | TX: 0.0635 USD/kWh | elec.eia2025.tx_industrial | 6.17% | 0.02151 | -0.531% | -0.779% | | +| electricity price | VA: 0.0949 USD/kWh | elec.eia2025.va_industrial | 8.95% | 0.02216 | -0.770% | -1.130% | | +| electricity price | CA-E20: 0.1273 USD/kWh | elec.pge_e20_transmission | 11.65% | 0.02284 | -1.003% | -1.471% | -0.277% | +| electricity price | DE: 0.1978 USD/kWh | elec.de.bdew_industry | 17.01% | 0.02432 | -1.464% | -2.146% | -1.512% | +| electricity price | CA: 0.2096 USD/kWh | elec.eia2025.ca_industrial | 17.84% | 0.02456 | -1.535% | -2.251% | -0.258% | +| electricity price | CA-E20-LF0.7: 0.1370 USD/kWh | elec.pge_e20_transmission | 12.43% | 0.02304 | -1.070% | -1.569% | -0.275% | +| MFU | 0.3 | calculator help text (uncited range) | 8.10% | 0.02928 | -0.697% | -1.022% | | +| MFU | 0.55 | calculator help text (uncited range) | 8.10% | 0.01597 | -0.697% | -1.022% | | +| facility cost | 0 USD/W (term removed) | ablation | 8.99% | 0.01980 | -0.773% | -1.134% | | +| facility cost | 8 USD/W (JLL emerging markets, below) | fac.jll_per_mw_2026 | 8.34% | 0.02133 | -0.718% | -1.053% | | +| facility cost | 15 USD/W (JLL premium markets, above) | fac.jll_per_mw_2026 | 7.85% | 0.02267 | -0.676% | -0.991% | | +| facility life | 10 years | life.facility.* rows (Amazon FY2025 10-K) | 7.87% | 0.02261 | -0.677% | -0.993% | | +| facility life | 40 years | life.facility.* rows (Amazon FY2025 10-K) | 8.68% | 0.02050 | -0.747% | -1.095% | | +| cost basis | rental, on demand (lessor's electricity as a share of the rental price) | rent.* rows | 1.80% | 0.09910 | -0.154% | -0.227% | | +| cost basis | rental, committed 1y (lessor's electricity as a share of the rental price) | rent.* rows | 3.26% | 0.05451 | -0.281% | -0.412% | | +| cost basis | rental, committed 3y (lessor's electricity as a share of the rental price) | rent.* rows | 4.92% | 0.03617 | -0.423% | -0.621% | | +| cost basis | rental on-demand, cheapest listed (6.69 USD/GPU-hour) | rent.* rows | 2.05% | 0.08672 | -0.177% | -0.259% | | +| cost basis | rental on-demand, dearest listed (8.6 USD/GPU-hour) | rent.* rows | 1.60% | 0.11148 | -0.137% | -0.201% | | + +## Grid summary + +Grid: chip (4) x server life (4, 5, 6 years) x electricity price (7 regions) x PUE (4 cited values) for purchase; chip x rental basis (3) x region x PUE for rental. + +| chip | basis | cases | min share | median share | max share | cases at or above 10% | at or above 20% | at or above 30% | +|---|---|---|---|---|---|---|---|---| +| H100 | purchase | 84 | 3.6% | 8.4% | 21.3% | 32 | 2 | 0 | +| H100 | rental_committed_1y | 28 | 2.8% | 5.6% | 13.9% | 6 | 0 | 0 | +| H100 | rental_committed_3y | 28 | 4.2% | 8.5% | 20.9% | 11 | 1 | 0 | +| H100 | rental_on_demand | 28 | 1.5% | 3.1% | 7.6% | 0 | 0 | 0 | +| H200 | purchase | 84 | 3.4% | 7.8% | 20.2% | 30 | 1 | 0 | +| H200 | rental_committed_1y | 28 | 2.0% | 4.1% | 10.2% | 1 | 0 | 0 | +| H200 | rental_committed_3y | 28 | 3.1% | 6.2% | 15.3% | 8 | 0 | 0 | +| H200 | rental_on_demand | 28 | 1.1% | 2.3% | 5.6% | 0 | 0 | 0 | +| B200 | purchase | 84 | 4.0% | 9.1% | 22.9% | 37 | 5 | 0 | +| B200 | rental_committed_1y | 28 | 2.0% | 4.1% | 10.2% | 1 | 0 | 0 | +| B200 | rental_committed_3y | 28 | 3.1% | 6.2% | 15.3% | 8 | 0 | 0 | +| B200 | rental_on_demand | 28 | 1.1% | 2.3% | 5.6% | 0 | 0 | 0 | +| GB200 | purchase | 84 | 4.6% | 10.6% | 25.9% | 45 | 11 | 0 | +| GB200 | rental_committed_1y | 28 | 1.5% | 3.1% | 7.6% | 0 | 0 | 0 | +| GB200 | rental_committed_3y | 28 | 2.3% | 4.7% | 11.5% | 3 | 0 | 0 | +| GB200 | rental_on_demand | 28 | 0.8% | 1.7% | 4.2% | 0 | 0 | 0 | + +## Grid: purchase basis, electricity share by region (PUE 1.2, server life 5 years) + +| chip | NM | TX | US | VA | CA-E20 | DE | CA | +|---|---|---|---|---|---|---|---| +| H100 | 4.8% | 5.2% | 6.9% | 7.6% | 9.9% | 14.6% | 15.3% | +| H200 | 4.5% | 4.9% | 6.4% | 7.1% | 9.3% | 13.8% | 14.5% | +| B200 | 5.3% | 5.7% | 7.5% | 8.3% | 10.8% | 15.8% | 16.6% | +| GB200 | 6.2% | 6.6% | 8.7% | 9.6% | 12.5% | 18.1% | 19.0% | + +Region prices used (USD/kWh): NM 0.0585; TX 0.0635; US 0.0851; VA 0.0949; CA-E20 0.1273; DE 0.1978; CA 0.2096. + +## Rental basis: lessor's electricity share of the rental price, by region (PUE 1.2) + +| chip | basis | NM | TX | US | VA | CA-E20 | DE | CA | +|---|---|---|---|---|---|---|---|---| +| H100 | rental_on_demand | 1.7% | 1.8% | 2.5% | 2.7% | 3.7% | 5.7% | 6.0% | +| H100 | rental_committed_1y | 3.1% | 3.3% | 4.5% | 5.0% | 6.7% | 10.4% | 11.0% | +| H100 | rental_committed_3y | 4.6% | 5.0% | 6.7% | 7.5% | 10.0% | 15.6% | 16.5% | +| H200 | rental_on_demand | 1.2% | 1.3% | 1.8% | 2.0% | 2.7% | 4.2% | 4.4% | +| H200 | rental_committed_1y | 2.2% | 2.4% | 3.3% | 3.6% | 4.9% | 7.6% | 8.0% | +| H200 | rental_committed_3y | 3.4% | 3.7% | 4.9% | 5.5% | 7.4% | 11.4% | 12.1% | +| B200 | rental_on_demand | 1.2% | 1.3% | 1.8% | 2.0% | 2.7% | 4.2% | 4.4% | +| B200 | rental_committed_1y | 2.2% | 2.4% | 3.3% | 3.6% | 4.9% | 7.6% | 8.0% | +| B200 | rental_committed_3y | 3.4% | 3.7% | 4.9% | 5.5% | 7.4% | 11.4% | 12.1% | +| GB200 | rental_on_demand | 0.9% | 1.0% | 1.3% | 1.5% | 2.0% | 3.1% | 3.3% | +| GB200 | rental_committed_1y | 1.7% | 1.8% | 2.4% | 2.7% | 3.7% | 5.7% | 6.0% | +| GB200 | rental_committed_3y | 2.5% | 2.7% | 3.7% | 4.1% | 5.5% | 8.6% | 9.1% | + +## Electricity price at which the share reaches a level (purchase basis, PUE 1.2) + +| chip | life (years) | hardware cost (USD) | energy (kWh) | price for 10% | price for 20% | price for 30% | price for 50% (USD/kWh) | +|---|---|---|---|---|---|---|---| +| H100 | 4.0 | 96,251 | 67,750 | 0.158 | 0.355 | 0.609 | 1.421 | +| H100 | 5.5 | 71,910 | 67,750 | 0.118 | 0.265 | 0.455 | 1.061 | +| H100 | 6.0 | 66,500 | 67,750 | 0.109 | 0.245 | 0.421 | 0.982 | +| H200 | 4.0 | 103,209 | 67,743 | 0.169 | 0.381 | 0.653 | 1.524 | +| H200 | 5.5 | 76,970 | 67,743 | 0.126 | 0.284 | 0.487 | 1.136 | +| H200 | 6.0 | 71,139 | 67,743 | 0.117 | 0.263 | 0.450 | 1.050 | +| B200 | 4.0 | 53,875 | 41,813 | 0.143 | 0.322 | 0.552 | 1.288 | +| B200 | 5.5 | 40,359 | 41,813 | 0.107 | 0.241 | 0.414 | 0.965 | +| B200 | 6.0 | 37,356 | 41,813 | 0.099 | 0.223 | 0.383 | 0.893 | +| GB200 | 4.0 | 42,192 | 38,707 | 0.121 | 0.273 | 0.467 | 1.090 | +| GB200 | 5.5 | 31,776 | 38,707 | 0.091 | 0.205 | 0.352 | 0.821 | +| GB200 | 6.0 | 29,461 | 38,707 | 0.085 | 0.190 | 0.326 | 0.761 | + +## Cost-structure cross-check (not an input) + +A published worked example of a 100-H100 cluster (Introl, 2026-04-04, see `xcheck.introl_100_h100_5yr`) lists costs the calculator does not model (staff, space, software, maintenance, insurance, bandwidth). Shares of its gross five-year cost (hardware and installation plus five years of annual items, before the residual credit): + +| item | share of gross 5-year cost | +|---|---| +| hardware | 30.6% | +| installation and training | 2.3% | +| power | 12.1% | +| cooling | 3.6% | +| space | 6.9% | +| network bandwidth | 3.5% | +| software | 5.8% | +| maintenance | 7.7% | +| insurance | 1.5% | +| personnel | 26.0% | + +The article's power lines are not consistent with its own inputs (see the row's basis), so this cross-check shows the kinds and rough size of omitted terms, not a reference value for electricity's share. Omitted terms lower the electricity share of a complete cost per token. + + +## e003 timing savings read from cost-v1.json + +Headline policy timestral_28M|f=0.3|W=24|rho=0.7|q=0.5, lambda 0.5: electricity saving CAISO 8.61% (wholesale mean 35.19 USD/MWh), DE-LU 12.62% (wholesale mean 139.36 USD/MWh at 1.1502 USD per EUR). At lambda 0: CAISO 8.09%, DE-LU 12.12%. +The savings are on wholesale day-ahead prices. A retail price has a wholesale-linked part and fixed parts (delivery, demand charges, taxes); the matched columns apply the saving to the wholesale-linked part only. Demand charges would rise if shifting raises the peak load; that is not modelled. + From 45378ec718471f7be36c981bd3df435ac48e22d5 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 18:20:30 -0700 Subject: [PATCH 32/52] e003 outage: per-GPU temperature-residual channel, scenario v2, runner with v1 comparison (simulated) Adds one monitored series per GPU (class hardware): the GPU's temperature above rack air, averaged over the decision interval from the simulator's per-GPU history, minus the GPU's own gain times the median of its job peers in the rack. The level is calibrated on event-free training-seed runs like the other anomaly channels. A per-GPU warning protects only the job that holds the GPU; hardware faults now list the faulty GPU as an event unit so a warning on another GPU of the rack is a false alarm. OutageRun no longer adds precursor offsets (the simulator exposes them since cbf6d7e). Scenario v2 reads sim-scenario-v3 and adds the channel; v1 settings reproduce the v1 channel set. The runner writes results/outage-v2.json with a side-by-side against outage-v1.json, headline useful GPU-hours and deficit, alarms per day by class and channel. Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek Co-Authored-By: Claude Sonnet --- .../e003-heatwave/outage-scenario-v2.json | 189 ++++++++++ experiments/e003-heatwave/run_outage.py | 141 +++++++- gpu_stack/research/heatwave_outage.py | 337 +++++++++++++++--- tests/test_research_heatwave_outage.py | 315 ++++++++++++++++ 4 files changed, 925 insertions(+), 57 deletions(-) create mode 100644 experiments/e003-heatwave/outage-scenario-v2.json diff --git a/experiments/e003-heatwave/outage-scenario-v2.json b/experiments/e003-heatwave/outage-scenario-v2.json new file mode 100644 index 0000000..1144a4f --- /dev/null +++ b/experiments/e003-heatwave/outage-scenario-v2.json @@ -0,0 +1,189 @@ +{ + "schema": "gpu-stack.e003-heatwave-outage-scenario.v1", + "data_kind": "simulated", + "scenario_id": "e003-heatwave-outage-v2", + "description": "Outage-response experiment on the e003 two-site simulated cluster. Each seed is a 15-hour simulated window with its own synthetic job mix, synthetic weather and a randomly drawn schedule of cooling failures, breaker events, grid curtailments and hardware faults (some with precursors). Every number here is a simulation assumption, not a measurement. v2 adds one monitored series per GPU (temperature residual against rack peers) and reads the simulator scenario v3; seeds, event generation and every other setting equal outage-scenario-v1.json.", + "base_scenario": "sim-scenario-v3.json", + "base_overrides": { + "horizon_s": 54000, + "base_dt_s": 60.0, + "control_dt_s": 60.0, + "controller_interval_s": 600.0, + "outputs": { + "gpu_windows": [] + }, + "environment": { + "synthetic": { + "start_hour_utc": 17.0, + "sites": { + "site-a": { + "outdoor": { + "heatwaves": [ + { + "center_h": 6.0, + "width_h": 3.0, + "add_c": 8.0 + } + ] + } + }, + "site-b": { + "outdoor": { + "heatwaves": [ + { + "center_h": 5.0, + "width_h": 3.0, + "add_c": 7.0 + } + ] + } + } + } + } + }, + "jobs": { + "generate": { + "duration_range_s": [ + 1800, + 21600 + ] + } + } + }, + "seeds": { + "train": [ + 101, + 102, + 103, + 104 + ], + "test": [ + 201, + 202, + 203, + 204, + 205, + 206, + 207, + 208, + 209, + 210 + ] + }, + "seed_streams": { + "job_seed_offset": 1000, + "environment_seed_offset": 2000, + "event_seed_offset": 3000, + "sim_seed_offset": 0 + }, + "event_generation": { + "warmup_s": 10800.0, + "tail_s": 3600.0, + "rates_per_site_per_day": { + "cooling_failure": 1.5, + "breaker_trip": 1.5, + "grid_curtailment": 1.5, + "hardware_fault": 3.0 + }, + "precursor_probability": 0.5, + "lead_range_s": [ + 1800.0, + 5400.0 + ], + "strength_ranges": { + "supply_temp_c": [ + 2.0, + 4.0 + ], + "gpu_temp_c": [ + 4.0, + 8.0 + ] + }, + "duration_range_s": [ + 1800.0, + 5400.0 + ], + "remaining_capacity_range": [ + 0.15, + 0.5 + ], + "derate_range": [ + 0.5, + 0.7 + ], + "limit_fraction_range": [ + 0.4, + 0.7 + ], + "repair_range_s": [ + 3600.0, + 14400.0 + ] + }, + "outage": { + "decision_interval_s": 600.0, + "context_steps": 18, + "horizon_steps": 6, + "clear_decisions": 2, + "recheckpoint_s": 1200.0, + "max_pause_s": 7200.0, + "calibration_quantile": 1.0, + "anomaly_baseline_window_s": 14400.0, + "anomaly_baseline_gap_s": 2700.0, + "anomaly_smooth_steps": 2, + "curtail_shed_margin": 0.02, + "oracle_extra_lead_steps": 2, + "episode_merge_gap_s": 1200.0, + "threshold_grid": [ + 0.05, + 0.1, + 0.2, + 0.3, + 0.5, + 0.7, + 0.9 + ], + "observed_threshold": 0.5, + "classical": { + "seasonal_naive_season_length": 1, + "ets_season_length": 1, + "arima_order": [ + 2, + 1, + 1 + ] + }, + "timestral_sizes": [ + "5M", + "28M" + ], + "bootstrap_resamples": 2000, + "per_gpu_channel": { + "min_peers": 3, + "peer_excess_floor_fraction_of_idle": 0.5, + "baseline_min_samples": 6, + "smooth_steps": 1 + } + }, + "assumptions": [ + "Everything in this file is a simulation setting. No number here is a measurement of a real cluster.", + "The two-site cluster, job generator, thermal, breaker and power parameters come from sim-scenario-v2.json (not edited here). Power levels are read through the simulator at run time, so a recalibration of the simulator scenario flows through.", + "Job durations are drawn log-uniform between 1800 s and 21600 s (the base scenario uses 3600 s to 172800 s) so that a 15-hour window holds dozens of jobs; the job mix is otherwise the base scenario's, including its 10 percent of two-shard jobs that already follow the averaging rule.", + "The simulated clock starts at 17:00 UTC (late morning to midday local time); heat-wave centers and amplitudes are chosen to fall inside the 15-hour window and are simulation assumptions.", + "Event rates, precursor probability, precursor lead times and strengths, durations and severities are simulation assumptions chosen to produce several events per window; they are not rates of real facilities.", + "Controller decisions, forecasts and warnings use a 600 s interval; the simulator control step is 60 s and the base step is 60 s (the sim-scenario-v2.json steps are 10 s and 1 s).", + "Warning horizon 6 steps x 600 s = 60 min; forecast context 18 steps x 600 s = 3 h, equal to the 3 h warm-up before the first scheduled event.", + "Probability levels for the supply-temperature and GPU-temperature anomaly channels are the largest value (calibration_quantile 1.0) seen in event-free runs on training seeds. Level of the GPU temperature channel is the throttle threshold of the simulator scenario; level of the breaker load channel is 1.0 (rack power above rating); level of the site channel is the grid import limit including announced curtailments.", + "recheckpoint_s, clear_decisions, max_pause_s and oracle_extra_lead_steps are response-policy design choices that are not tuned.", + "Per-GPU channel (v2): one series per GPU, the GPU's temperature above rack air averaged over the decision interval (from the simulator's per-GPU history) minus its own gain times the median of its peers (same job, same rack). The gain is the median ratio to the peer median over the GPU's samples that are between anomaly_baseline_gap_s and anomaly_baseline_window_s old, with at least baseline_min_samples samples. A sample needs at least min_peers peers, a peer median above peer_excess_floor_fraction_of_idle times the idle-level excess (idle GPU power times the thermal scenario's GPU-to-air coefficient, both read from the simulator configuration) and an unchanged job group since the previous decision. Its level is the largest value seen in event-free runs on training seeds, as for the other anomaly channels." + ], + "derived_from": { + "file": "outage-scenario-v1.json", + "changes": [ + "base_scenario: sim-scenario-v2.json -> sim-scenario-v3.json (job schedule and synthetic environment follow the run seed; outage seed_scenario already sets both seeds, so the simulated inputs are unchanged)", + "outage.per_gpu_channel added (one residual series per GPU, class hardware)", + "scenario_id and description" + ] + } +} diff --git a/experiments/e003-heatwave/run_outage.py b/experiments/e003-heatwave/run_outage.py index 155adc8..759f941 100644 --- a/experiments/e003-heatwave/run_outage.py +++ b/experiments/e003-heatwave/run_outage.py @@ -1,7 +1,9 @@ """Outage-response experiment on the e003 Heatwave simulator (all data simulated). Compares warning-and-response policies on the simulated two-site cluster of -``sim-scenario-v2.json`` with the settings in ``outage-scenario-v1.json``: +``sim-scenario-v3.json`` with the settings in ``outage-scenario-v2.json`` (v2 adds +one per-GPU temperature-residual series; ``--scenario outage-scenario-v1.json +--out results/outage-v1.json`` runs the v1 channel set): * reactive (no forecast; the simulator rolls work back to the last checkpoint), * the e001 averaging rule alone (every splittable job runs as two shards), @@ -18,13 +20,16 @@ ``tune`` warning threshold per forecaster on training seeds only (replay objective) ``closed`` closed-loop runs on held-out seeds (the response actually runs in the simulator) ``report`` aggregation with seed-bootstrap intervals, written with provenance + (and, for v2, the side-by-side comparison with ``results/outage-v1.json``) python experiments/e003-heatwave/run_outage.py --work-dir DIR --stage all ``--policies`` and ``--forecasters`` restrict a stage so several processes can share the work. Needs numpy, scipy, statsforecast and (for Timestral) torch plus the ``timestral`` package. Provenance is written with -``heatwave.provenance`` and then pointed at this repository's git state. +``heatwave.provenance`` and then pointed at this repository's git state. In a +``git archive`` copy (no .git, as on Colab) set ``GPU_STACK_GIT_COMMIT`` to the +archived commit, or run the ``report`` stage on a machine with the repository. """ from __future__ import annotations @@ -32,7 +37,7 @@ import argparse import hashlib import json -import pickle +import os import subprocess import sys import time @@ -78,9 +83,9 @@ ) from gpu_stack.research.heatwave_sim import build_config # noqa: E402 -SCENARIO = HERE / "outage-scenario-v1.json" -BASE_SCENARIO = HERE / "sim-scenario-v2.json" -OUT = HERE / "results" / "outage-v1.json" +SCENARIO = HERE / "outage-scenario-v2.json" +OUT = HERE / "results" / "outage-v2.json" +V1_RESULT = HERE / "results" / "outage-v1.json" FORECASTERS = ("naive", "ets", "arima", "ts5m", "ts28m", "finetuned") CLASSICAL = ("naive", "ets", "arima") @@ -105,14 +110,17 @@ def log(msg: str) -> None: def load_pickle(path: Path): - with open(path, "rb") as f: - return pickle.load(f) + """Read a cached stage object (stored as a one-element object array by ``save_pickle``).""" + return np.load(path, allow_pickle=True)[0] def save_pickle(path: Path, obj) -> None: - tmp = path.with_suffix(path.suffix + ".tmp") + """Cache a stage object atomically (a one-element object array in an .npy file).""" + tmp = path.with_name(path.name + f".{os.getpid()}.tmp") + holder = np.empty(1, dtype=object) + holder[0] = obj with open(tmp, "wb") as f: - pickle.dump(obj, f) + np.save(f, holder, allow_pickle=True) tmp.replace(path) @@ -146,6 +154,8 @@ def seeds_for(outage: dict, which: str, args: argparse.Namespace) -> list[int]: test = [int(s) for s in outage["seeds"]["test"]] if args.n_test: test = test[: args.n_test] + if getattr(args, "only_seeds", None) and which != "train": + test = [x for x in test if x in set(args.only_seeds)] return {"train": train, "test": test, "all": train + test}[which] @@ -197,7 +207,7 @@ def load_calibration(outage, S, args, work): train = seeds_for(outage, "train", args) preps = [prepare_seed(outage, S, s, True, work) for s in train] cfg = build_config(seed_scenario(outage, train[0], with_events=False), seed=train[0]) - index = SeriesIndex.from_config(cfg) + index = SeriesIndex.from_config(cfg, S.per_gpu_channel) return calibrate([p["ef_samples"] for p in preps], index, S, [p["ef_times"] for p in preps], "event-free runs on training seeds"), index @@ -376,8 +386,74 @@ def git_state() -> dict: def git(*a: str) -> str: return subprocess.run(["git", *a], cwd=ROOT, capture_output=True, text=True, check=True).stdout.strip() - dirty = bool(git("status", "--porcelain", "--", ".", ":(exclude)experiments/e003-heatwave/results")) - return {"commit": git("rev-parse", "HEAD"), "branch": git("rev-parse", "--abbrev-ref", "HEAD"), "dirty": str(dirty).lower()} + try: + dirty = bool(git("status", "--porcelain", "--", ".", ":(exclude)experiments/e003-heatwave/results")) + return {"commit": git("rev-parse", "HEAD"), "branch": git("rev-parse", "--abbrev-ref", "HEAD"), "dirty": str(dirty).lower()} + except (subprocess.CalledProcessError, FileNotFoundError, OSError): + commit = os.environ.get("GPU_STACK_GIT_COMMIT") + if not commit: + raise RuntimeError("not a git checkout: set GPU_STACK_GIT_COMMIT to the archived commit, or run the report stage on the VM") + return {"commit": commit, "branch": "archive", "dirty": "false", "source": "archive"} + + +def paired_by_seed(new: dict, old: dict, key: str, seeds: list, B: int, seed: int) -> dict: + """Mean over seeds of (new - old) for a per-seed field, with a bootstrap interval.""" + a = [new[str(s)][key] for s in seeds] + b = [old[str(s)][key] for s in seeds] + return paired_difference(a, b, B, seed) + + +def compare_with_v1(policies: dict, v1: dict, test: list, B: int) -> dict: + """Side-by-side v1 (rack-level channels only) and v2 numbers from the two result files.""" + v1r = v1["result"] + out: dict = {"v1_scenario_id": v1r["scenario_id"], "v1_seeds": v1r["seeds"], "same_test_seeds": v1r["seeds"]["test"] == test, "policies": {}} + for name, p2 in policies.items(): + p1 = v1r["policies"].get(name) + if p1 is None: + continue + o1, o2 = p1["overall"], p2["overall"] + + def val(o, k): + x = o.get(k) + return x + + def hw(p): + e = p["by_event_type"].get("hardware_fault") + if e is None: + return None + return {"n_events": e["n_events"], "n_with_precursor": e["n_with_precursor"], "recall": e["recall"], + "recall_in_time": e["recall_in_time"], "lead_minutes_median": e["lead_minutes_median"], "lost_gpu_h": e["lost_gpu_h"]} + + row = { + "threshold_v1": p1.get("threshold"), "threshold_v2": p2.get("threshold"), + "useful_gpu_h": {"v1": o1["useful_gpu_h"], "v2": o2["useful_gpu_h"]}, + "deficit_gpu_h": {"v1": o1["deficit_gpu_h"], "v2": o2["deficit_gpu_h"]}, + "lost_gpu_h": {"v1": o1["lost_gpu_h"], "v2": o2["lost_gpu_h"]}, + "response_gpu_h": {"v1": o1["response_gpu_h"], "v2": o2["response_gpu_h"]}, + "recall": {"v1": val(o1, "recall"), "v2": val(o2, "recall")}, + "recall_in_time": {"v1": val(o1, "recall_in_time"), "v2": val(o2, "recall_in_time")}, + "lead_minutes_median": {"v1": None if o1.get("lead_minutes") is None else o1["lead_minutes"]["median"], + "v2": None if o2.get("lead_minutes") is None else o2["lead_minutes"]["median"]}, + "false_alarms_per_day": {"v1": val(o1, "false_alarms_per_day"), "v2": val(o2, "false_alarms_per_day")}, + "hardware_fault": {"v1": hw(p1), "v2": hw(p2)}, + "recall_by_event_type": { + k: {"v1": None if p1["by_event_type"].get(k, {}).get("recall") is None else p1["by_event_type"][k]["recall"], + "v2": None if p2["by_event_type"].get(k, {}).get("recall") is None else p2["by_event_type"][k]["recall"]} + for k in sorted(set(p1["by_event_type"]) | set(p2["by_event_type"])) + }, + } + if all(str(sd) in p1["per_seed"] and str(sd) in p2["per_seed"] for sd in test): + row["paired_v2_minus_v1"] = { + "deficit_gpu_h": paired_by_seed(p2["per_seed"], p1["per_seed"], "deficit_gpu_h", test, B, 81), + "lost_gpu_h": paired_by_seed(p2["per_seed"], p1["per_seed"], "lost_gpu_h", test, B, 82), + "response_gpu_h": paired_by_seed(p2["per_seed"], p1["per_seed"], "response_gpu_h", test, B, 83), + } + row["max_abs_per_seed_difference"] = { + k: float(max(abs(p2["per_seed"][str(sd)][k] - p1["per_seed"][str(sd)][k]) for sd in test)) + for k in ("deficit_gpu_h", "lost_gpu_h", "lost_steps", "response_gpu_h", "n_alarms") + } + out["policies"][name] = row + return out def stage_report(outage, S, args, work) -> None: @@ -412,6 +488,7 @@ def stage_report(outage, S, args, work) -> None: agg["per_seed"] = { str(r["seed"]): { "lost_gpu_h": r["score"]["summary"]["lost_gpu_hours"], "lost_steps": r["score"]["summary"]["lost_steps"], + "useful_gpu_h": r["score"]["summary"]["useful_gpu_hours"], "deficit_gpu_h": r["score"]["deficit_gpu_h"], "response_gpu_h": r["score"]["response"]["total_gpu_h"], "n_alarms": r["score"]["response"]["n_alarms"], "n_false_alarms": r["score"]["response"]["n_false_alarms"], } @@ -428,6 +505,8 @@ def stage_report(outage, S, args, work) -> None: "lost_gpu_h_minus_reactive": paired_difference([r["summary"]["lost_gpu_hours"] for r in runs], [r["summary"]["lost_gpu_hours"] for r in base], B, 70), "lost_steps_minus_reactive": paired_difference([r["summary"]["lost_steps"] for r in runs], [r["summary"]["lost_steps"] for r in base], B, 71), "deficit_gpu_h_minus_reactive": paired_difference([r["deficit_gpu_h"] for r in runs], [r["deficit_gpu_h"] for r in base], B, 72), + "useful_gpu_h_minus_reactive": paired_difference( + [r["summary"]["useful_gpu_hours"] for r in runs], [r["summary"]["useful_gpu_hours"] for r in base], B, 74), "import_energy_above_kwh_minus_reactive": paired_difference( [r["summary"]["import_energy_above_kwh"] for r in runs], [r["summary"]["import_energy_above_kwh"] for r in base], B, 73), } @@ -444,6 +523,25 @@ def stage_report(outage, S, args, work) -> None: c["scheduled"] += int(ev["source"] == "scheduled") first = preps[test[0]] cfg0 = build_config(seed_scenario(outage, test[0]), seed=test[0]) + ref_useful = {str(s): float(preps[s]["ref_useful"]) for s in test} + reference = { + "useful_gpu_h_event_free_per_seed": ref_useful, + "useful_gpu_h_event_free": bootstrap_ci(list(ref_useful.values()), B, 61), + "note": "useful GPU-hours of the event-free run of the same seed; deficit_gpu_h = this minus the policy's useful GPU-hours", + } + headline = {} + for name, agg in policies.items(): + o = agg["overall"] + headline[name] = { + "label": agg["label"], "threshold": agg["threshold"], + "useful_gpu_h": o["useful_gpu_h"], "deficit_gpu_h": o["deficit_gpu_h"], "lost_gpu_h": o["lost_gpu_h"], + "response_gpu_h": o["response_gpu_h"], + "deficit_vs_reactive_paired": paired.get(name, {}).get("deficit_gpu_h_minus_reactive"), + "useful_vs_reactive_paired": paired.get(name, {}).get("useful_gpu_h_minus_reactive"), + } + v1_cmp = None + if V1_RESULT.exists() and args.compare_v1: + v1_cmp = compare_with_v1(policies, json.loads(V1_RESULT.read_text()), test, B) result = { "data_kind": "simulated", "scenario_id": outage["scenario_id"], @@ -455,8 +553,11 @@ def stage_report(outage, S, args, work) -> None: "event_counts_test_seeds": event_counts, "tuning": tuning, "threshold_sweep_open_loop_test_seeds": sweep, + "event_free_reference": reference, + "headline_useful_and_deficit": headline, "policies": policies, "paired_vs_reactive": paired, + "comparison_with_v1": v1_cmp, "not_run": not_run, "finetuned_weights": args.finetuned_weights if finetuned_available(args) else None, "notes": [ @@ -472,7 +573,8 @@ def stage_report(outage, S, args, work) -> None: } from heatwave.provenance import write_result - inputs = [SCENARIO, BASE_SCENARIO] + base_scenario = SCENARIO.parent / outage["base_scenario"] + inputs = [SCENARIO, base_scenario] + ([V1_RESULT] if v1_cmp is not None else []) record = write_result(OUT, result, script=__file__, data_kind="simulated", inputs=inputs, notes="Held-out seeds; thresholds tuned on training seeds; all outputs simulated.") record["provenance"]["git"] = git_state() @@ -485,6 +587,11 @@ def stage_report(outage, S, args, work) -> None: log(f"wrote {OUT} ({OUT.stat().st_size / 1024:.0f} KiB)") +def set_paths(scenario: Path, out: Path) -> None: + global SCENARIO, OUT + SCENARIO, OUT = Path(scenario).resolve(), Path(out).resolve() + + def main() -> None: warnings.filterwarnings("ignore") ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) @@ -493,13 +600,19 @@ def main() -> None: ap.add_argument("--policies", nargs="*", default=None, choices=POLICIES) ap.add_argument("--forecasters", nargs="*", default=None, choices=FORECASTERS + ("perfect",)) ap.add_argument("--n-test", type=int, default=0, help="use only the first N held-out seeds (0 = all)") + ap.add_argument("--only-seeds", type=int, nargs="*", default=None, + help="restrict the openloop/closed stages to these held-out seeds (several processes can share the work; report needs all)") ap.add_argument("--reverse", action="store_true", help="open-loop pass visits seeds last to first (a second process can share the work)") ap.add_argument("--threads", type=int, default=None) ap.add_argument("--batch-size", type=int, default=512) + ap.add_argument("--scenario", type=Path, default=SCENARIO, help="outage scenario file (default outage-scenario-v2.json)") + ap.add_argument("--out", type=Path, default=OUT, help="result file written by the report stage") + ap.add_argument("--no-compare-v1", dest="compare_v1", action="store_false", help="skip the side-by-side with results/outage-v1.json") ap.add_argument("--finetuned-weights", default=None) ap.add_argument("--finetuned-size", default="28M") args = ap.parse_args() args.work_dir.mkdir(parents=True, exist_ok=True) + set_paths(args.scenario, args.out) outage = load_outage_scenario(SCENARIO) S = OutageSettings.from_scenario(outage) work = args.work_dir diff --git a/gpu_stack/research/heatwave_outage.py b/gpu_stack/research/heatwave_outage.py index 48e1668..071ef10 100644 --- a/gpu_stack/research/heatwave_outage.py +++ b/gpu_stack/research/heatwave_outage.py @@ -42,6 +42,13 @@ ``site_power`` (site, class ``curtail``) Site power against the grid import limit, including curtailments announced in the known-future schedule. +``gpu_resid`` (one series per GPU, class ``hardware``; only when ``per_gpu_channel`` is on) + Temperature of one GPU above the level its rack peers predict (K). The + GPU's temperature above rack air is compared with the median of the GPUs of + the same job in the same rack, scaled by the GPU's own gain against those + peers (a median over its older samples). Job power changes cancel because + peers share them, and a creep of one GPU shows at full size even when the + rack maximum stays unchanged. Level: calibrated like ``supply_anom``. Information boundary -------------------- @@ -53,12 +60,13 @@ Simulator note -------------- -At simulator commit b6adcfa the per-GPU temperature offsets of the hardware -fault precursor are applied inside the simulator's thermal step but not to the -visible per-GPU temperature, so that precursor is invisible to every -controller. :class:`OutageRun` adds the offsets to the visible state (and -skips the addition if the simulator already exposes them). The request to fix -this upstream is in the experiment report. +From simulator commit cbf6d7e the per-GPU temperature offsets of the hardware +fault precursor are part of the visible per-GPU temperature +(``VisibleState.now["gpu_temp_c"]``), of the history channel +``history("gpu_temp_c", window_s)`` and of the rack maximum. Before that +commit they were applied inside the thermal step only, and :class:`OutageRun` +added them to the visible state; the simulator now does this itself, so +:class:`OutageRun` is the plain simulator run. Scoring ------- @@ -121,14 +129,17 @@ CH_BREAKER = "breaker_ratio" CH_GPU_ANOM = "gpu_anom" CH_SITE_POWER = "site_power" +CH_GPU_RESID = "gpu_resid" RACK_CHANNELS = (CH_GPU_TEMP, CH_SUPPLY, CH_BREAKER, CH_GPU_ANOM) SITE_CHANNELS = (CH_SITE_POWER,) +GPU_CHANNELS = (CH_GPU_RESID,) CHANNEL_CLASS = { CH_GPU_TEMP: CLASS_THERMAL, CH_SUPPLY: CLASS_COOLING, CH_BREAKER: CLASS_POWER, CH_GPU_ANOM: CLASS_HARDWARE, CH_SITE_POWER: CLASS_CURTAIL, + CH_GPU_RESID: CLASS_HARDWARE, } # Which warning classes may explain which kind of event. @@ -194,6 +205,12 @@ class OutageSettings: threshold_grid: tuple[float, ...] observed_threshold: float bootstrap_resamples: int + # per-GPU residual channel (off by default: the v1 channel set) + per_gpu_channel: bool = False + gpu_min_peers: int = 3 + gpu_peer_excess_floor_frac: float = 0.5 + gpu_baseline_min_samples: int = 6 + gpu_smooth_steps: int = 1 def __post_init__(self) -> None: _num(self.decision_interval_s, "decision_interval_s", strict=True) @@ -212,6 +229,10 @@ def __post_init__(self) -> None: _num(self.episode_merge_gap_s, "episode_merge_gap_s") _num(self.observed_threshold, "observed_threshold", strict=True, maximum=1.0) _int(self.bootstrap_resamples, "bootstrap_resamples", 10) + _int(self.gpu_min_peers, "gpu_min_peers", 2) + _num(self.gpu_peer_excess_floor_frac, "gpu_peer_excess_floor_frac", strict=True, maximum=1.0) + _int(self.gpu_baseline_min_samples, "gpu_baseline_min_samples", 2) + _int(self.gpu_smooth_steps, "gpu_smooth_steps", 1) if not self.threshold_grid or any(not 0.0 < g < 1.0 for g in self.threshold_grid): raise ValueError("threshold_grid needs values strictly between 0 and 1") @@ -223,6 +244,16 @@ def horizon_s(self) -> float: def from_scenario(cls, outage: Mapping[str, Any]) -> "OutageSettings": block = outage["outage"] gen = outage["event_generation"] + pg = block.get("per_gpu_channel") + extra: dict[str, Any] = {} + if pg: + extra = { + "per_gpu_channel": True, + "gpu_min_peers": int(pg["min_peers"]), + "gpu_peer_excess_floor_frac": float(pg["peer_excess_floor_fraction_of_idle"]), + "gpu_baseline_min_samples": int(pg["baseline_min_samples"]), + "gpu_smooth_steps": int(pg["smooth_steps"]), + } return cls( decision_interval_s=float(block["decision_interval_s"]), warmup_s=float(gen["warmup_s"]), @@ -241,6 +272,7 @@ def from_scenario(cls, outage: Mapping[str, Any]) -> "OutageSettings": threshold_grid=tuple(float(x) for x in block["threshold_grid"]), observed_threshold=float(block["observed_threshold"]), bootstrap_resamples=int(block["bootstrap_resamples"]), + **extra, ) @@ -330,27 +362,11 @@ def seed_scenario(outage: Mapping[str, Any], seed: int, *, with_events: bool = T class OutageRun(_Run): - """Simulator run that also exposes the hardware precursor in the visible per-GPU temperature.""" - - def _visible_state(self, t: float, b: int) -> VisibleState: - vs = super()._visible_state(t, b) - offsets = self.signals.gpu_temp_offsets(t) - if not offsets: - return vs - gpu_temp = vs.now["gpu_temp_c"] - changed = False - for rack, slot, off in offsets: - idx = int(self.rack_start[rack] + slot) - if not np.isfinite(gpu_temp[idx]): - continue - base_value = self.rack_air_now[rack] + self.k_gpu[idx] * self.gpu_mean_prev[idx] - if abs(gpu_temp[idx] - base_value) > 0.5 * abs(off) and abs(off) > 1e-9: - continue # the simulator already includes the offset - gpu_temp[idx] += off - changed = True - if changed: - vs.now["max_gpu_temp_c"][:] = self._rack_max(gpu_temp) - return vs + """The simulator run used by every outage policy. + + The hardware precursor's per-GPU temperature offsets are part of the + simulator's visible state since commit cbf6d7e, so nothing is added here. + """ class PlacementView: @@ -378,6 +394,12 @@ def gpu_group(self) -> np.ndarray: g[g == self._run.n_shards] = -1 return g + def gpu_job_ids(self) -> list[Optional[str]]: + """Job id per GPU (``None`` for GPUs without a job).""" + run = self._run + sid = np.asarray(run.gpu_shard).astype(int) + return [None if int(x) == run.n_shards else run.shards[int(x)].job.job_id for x in sid] + def job_rack_counts(self) -> dict[str, dict[str, int]]: out: dict[str, dict[str, int]] = {} run = self._run @@ -438,22 +460,32 @@ def periodic_local_jobs(jobs: Iterable[Any]) -> list[Any]: # --------------------------------------------------------------------------- +def gpu_unit_id(rack_id: str, slot: int) -> str: + """Unit id of one GPU: ``/`` (the simulator's own spelling in fault reasons).""" + return f"{rack_id}/{int(slot)}" + + @dataclass(frozen=True) class SeriesIndex: - """Fixed order of the monitored series: rack channels first, then site channels.""" + """Fixed order of the monitored series: rack channels, site channels, then per-GPU channels.""" keys: tuple[tuple[str, str], ...] # (unit_id, channel) rack_ids: tuple[str, ...] site_ids: tuple[str, ...] rack_site: tuple[str, ...] + gpu_units: tuple[str, ...] = () # unit ids of the per-GPU series (empty without the per-GPU channel) @classmethod - def from_config(cls, cfg: SimConfig) -> "SeriesIndex": + def from_config(cls, cfg: SimConfig, per_gpu: bool = False) -> "SeriesIndex": rack_ids = tuple(r.rack_id for lay in cfg.layouts for r in lay.racks) rack_site = tuple(lay.site.site_id for lay in cfg.layouts for _ in lay.racks) site_ids = tuple(lay.site.site_id for lay in cfg.layouts) keys = [(r, ch) for ch in RACK_CHANNELS for r in rack_ids] + [(s, ch) for ch in SITE_CHANNELS for s in site_ids] - return cls(tuple(keys), rack_ids, site_ids, rack_site) + gpu_units: tuple[str, ...] = () + if per_gpu: + gpu_units = tuple(gpu_unit_id(r.rack_id, slot) for lay in cfg.layouts for r in lay.racks for slot in range(r.n_gpus)) + keys += [(u, ch) for ch in GPU_CHANNELS for u in gpu_units] + return cls(tuple(keys), rack_ids, site_ids, rack_site, gpu_units) def __len__(self) -> int: return len(self.keys) @@ -467,6 +499,11 @@ def classes(self) -> list[str]: def units(self) -> list[str]: return [u for u, _ in self.keys] + def gpu_parts(self, unit: str) -> tuple[str, int]: + """``(rack_id, slot)`` of a per-GPU unit id.""" + rack, slot = unit.rsplit("/", 1) + return rack, int(slot) + # --------------------------------------------------------------------------- # probabilities from quantiles @@ -675,6 +712,78 @@ def _peer_median(values: np.ndarray, group: np.ndarray, rack: np.ndarray) -> np. return out +def _peer_stats(values: np.ndarray, key: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Median and count of the finite ``values`` that share a ``key`` (per element; NaN/0 if none).""" + med = np.full(values.shape, np.nan) + cnt = np.zeros(values.shape) + for k in np.unique(key): + idx = np.flatnonzero(key == k) + v = values[idx] + f = np.isfinite(v) + if f.any(): + med[idx] = np.median(v[f]) + cnt[idx] = float(f.sum()) + return med, cnt + + +class GpuResidualChannel: + """Per-GPU temperature residual against the GPU's rack peers (K). Causal. + + Input per decision: ``excess`` (GPU temperature above its rack air, averaged + over the decision interval, NaN where the GPU is dead or its rack is off) and + ``key`` (job group and rack of each GPU; GPUs with the same key are peers). + + All GPUs of a job in a rack run at the same power, so ``excess`` of a GPU is + its own gain times the peer median, whatever the power level. The gain is + estimated as the median ratio ``excess / peer median`` over the GPU's + samples that are at least ``anomaly_baseline_gap_s`` old and at most + ``anomaly_baseline_window_s`` old. The residual is ``excess`` minus gain + times the current peer median. A sample is valid only if the GPU had at + least ``gpu_min_peers`` finite peers, the peer median is above a floor + (a fraction of the idle-level excess), and the GPU kept its key since the + previous decision (a job start, end or move inside the interval gives NaN). + The residual is then averaged over the last ``gpu_smooth_steps`` valid + decisions. Only past samples enter any step. + """ + + def __init__(self, n_gpus: int, settings: OutageSettings, floor_k: float) -> None: + self.n = int(n_gpus) + self.settings = settings + self.floor_k = float(floor_k) + self._ratio: list[np.ndarray] = [] + self._raw: list[np.ndarray] = [] + self._prev_key: Optional[np.ndarray] = None + + def update(self, excess: np.ndarray, key: np.ndarray) -> np.ndarray: + st = self.settings + excess = np.asarray(excess, dtype=float) + key = np.asarray(key) + med, cnt = _peer_stats(excess, key) + stable = np.zeros(self.n, dtype=bool) if self._prev_key is None else (key == self._prev_key) + ok = np.isfinite(excess) & (cnt >= st.gpu_min_peers) & (med > self.floor_k) & stable + safe = np.where(ok, med, 1.0) + self._ratio.append(np.where(ok, excess / safe, np.nan)) + n = len(self._ratio) + dt = st.decision_interval_s + gap = int(math.ceil(st.anomaly_baseline_gap_s / dt)) + win = int(math.ceil(st.anomaly_baseline_window_s / dt)) + base = np.full(self.n, np.nan) + lo, hi = max(0, n - 1 - win), max(0, n - gap) + if hi - lo >= st.gpu_baseline_min_samples: + old = np.stack(self._ratio[lo:hi]) + enough = np.isfinite(old).sum(axis=0) >= st.gpu_baseline_min_samples + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + base = np.where(enough, np.nanmedian(old, axis=0), np.nan) + raw = np.where(ok & np.isfinite(base), excess - base * med, np.nan) + self._raw.append(raw) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + out = np.where(np.isfinite(raw), np.nanmean(np.stack(self._raw[-st.gpu_smooth_steps:]), axis=0), np.nan) + self._prev_key = key.copy() + return out + + class ChannelBank: """Turns visible state into one sample vector per decision and keeps the history.""" @@ -682,7 +791,7 @@ def __init__(self, cfg: SimConfig, settings: OutageSettings, placement: Optional self.cfg = cfg self.settings = settings self.placement = placement - self.index = SeriesIndex.from_config(cfg) + self.index = SeriesIndex.from_config(cfg, settings.per_gpu_channel) self.times: list[float] = [] self.rows: list[np.ndarray] = [] self.site_limits: list[np.ndarray] = [] # (S, H) per decision @@ -691,6 +800,14 @@ def __init__(self, cfg: SimConfig, settings: OutageSettings, placement: Optional self._site_nominal_import = np.array([float(lay.site.grid_import_limit_w) for lay in cfg.layouts]) self._site_cap = np.array([float(lay.site.power_cap_w) for lay in cfg.layouts]) self._rack_start = None + self.gpu_channel: Optional[GpuResidualChannel] = None + if settings.per_gpu_channel: + if placement is None: + raise ValueError("the per-GPU channel needs the placement view") + idle_excess_k = float(cfg.levels.idle_w) * float(cfg.thermal.gpu_to_air_k_per_w) + self.gpu_channel = GpuResidualChannel( + len(self.index.gpu_units), settings, settings.gpu_peer_excess_floor_frac * idle_excess_k + ) # -- helpers ----------------------------------------------------------------- @@ -734,13 +851,36 @@ def sample(self, t_s: float, vs: VisibleState) -> np.ndarray: ratio = np.where(on, np.asarray(now["breaker_load_ratio"], dtype=float), np.nan) gpu_anom = self._gpu_anomaly(vs) site_power = np.asarray(now["site_power_w"], dtype=float) - row = np.concatenate([gpu_temp_max, supply, ratio, gpu_anom, site_power]) + parts = [gpu_temp_max, supply, ratio, gpu_anom, site_power] + if self.gpu_channel is not None: + parts.append(self._gpu_resid(vs)) + row = np.concatenate(parts) row[~np.isfinite(row)] = np.nan self.times.append(float(t_s)) self.rows.append(row) self.site_limits.append(self.site_limit_path(vs, t_s)) return row + def _gpu_resid(self, vs: VisibleState) -> np.ndarray: + """Per-GPU residual against rack peers (see :class:`GpuResidualChannel`). + + The GPU's excess over rack air is the mean of the simulator's per-GPU + temperature history over the last decision interval (past samples only). + """ + assert self.gpu_channel is not None and self.placement is not None + dt = self.settings.decision_interval_s + gpu_rack = self.placement.gpu_rack() + _, temp = vs.history("gpu_temp_c", dt) + _, air = vs.history("rack_air_c", dt) + if temp.shape[0] == 0: + excess = np.full(gpu_rack.shape, np.nan) + else: + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + excess = np.nanmean(np.asarray(temp, dtype=float) - np.asarray(air, dtype=float)[:, gpu_rack], axis=0) + key = (self.placement.gpu_group().astype(np.int64) + 1) * 1000 + gpu_rack.astype(np.int64) + return self.gpu_channel.update(excess, key) + def _gpu_anomaly(self, vs: VisibleState) -> np.ndarray: """Per-rack largest per-GPU temperature rise against the GPU's own baseline (K). @@ -816,7 +956,7 @@ def series_limits( out[k] = float(cfg.throttle.throttle_threshold_c) elif ch == CH_BREAKER: out[k] = 1.0 - elif ch in (CH_SUPPLY, CH_GPU_ANOM): + elif ch in (CH_SUPPLY, CH_GPU_ANOM, CH_GPU_RESID): if calibration is not None and ch in calibration.limits: out[k] = float(calibration.limits[ch]) elif ch == CH_SITE_POWER: @@ -831,14 +971,15 @@ def calibrate( times: Sequence[np.ndarray], source: str = "", ) -> Calibration: - """Trouble levels for ``supply_anom`` and ``gpu_anom``: a high quantile of event-free samples. + """Trouble levels for ``supply_anom``, ``gpu_anom`` and (if present) ``gpu_resid``: a high quantile of event-free samples. ``traces`` are ``(n_series, n_decisions)`` sample matrices from event-free runs on training seeds; samples before the warm-up are ignored. """ limits: dict[str, float] = {} counts: dict[str, int] = {} - for ch in (CH_SUPPLY, CH_GPU_ANOM): + channels = (CH_SUPPLY, CH_GPU_ANOM) + tuple(ch for ch in GPU_CHANNELS if any(c == ch for _, c in index.keys)) + for ch in channels: ks = [k for k, (_, c) in enumerate(index.keys) if c == ch] pool = [] for tr, t in zip(traces, times): @@ -931,8 +1072,15 @@ def plan( rack_alarms: Mapping[str, Alarm], site_alarms: Mapping[str, Alarm], job_at: Optional[Callable[[str, int], Optional[str]]] = None, + gpu_alarms: Optional[Mapping[str, Mapping[int, Alarm]]] = None, ) -> list[Any]: - """Actions for this decision. Site alarms carry ``limit_w``; rack alarms may name one GPU slot.""" + """Actions for this decision. + + Site alarms carry ``limit_w``; rack alarms may name one GPU slot (oracle). + ``gpu_alarms`` maps rack id to ``{slot: alarm}`` for per-GPU warnings: only + the job that holds the GPU is protected, and actions are tagged with the + GPU unit id (``/``) unless a rack-wide alarm already covers it. + """ st = self.settings views = {jv.job_id: jv for jv in vs.jobs} free = {s: int(vs.now["free_gpus"][i]) for i, s in enumerate(vs.site_ids)} @@ -1009,6 +1157,32 @@ def plan( self.last_checkpoint[job_id] = t_s continue self._checkpoint_or_pause(t_s, job_id, jv, rack, "rack", actions) + + # -- per-GPU alarms: protect the job that holds the warned GPU (unless a rack-wide alarm covers it) + for rack, slots in sorted((gpu_alarms or {}).items()): + if rack in rack_alarms or job_at is None: + continue + site = self.rack_site[rack] + for slot, alarm in sorted(slots.items()): + job_id = job_at(rack, slot) + if job_id is None or job_id in handled: + continue + job = self.jobs[job_id] + jv = views.get(job_id) + if jv is None or job.multisite or jv.paused: + continue + handled.add(job_id) + unit = gpu_unit_id(rack, slot) + n = int(job.n_gpus) + dest = self._dest(site, n, free, blocked_dest) if (jv.flexible and jv.status == "running") else None + if dest is not None: + actions.append(MigrateJob(job_id, dest)) + self._note(t_s, "MigrateJob", job_id, unit, "gpu") + free[dest] -= n + free[site] += n + self.last_checkpoint[job_id] = t_s + continue + self._checkpoint_or_pause(t_s, job_id, jv, unit, "gpu", actions) return actions def _dest_has_power(self, dest: str, n: int, site_power: Mapping[str, float], site_limit: Mapping[str, float]) -> bool: @@ -1116,6 +1290,8 @@ def _on_bind(self) -> None: self.rack_nonflex_checkpoint_gpu_h: list[np.ndarray] = [] self.site_flex_move_gpu_h: list[np.ndarray] = [] self.rack_on_rows: list[np.ndarray] = [] + self.gpu_flex_move_gpu_h: list[np.ndarray] = [] + self.gpu_nonflex_checkpoint_gpu_h: list[np.ndarray] = [] def decide(self, t_s, visible_state): assert self.bank is not None and self.placement is not None and self.cfg is not None and self.index is not None @@ -1143,12 +1319,26 @@ def decide(self, t_s, visible_state): self.rack_flex_move_gpu_h.append(flex_move) self.rack_nonflex_checkpoint_gpu_h.append(nonflex) self.site_flex_move_gpu_h.append(site_flex) + if self.settings.per_gpu_channel: + # cost of protecting the job that holds each GPU (single-site jobs only, as the planner acts) + g_flex = np.zeros(self.placement.n_gpus()) + g_non = np.zeros(self.placement.n_gpus()) + for g, job_id in enumerate(self.placement.gpu_job_ids()): + if job_id is None or jobs[job_id].multisite: + continue + c = job_costs(jobs[job_id], self.cfg.transfer) + if jobs[job_id].flexible: + g_flex[g] = c["move"] + else: + g_non[g] = c["checkpoint"] + self.gpu_flex_move_gpu_h.append(g_flex) + self.gpu_nonflex_checkpoint_gpu_h.append(g_non) self.rack_on_rows.append(np.asarray(visible_state.now["rack_on"], dtype=bool)) return () def trace(self) -> dict[str, Any]: assert self.bank is not None - return { + out = { "times_s": np.array(self.decision_times), "samples": self.bank.matrix(), "site_limits": np.stack(self.bank.site_limits) if self.bank.site_limits else np.zeros((0, 0, 0)), @@ -1156,6 +1346,10 @@ def trace(self) -> dict[str, Any]: "rack_nonflex_checkpoint_gpu_h": np.stack(self.rack_nonflex_checkpoint_gpu_h), "site_flex_move_gpu_h": np.stack(self.site_flex_move_gpu_h), } + if self.gpu_flex_move_gpu_h: + out["gpu_flex_move_gpu_h"] = np.stack(self.gpu_flex_move_gpu_h) + out["gpu_nonflex_checkpoint_gpu_h"] = np.stack(self.gpu_nonflex_checkpoint_gpu_h) + return out class ForecastWarningController(_BankController): @@ -1206,9 +1400,14 @@ def _respond(self, t_s: float, vs: VisibleState, active: np.ndarray) -> list[Any assert self.index is not None and self.tracker is not None and self.planner is not None and self.placement is not None rack_alarms: dict[str, Alarm] = {} site_alarms: dict[str, Alarm] = {} + gpu_alarms: dict[str, dict[int, Alarm]] = {} for k in np.flatnonzero(active): unit, ch = self.index.keys[k] cls = CHANNEL_CLASS[ch] + if ch == CH_GPU_RESID: + rack, slot = self.index.gpu_parts(unit) + gpu_alarms.setdefault(rack, {})[slot] = Alarm(rack, (cls,), self.tracker.since(int(k)), slot=slot) + continue by_unit = site_alarms if ch in SITE_CHANNELS else rack_alarms a = by_unit.get(unit) if a is None: @@ -1219,8 +1418,11 @@ def _respond(self, t_s: float, vs: VisibleState, active: np.ndarray) -> list[Any else: a.classes = tuple(sorted(set(a.classes) | {cls})) a.since_s = min(a.since_s, self.tracker.since(int(k))) - actions = list(self.planner.resumes(t_s, vs, set(rack_alarms), set(site_alarms))) - actions += self.planner.plan(t_s, vs, self.placement.job_rack_counts(), rack_alarms, site_alarms, self.placement.job_at) + gpu_tags = {gpu_unit_id(rack, slot) for rack, slots in gpu_alarms.items() for slot in slots} + actions = list(self.planner.resumes(t_s, vs, set(rack_alarms) | gpu_tags, set(site_alarms))) + actions += self.planner.plan( + t_s, vs, self.placement.job_rack_counts(), rack_alarms, site_alarms, self.placement.job_at, gpu_alarms + ) return actions def export(self) -> dict[str, Any]: @@ -1416,7 +1618,7 @@ def openloop_pmax( realized future of the same trace is used as a zero-spread forecast (an upper bound for any forecaster on that trace). """ - index = SeriesIndex.from_config(cfg) + index = SeriesIndex.from_config(cfg, settings.per_gpu_channel) samples = np.asarray(trace["samples"], dtype=float) n_series, n_dec = samples.shape T, H = settings.context_steps, settings.horizon_steps @@ -1513,6 +1715,8 @@ def build_event_table( for ev in cfg.events.events: if ev.event_id == gt["event_id"]: slot = getattr(ev, "gpu_slot", None) + if kind == KIND_HARDWARE_FAULT and slot is not None and rack is not None: + units = units + [gpu_unit_id(rack, int(slot))] # a per-GPU warning must name the faulty GPU window_end = gt["end_s"] if kind != KIND_HARDWARE_FAULT else gt["onset_s"] rows.append( { @@ -1759,8 +1963,11 @@ class may explain the event kind, it starts at or before the onset and the cand = [e for e in episodes if e["unit"] in ev["units"] and e["class"] in classes] covering = [e for e in cand if e["start_s"] <= ev["onset_s"] + 1e-9 and ev["onset_s"] <= e["end_s"] + L + 1e-9] lead = None + first_channel = None if covering: - lead = ev["onset_s"] - min(e["start_s"] for e in covering) + first = min(covering, key=lambda e: (e["start_s"], str(e.get("channel", "")))) + lead = ev["onset_s"] - first["start_s"] + first_channel = first.get("channel") late = None if lead is None: after = [e["start_s"] - ev["onset_s"] for e in cand if ev["onset_s"] < e["start_s"] <= ev["onset_s"] + L + 1e-9] @@ -1771,6 +1978,7 @@ class may explain the event kind, it starts at or before the onset and the "caught_in_time": lead is not None and lead >= min_lead_s, "lead_s": None if lead is None else float(lead), "late_s": late, + "first_channel": first_channel, } return out @@ -1795,6 +2003,7 @@ def classify_alarms( explained = ev["event_id"] break out.append({"unit": a["unit"], "start_s": a["start_s"], "end_s": a["end_s"], "classes": sorted(a["classes"]), "peak_p": a["peak_p"], + "channels": sorted({str(e.get("channel", "")) for e in a["episodes"]}), "true": explained is not None, "event_id": explained}) return out @@ -1901,7 +2110,11 @@ def replay_objective( for a in merge_alarms(episodes, settings.episode_merge_gap_s): d0 = int(np.argmin(np.abs(times - a["start_s"]))) dur = a["end_s"] - a["start_s"] - if a["unit"] in index.rack_ids: + if a["unit"] in index.gpu_units: + g = index.gpu_units.index(a["unit"]) + alarm_cost += float(trace["gpu_flex_move_gpu_h"][d0, g]) + alarm_cost += float(trace["gpu_nonflex_checkpoint_gpu_h"][d0, g]) * (1 + math.floor(dur / settings.recheckpoint_s)) + elif a["unit"] in index.rack_ids: r = index.rack_ids.index(a["unit"]) alarm_cost += float(trace["rack_flex_move_gpu_h"][d0, r]) alarm_cost += float(trace["rack_nonflex_checkpoint_gpu_h"][d0, r]) * (1 + math.floor(dur / settings.recheckpoint_s)) @@ -2025,6 +2238,26 @@ def per_seed(fn: Callable[[Mapping[str, Any]], float]) -> list[float]: alarms_true = [sum(1 for a in r["alarms"] if a["true"]) for r in runs] alarms_all = [len(r["alarms"]) for r in runs] out["overall"]["alarm_precision"] = ratio_ci(alarms_true, alarms_all, B, 17) + # false alarms per day by alarm class (an alarm that carries several classes counts once for each) + by_class: dict[str, Any] = {} + for cls_i, cls in enumerate(CLASSES): + by_class[cls] = { + "false_alarms_per_day": bootstrap_ci( + [sum(1 for a in r["alarms"] if not a["true"] and cls in a["classes"]) / r["response"]["evaluated_days"] for r in runs], B, 80 + cls_i), + "alarms_per_day": bootstrap_ci( + [sum(1 for a in r["alarms"] if cls in a["classes"]) / r["response"]["evaluated_days"] for r in runs], B, 90 + cls_i), + } + out["by_alarm_class"] = by_class + by_channel: dict[str, Any] = {} + channels = sorted({c for r in runs for a in r["alarms"] for c in a.get("channels", ())}) + for ch_i, ch in enumerate(channels): + by_channel[ch] = { + "false_alarms_per_day": bootstrap_ci( + [sum(1 for a in r["alarms"] if not a["true"] and ch in a.get("channels", ())) / r["response"]["evaluated_days"] for r in runs], B, 100 + ch_i), + "alarms_per_day": bootstrap_ci( + [sum(1 for a in r["alarms"] if ch in a.get("channels", ())) / r["response"]["evaluated_days"] for r in runs], B, 120 + ch_i), + } + out["by_alarm_channel"] = by_channel by_type: dict[str, Any] = {} for kind in EVENT_TYPES: sel = [[e for e in r["by_event"] if e["kind"] == kind] for r in runs] @@ -2040,6 +2273,19 @@ def per_seed(fn: Callable[[Mapping[str, Any]], float]) -> list[float]: "lost_gpu_h": bootstrap_ci([sum(e["lost_gpu_h"] for e in s) for s in sel], B, 23), "lost_steps": bootstrap_ci([sum(e["lost_steps"] for e in s) for s in sel], B, 24), } + with_pre = [[e for e in s_ if e["has_precursor"]] for s_ in sel] + if sum(len(x) for x in with_pre): + entry["recall_with_precursor"] = ratio_ci([sum(1 for e in x if e["caught"]) for x in with_pre], [len(x) for x in with_pre], B, 28) + entry["recall_in_time_with_precursor"] = ratio_ci([sum(1 for e in x if e["caught_in_time"]) for x in with_pre], [len(x) for x in with_pre], B, 29) + else: + entry["recall_with_precursor"] = None + entry["recall_in_time_with_precursor"] = None + first_ch: dict[str, int] = {} + for s_ in sel: + for e in s_: + if e.get("first_channel"): + first_ch[e["first_channel"]] = first_ch.get(e["first_channel"], 0) + 1 + entry["first_warning_channel_counts"] = first_ch if kind == KIND_GRID_CURTAILMENT: entry["violation_kwh"] = bootstrap_ci([sum(e["violation_kwh"] for e in s) for s in sel], B, 25) entry["violation_s"] = bootstrap_ci([sum(e["violation_s"] for e in s) for s in sel], B, 26) @@ -2069,9 +2315,14 @@ def per_seed(fn: Callable[[Mapping[str, Any]], float]) -> list[float]: if not has_warnings: for key in ("recall", "recall_in_time", "lead_minutes", "alarm_precision", "false_alarms_per_day", "false_alarm_gpu_h", "n_alarms"): out["overall"][key] = None + out["by_alarm_class"] = None + out["by_alarm_channel"] = None for entry in by_type.values(): for key in ("recall", "recall_in_time", "lead_minutes_median"): entry[key] = None + entry["first_warning_channel_counts"] = None + entry["recall_with_precursor"] = None + entry["recall_in_time_with_precursor"] = None out["no_precursor"]["recall"] = None out["with_precursor"]["recall"] = None return out @@ -2094,7 +2345,7 @@ def file_sha256(path: Path | str) -> str: __all__ = [ "Alarm", "Calibration", "ChannelBank", "EpisodeTracker", "ForecastWarningController", "OracleAlarm", "OracleController", "OutageRun", "OutageSettings", "PersistenceForecaster", "PlacementView", "ResponsePlanner", "SeriesIndex", "TraceRecorder", - "aggregate_runs", "attribute_faults", "bootstrap_ci", "build_event_table", "calibrate", "classify_alarms", + "CH_GPU_RESID", "GpuResidualChannel", "aggregate_runs", "attribute_faults", "gpu_unit_id", "bootstrap_ci", "build_event_table", "calibrate", "classify_alarms", "curtailment_violation", "deep_merge", "detection_stats", "episodes_from_pmax", "exceedance_probability", "job_costs", "load_outage_scenario", "match_events", "oracle_scenario", "oracle_settings", "merge_alarms", "openloop_pmax", "oracle_alarms_from_reference", "paired_difference", "periodic_local_jobs", "replay_objective", "response_costs", "run_policy", "score_run", diff --git a/tests/test_research_heatwave_outage.py b/tests/test_research_heatwave_outage.py index a0c4c6e..f3ea2ce 100644 --- a/tests/test_research_heatwave_outage.py +++ b/tests/test_research_heatwave_outage.py @@ -284,3 +284,318 @@ def test_aggregate_hides_detection_fields_without_a_warning_rule(faulted): assert without["no_precursor"]["n_events"] == 3 assert without["no_precursor"]["lost_gpu_h"]["mean"] == pytest.approx(with_w["no_precursor"]["lost_gpu_h"]["mean"]) assert with_w["data_kind"] == "simulated" + + +# --------------------------------------------------------------------------- +# per-GPU residual channel (outage scenario v2) +# --------------------------------------------------------------------------- + +SCENARIO_V2 = ROOT / "experiments" / "e003-heatwave" / "outage-scenario-v2.json" + + +def gpu_settings(S: OutageSettings) -> OutageSettings: + """Per-GPU channel on, 60 s decisions, short baseline so tiny windows can use it.""" + return replace( + S, decision_interval_s=60.0, context_steps=12, horizon_steps=6, warmup_s=0.0, recheckpoint_s=300.0, + anomaly_baseline_window_s=3600.0, anomaly_baseline_gap_s=600.0, per_gpu_channel=True, + ) + + +def v2_and_settings(): + outage = load_outage_scenario(SCENARIO_V2) + return outage, OutageSettings.from_scenario(outage) + + +class Layout: + """Dry run without events: which GPU slots of one rack hold which job, and each GPU's gain.""" + + def __init__(self): + self.job_slots = {} + self.k_gpu = None + self.rack_start = None + + def bind(self, run, cfg): + self.run = run + self.k_gpu = np.array(run.k_gpu) + self.rack_start = np.array(run.rack_start) + + def decide(self, t_s, vs): + if not self.job_slots and t_s > 0: + run = self.run + for sh in run.shards: + if sh.gpus.size: + self.job_slots.setdefault(sh.job.job_id, []).extend(int(g) for g in sh.gpus) + return () + + +def layout_of(outage, jobs, horizon_s=3600): + lay = Layout() + sc = tiny_scenario(outage, horizon_s, jobs) + cfg = build_config(sc, seed=0) + from gpu_stack.research.heatwave_outage import OutageRun + + run = OutageRun(cfg, lay, 0) + lay.bind(run, cfg) + run.run() + return lay, cfg + + +def test_scenario_v2_differs_from_v1_only_by_the_per_gpu_channel(): + v1, S1 = outage_and_settings() + v2, S2 = v2_and_settings() + assert not S1.per_gpu_channel and S2.per_gpu_channel + assert v1["seeds"] == v2["seeds"] and v1["event_generation"] == v2["event_generation"] and v1["seed_streams"] == v2["seed_streams"] + a = {k: v for k, v in v1["outage"].items()} + b = {k: v for k, v in v2["outage"].items() if k != "per_gpu_channel"} + assert a == b + assert v2["data_kind"] == "simulated" and v2["sim"]["data_kind"] == "simulated" + # the simulated inputs of a seed do not depend on the base scenario version (both seeds are set per run seed) + assert seed_scenario(v1, 201)["events"] == seed_scenario(v2, 201)["events"] + assert seed_scenario(v1, 201)["jobs"]["generate"] == seed_scenario(v2, 201)["jobs"]["generate"] + + +def test_series_layout_appends_one_series_per_gpu_after_the_v1_series(): + outage, S = v2_and_settings() + cfg = build_config(seed_scenario(outage, 201, with_events=False), seed=201) + from gpu_stack.research.heatwave_outage import SeriesIndex, gpu_unit_id + + base, full = SeriesIndex.from_config(cfg), SeriesIndex.from_config(cfg, True) + n_gpus = sum(r.n_gpus for lay in cfg.layouts for r in lay.racks) + assert len(full) == len(base) + n_gpus and full.keys[: len(base)] == base.keys + assert len(set(full.gpu_units)) == n_gpus and full.gpu_units[0] == gpu_unit_id("a1", 0) + assert all(full.gpu_parts(u) == (u.rsplit("/", 1)[0], int(u.rsplit("/", 1)[1])) for u in full.gpu_units) + assert set(full.classes()[len(base):]) == {"hardware"} + + +def test_residual_cancels_job_power_changes_that_a_plain_difference_keeps(): + from gpu_stack.research.heatwave_outage import GpuResidualChannel + + outage, S = v2_and_settings() + S = gpu_settings(S) + rng = np.random.default_rng(0) + gains = 0.0256 * (1.0 + 0.1 * rng.normal(size=12)) + key = np.zeros(12, dtype=np.int64) + ch = GpuResidualChannel(12, S, floor_k=1.0) + power = np.where(np.arange(40) % 8 < 4, 700.0, 300.0) # the whole job changes power every 4 decisions + out, plain = [], [] + for p in power: + excess = gains * p + out.append(ch.update(excess, key)) + plain.append(excess - np.median(excess)) + out, plain = np.array(out), np.array(plain) + late = out[20:] + assert np.isfinite(late).all() + assert np.abs(late).max() < 1e-9 + assert np.abs(plain[20:]).max() > 0.5 # the plain difference to the peer median moves by several tenths of a kelvin or more + + +def test_residual_sees_a_creep_on_one_gpu_and_marks_group_changes_missing(): + from gpu_stack.research.heatwave_outage import GpuResidualChannel + + outage, S = v2_and_settings() + S = gpu_settings(S) + gains = np.full(10, 0.025) + key = np.zeros(10, dtype=np.int64) + ch = GpuResidualChannel(10, S, floor_k=1.0) + res = [] + for i in range(60): + excess = gains * 600.0 + if i >= 40: + excess = excess.copy() + excess[3] += 0.25 * (i - 40) # 0.25 K per decision on GPU 3 only + if i == 50: + key = key.copy() + key[7] = 5 # GPU 7 changes job group: its sample is missing at that decision + res.append(ch.update(excess, key)) + res = np.array(res) + assert res[59, 3] == pytest.approx(0.25 * 19, abs=1e-6) + others = np.delete(res[59], 3) + assert np.nanmax(np.abs(others)) < 1e-9 + assert np.isnan(res[50, 7]) and np.isfinite(res[49, 7]) + + +def test_residual_channel_uses_no_future_samples(): + from gpu_stack.research.heatwave_outage import GpuResidualChannel + + outage, S = v2_and_settings() + S = gpu_settings(S) + rng = np.random.default_rng(3) + n, g = 50, 16 + gains = 0.0256 * (1.0 + 0.1 * rng.normal(size=g)) + inputs = [(gains * rng.uniform(300, 700) + rng.normal(0, 0.1, size=g), np.zeros(g, dtype=np.int64)) for _ in range(n)] + d = 30 + a = GpuResidualChannel(g, S, 1.0) + out_a = [a.update(x, k) for x, k in inputs] + changed = list(inputs[: d + 1]) + [(rng.uniform(0, 50, size=g), rng.integers(0, 3, size=g)) for _ in range(n - d - 1)] + b = GpuResidualChannel(g, S, 1.0) + out_b = [b.update(x, k) for x, k in changed] + for i in range(d + 1): + assert np.array_equal(out_a[i], out_b[i], equal_nan=True) + assert not np.array_equal(out_a[-1], out_b[-1], equal_nan=True) + + +def test_channel_rows_before_a_precursor_start_do_not_depend_on_the_event(): + outage, S = v2_and_settings() + S = gpu_settings(S) + job = make_job(n=16, duration=7200.0) + onset, lead = 4800.0, 1200.0 + pre = (Precursor("gpu_temp_c", lead, 6.0),) + rows = [] + for ev in ([], [HardwareFault("h1", "site-a", "a1", 3, onset, 120.0, precursors=pre)]): + rec = TraceRecorder(S) + run_policy(tiny_scenario(outage, 7200, [job], ev), rec, 0) + rows.append(rec.trace()) + t = rows[0]["times_s"] + before = t < onset - lead - 1e-9 + assert before.sum() > 20 + assert np.array_equal(rows[0]["samples"][:, before], rows[1]["samples"][:, before], equal_nan=True) + assert not np.array_equal(rows[0]["samples"][:, ~before], rows[1]["samples"][:, ~before], equal_nan=True) + + +def test_v1_series_are_unchanged_when_the_per_gpu_channel_is_on(): + outage, S = v2_and_settings() + S_on = gpu_settings(S) + S_off = replace(S_on, per_gpu_channel=False) + job = make_job(n=16, duration=3600.0) + traces = [] + for st in (S_off, S_on): + rec = TraceRecorder(st) + run_policy(tiny_scenario(outage, 3600, [job]), rec, 0) + traces.append(rec.trace()) + n_v1 = traces[0]["samples"].shape[0] + assert traces[1]["samples"].shape[0] > n_v1 + assert np.array_equal(traces[0]["samples"], traces[1]["samples"][:n_v1], equal_nan=True) + assert "gpu_flex_move_gpu_h" not in traces[0] and traces[1]["gpu_nonflex_checkpoint_gpu_h"].shape[1] == traces[1]["samples"].shape[0] - n_v1 + + +def creep_scenario(outage, jobs, events=()): + """Six simulated hours with the scenario's own 600 s decision interval.""" + sc = tiny_scenario(outage, 21600, jobs, events) + sc["controller_interval_s"] = 600.0 + return sc + + +@pytest.fixture(scope="module") +def creep(): + """Two 8-GPU jobs on rack a1; the lowest-gain GPU of job j1 creeps by 2.4 K over 3600 s, then faults. + + 2.4 K is below the smallest gap between the rack's hottest GPU and this GPU in the same run without + the event during the ramp, so the rack maximum never shows the creep; the test checks that. + """ + outage, S = v2_and_settings() + S = replace(S, warmup_s=0.0, context_steps=12) + jobs = [make_job("j1", n=8, duration=21600.0), make_job("j2", n=8, duration=21600.0)] + lay, cfg0 = layout_of(outage, jobs) + first_rack_gpus = set(range(int(cfg0.layouts[0].racks[0].n_gpus))) + j1 = [g for g in lay.job_slots["j1"] if g in first_rack_gpus] + j2 = [g for g in lay.job_slots["j2"] if g in first_rack_gpus] + assert len(j1) == 8 and len(j2) == 8 + slot = min(j1, key=lambda g: lay.k_gpu[g]) + strength, lead, onset = 2.4, 3600.0, 18000.0 + ev = HardwareFault("h1", "site-a", "a1", int(slot), onset, 300.0, precursors=(Precursor("gpu_temp_c", lead, strength),)) + return {"outage": outage, "S": S, "jobs": jobs, "slot": int(slot), "j2_gpus": j2, "strength": strength, "lead": lead, + "onset": onset, "event": ev, "lay": lay} + + +class WatchRecorder(TraceRecorder): + """Trace recorder that also keeps one GPU's visible temperature and its rack's visible maximum.""" + + def __init__(self, S, g, rack_i): + super().__init__(S) + self.g, self.rack_i, self.seen = g, rack_i, [] + + def decide(self, t_s, vs): + self.seen.append((t_s, float(vs.now["gpu_temp_c"][self.g]), float(vs.now["max_gpu_temp_c"][self.rack_i]))) + return super().decide(t_s, vs) + + +def test_residual_sees_a_one_gpu_precursor_that_the_rack_maximum_hides(creep): + from gpu_stack.research.heatwave_outage import CH_GPU_RESID, CH_GPU_TEMP, SeriesIndex, gpu_unit_id + + outage, S, onset, lead, strength = creep["outage"], creep["S"], creep["onset"], creep["lead"], creep["strength"] + traces, seen = {}, {} + for name, ev in (("with", [creep["event"]]), ("without", [])): + rec = WatchRecorder(S, creep["slot"], 0) + _, cfg = run_policy(creep_scenario(outage, creep["jobs"], ev), rec, 0) + traces[name], seen[name] = rec.trace(), np.array(rec.seen) + idx = SeriesIndex.from_config(cfg, True) + t = traces["with"]["times_s"] + k_res = idx.index(gpu_unit_id("a1", creep["slot"]), CH_GPU_RESID) + k_max = idx.index("a1", CH_GPU_TEMP) + pre = (t >= onset - lead) & (t < onset) + late_ramp = (t >= onset - 2 * S.decision_interval_s) & (t < onset) + # the creep is visible in the faulty GPU's own sensor ... + own = seen["with"][late_ramp, 1] - seen["without"][late_ramp, 1] + assert own.max() > 0.8 * strength + # ... but the rack's visible maximum is another GPU's temperature the whole time, and is unchanged by the creep + assert np.all(seen["with"][pre, 2] > seen["with"][pre, 1]) + assert np.allclose(traces["with"]["samples"][k_max][pre], traces["without"]["samples"][k_max][pre]) + # the residual follows the ramp (kelvin) and ends above the largest residual of any GPU in the run without the event + gk = [k for k, (u, c) in enumerate(idx.keys) if c == CH_GPU_RESID] + noise = np.nanmax(np.abs(traces["without"]["samples"][gk])) + ramp = traces["with"]["samples"][k_res] + assert np.nanmax(ramp[late_ramp]) > 0.5 * strength + assert np.nanmax(ramp[late_ramp]) > noise + # no other GPU of the cluster moves by more than the noise level of the run without the event (+ a small allowance) + others = [k for k in gk if k != k_res] + assert np.nanmax(np.abs(traces["with"]["samples"][others][:, t < onset])) < noise + 0.25 + + +def test_gpu_warning_protects_only_the_job_that_holds_the_gpu(creep): + from gpu_stack.research.heatwave_outage import Calibration, gpu_unit_id + + outage, S, onset = creep["outage"], creep["S"], creep["onset"] + cal = Calibration({"supply_anom": 1.0e9, "gpu_anom": 1.0e9, "gpu_resid": 1.5}, 1.0, {}, "test") + ctrl = ForecastWarningController(S, PersistenceForecaster(), 0.5, cal, "persistence") + sc = creep_scenario(outage, creep["jobs"], [creep["event"]]) + res, cfg = run_policy(sc, ctrl, 0) + unit = gpu_unit_id("a1", creep["slot"]) + ex = ctrl.export() + gpu_eps = [e for e in ex["episodes"] if e["channel"] == "gpu_resid"] + assert gpu_eps and {e["unit"] for e in gpu_eps} == {unit} and {e["class"] for e in gpu_eps} == {"hardware"} + assert gpu_eps[0]["start_s"] < onset + applied = [a for a in res.actions if a["status"] == "applied"] + assert applied and {a["detail"]["job_id"] for a in applied} == {"j1"} + assert all(m["unit"] == unit for m in ex["meta_actions"]) + table = build_event_table(cfg, res.events, S) + score = score_run(res, cfg, table, ex, S, 0.0) + row = [e for e in score["by_event"] if e["kind"] == "hardware_fault"][0] + assert row["caught"] and row["first_channel"] == "gpu_resid" and row["lead_s"] > 0.0 + assert score["response"]["n_false_alarms"] == 0 + alarm = [a for a in score["alarms"] if a["unit"] == unit][0] + assert alarm["true"] and alarm["channels"] == ["gpu_resid"] and alarm["cost_gpu_h"] > 0.0 + + +def test_a_warning_on_another_gpu_of_the_rack_does_not_count_as_catching_the_fault(faulted): + from gpu_stack.research.heatwave_outage import classify_alarms, match_events, merge_alarms + + S, table = faulted["S"], faulted["table"] + ev = [e for e in table if e["kind"] == "hardware_fault"][0] + assert "a1/2" in ev["units"] and "a1" in ev["units"] + + def episode(unit): + return {"unit": unit, "class": "hardware", "channel": "gpu_resid", "start_s": ev["onset_s"] - 1200.0, + "end_s": ev["onset_s"] - 120.0, "peak_p": 1.0, "n_decisions": 2, "k": 0} + + right, wrong = [episode("a1/2")], [episode("a1/5")] + assert match_events(table, right, S)[ev["event_id"]]["caught"] + assert not match_events(table, wrong, S)[ev["event_id"]]["caught"] + cls = classify_alarms(merge_alarms(wrong, S.episode_merge_gap_s), table, S) + assert cls and not cls[0]["true"] + assert classify_alarms(merge_alarms(right, S.episode_merge_gap_s), table, S)[0]["true"] + + +def test_replay_objective_prices_a_gpu_alarm_by_the_job_that_holds_the_gpu(creep): + from gpu_stack.research.heatwave_outage import SeriesIndex, gpu_unit_id, replay_objective + + outage, S = creep["outage"], creep["S"] + rec = TraceRecorder(S) + res, cfg = run_policy(creep_scenario(outage, creep["jobs"]), rec, 0) + trace = rec.trace() + idx = SeriesIndex.from_config(cfg, True) + unit = gpu_unit_id("a1", creep["slot"]) + ep = {"unit": unit, "class": "hardware", "channel": "gpu_resid", "start_s": 7200.0, "end_s": 7200.0, "peak_p": 1.0, "n_decisions": 1, "k": 0} + r = replay_objective([], [], [ep], trace, idx, S, 60.0) + expected = job_costs(cfg.jobs[0], cfg.transfer)["checkpoint"] # j1 is not flexible: one checkpoint of its 8 GPUs + assert r["alarm_gpu_h"] == pytest.approx(expected) + assert expected == pytest.approx(8 * cfg.jobs[0].checkpoint_duration_s / 3600.0) From 40602ce78c8932f464a8177053711f51bc583930 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 18:45:06 -0700 Subject: [PATCH 33/52] Packing: all 6 test seeds and the full 21-level grid for Timestral 5M (Colab CPU, resumed from parts, code e0a2364); merged packing-v1.json Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- .../eval/arima__q0.5__seed503.json | 77 + .../eval/arima__q0.5__seed504.json | 77 + .../eval/arima__q0.5__seed505.json | 77 + .../eval/arima__q0.99__seed503.json | 77 + .../eval/arima__q0.99__seed504.json | 77 + .../eval/arima__q0.99__seed505.json | 77 + .../eval/arima__q0.9__seed503.json | 77 + .../eval/arima__q0.9__seed504.json | 77 + .../eval/arima__q0.9__seed505.json | 77 + .../eval/ets__q0.5__seed503.json | 77 + .../eval/ets__q0.5__seed504.json | 77 + .../eval/ets__q0.5__seed505.json | 77 + .../eval/ets__q0.99__seed503.json | 77 + .../eval/ets__q0.99__seed504.json | 77 + 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Held-out episodes only; training episodes are listed separately. Most eval parts ran on a Colab CPU runtime (8 cores, high-RAM); see the compute field and packing-v1-parts/colab-run.json." }, "result": { @@ -334,12 +481,16 @@ "Running jobs are planned to end at their declared walltime stretched by the checkpoint overhead implied by the job generator's checkpoint ranges, and count in every 5-minute bin they overlap (end_rule 'overlap')." ], "run_options": [ + "{\"grid_stride\": 1, \"n_test\": 0, \"n_train\": 6, \"subset_q\": \"0.5,0.9,0.99\"}", "{\"grid_stride\": 2, \"n_test\": 3, \"n_train\": 6, \"subset_q\": \"0.5,0.9,0.99\"}" ], "test_seeds_run": [ 500, 501, - 502 + 502, + 503, + 504, + 505 ], "calibration": { "data_kind": "simulated", @@ -415,3910 +566,5365 @@ }, "policies": { "arima/q0.5": { - "n_seeds": 3, + "n_seeds": 6, "seeds": [ 500, 501, - 502 + 502, + 503, + 504, + 505 ], "gpu_hours": { - "mean": 1572.0124189669048, - "lo": 1540.4753411892173, - "hi": 1606.8559843696955, - "n": 3 + "mean": 1595.8812110681545, + "lo": 1550.630925738383, + 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"gpu_hours": 1404.179285626771, + "gpu_hours_multisite_jobs": 200.1213639063208, + "value_usd": 3721.6091530427984, + "jobs_admitted": 42, + "jobs_completed": 47, + "violation_rate": 0.14583333333333334, + "violation_rate_by_site": [ + 0.14583333333333334, + 0.0 + ], + "energy_above_cap_kwh": 5.785176614442549, + "energy_above_cap_kwh_by_site": [ + 5.785176614442552, + 0.0 + ], + "seconds_above_cap": 4580.0, + "max_excess_w": 14014.924079118966, + "stranded_kwh": 153.54422913967645, + "stranded_intervals": 192, + "mean_power_over_cap": 0.9259845637277269, + "energy_kwh": 1877.4343962185796, + "forecast_calls": 192, + "forecast_refresh_bins": 1, + "job_faults": 2 } } } From b50fd57ed1c79a3364ea5fd28c6341bc33eb010b Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 18:48:48 -0700 Subject: [PATCH 34/52] e003: experiment README (outage v2 pending) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- experiments/e003-heatwave/README.md | 41 +++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 experiments/e003-heatwave/README.md diff --git a/experiments/e003-heatwave/README.md b/experiments/e003-heatwave/README.md new file mode 100644 index 0000000..42690fe --- /dev/null +++ b/experiments/e003-heatwave/README.md @@ -0,0 +1,41 @@ +# e003 Heatwave: forecast-driven power and thermal decisions + +Experiment e003 adds power and heat to the lab. A simulated two-site cluster (the e001 sites, site-a and site-b) runs training jobs whose GPUs draw power in the e002 shape: steady compute, a dip while workers wait on communication, a dip at every checkpoint. Each rack heats up through a lumped thermal model, cooling answers with a lag, breakers trip, the grid curtails, GPUs fail. Three decisions then act on 21-quantile forecasts from Mistral's Timestral (Oct 2026) and from classical baselines, against reacting without a forecast, against the e001 averaging rule, and against perfect foresight: + +1. **Power-cap packing** (`run_packing.py`, `results/packing-v1.json`): admit queued jobs while the planned site power, a chosen forecast quantile, stays under the cap; a learned policy picks the quantile per situation. +2. **Outage response** (`run_outage.py`, `results/outage-v1.json`, PENDING v2): warn when the forecast probability of trouble within the horizon is high, checkpoint early, then move the work to the other site or pause it. +3. **Cost timing** (`run_cost.py`, `results/cost-v1.json`): shift flexible work into cheaper and cleaner hours of real public price and carbon series; trace dollars per token against carbon per token. + +The forecasting studies, the measured laptop runs and the public data live in the companion repository Heatwave (github.com/Cuuper22/heatwave once published; it imports this branch for the simulator). + +## What is simulated, what is measured, what is public + +Everything the simulator outputs is **simulated**. Its inputs are labeled in each scenario's `assumptions` list and in every run's metadata: + +- GPU power levels (`sim-scenario-v2.json` onward) are calibrated on **public** H100 SXM telemetry (Elsayed et al. 2026, Scientific Data 13:1268, CC BY 4.0): compute at 0.855 of the 700 W limit, idle at 0.213 of compute, the communication dip at 0.249 of compute for 14% of a step, and a GPU-to-air temperature offset of 0.0256 K/W (medians over 72 GPU-sessions; `make_scenario_v2.py` copies them from the Heatwave calibration result and records its sha256). The checkpoint level is a **measured** laptop ratio (0.612, NVML energy counter). The first scenario (v1) used laptop ratios from e002 for idle power, which put idle at 0.764 of compute (`results/sim-smoke-v1.json`); v2 replaced them. +- `sim-scenario-v3.json` equals v2 except that the job schedule and the synthetic weather follow the run seed (in v2 every seed shared one schedule, which made seed intervals too narrow). Final packing numbers use v3. +- Thermal throttle thresholds, breaker curves, cooling lags and event rates are scenario assumptions with no default numbers in code; event precursors (a supply temperature that creeps up before a cooling failure, a GPU that runs hot before it dies) are assumptions of the simulation, and every event type also occurs with no precursor. +- Cost timing uses **public** CAISO and DE-LU day-ahead prices and carbon intensity directly, with a modeled workload from `gpu_stack.calculator`. + +## Files + +| file | what | +|---|---| +| `gpu_stack/research/heatwave_thermal.py` | lumped RC rack/GPU thermal model with exact integration, least-squares fit, throttle and cooling-power models | +| `gpu_stack/research/heatwave_events.py` | cooling failures, breaker trips, grid curtailments, hardware faults, with and without precursors | +| `gpu_stack/research/heatwave_sim.py` | the cluster simulator: sites (multisite `Site`), racks, GPUs, jobs, controller hook, multi-scale outputs that add up across GPU, rack and site | +| `gpu_stack/research/heatwave_packing.py`, `heatwave_outage.py`, `heatwave_cost.py` | the three decisions | +| `gpu_stack/scopes/power_forecast*.py` | the calculator scope: planning at a quantile, stranded power, RC thermal closed forms, early-checkpoint value, time shifting | +| `sim-scenario-v{1,2,3}.json` | simulator scenarios | +| `{packing,outage,cost}-scenario-v1.json` | decision scenarios | +| `results/` | result files with provenance (script and module sha256, git commit, seeds, hardware) | + +## Results + +Numbers, intervals and caveats are in each result file; a curated list with file and key for every number is `results/HEADLINE.md` in the Heatwave repository. In short (all simulated except cost timing, which uses public price and carbon): + +- Packing, 6 held-out seeds: reacting to measured power packs 1,841 GPU-hours with 42.5 kWh above the cap; planning on Timestral-5M's 90th percentile packs 1,531 GPU-hours with 7.1 kWh above it. The learned quantile policy beats reacting by $747 per episode at an over-cap penalty of $40/kWh and by $5,575 at $160/kWh, and loses by $295 at $2.5/kWh. +- Outage response: PENDING (v2 with the per-GPU precursor channel). +- Cost timing, CAISO, last 26 weeks: Timestral-28M cuts the electricity cost of a 30% flexible share by 8.6% (perfect foresight 9.0%, seasonal naive 8.3%) and total cost per token by 0.22%. + +Run commands are in each `run_*.py` docstring. From 47495c7d4105837c35633abf371f9eb274a220bb Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 19:28:10 -0700 Subject: [PATCH 35/52] README and site: electricity share of the example run is about 7.4 percent after the cost audit (was about 6), with the regional range and a link to the correction note Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- README.md | 14 +++++++------- docs/data/explainers.json | 2 +- docs/index.html | 2 +- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 00ec27a..40d6291 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ The easiest way in is the site, which tells the story with pictures you can play **The share of speed you actually get is the one people underestimate.** The spec sheet number is a ceiling. Meta reports about 40 percent for Llama 3, and that counts as good. -**Electricity is a small slice.** For a 7 billion parameter model trained on 2 trillion tokens on 1,024 H100s, power and cooling come to about 6 percent of the cost. The rest is expensive hardware gradually becoming old hardware. +**Electricity is a small slice, though not as small as I first wrote.** For a 7 billion parameter model trained on 2 trillion tokens on 1,024 H100s, power and cooling come to about 7.4 percent of the cost. Earlier versions of this page said about 6 percent; an audit against 2026 prices, measured server power and a separate term for the building moved it ([what changed and why](experiments/e003-heatwave/cost_audit/CORRECTION.md)). At the industrial electricity prices of different places the same run ranges from about 5 percent (New Mexico) to about 16 percent (California). The rest is expensive hardware gradually becoming old hardware. **How the chip is made doesn't change the bill.** The map once went down to lithography, atoms and even quarks. All of it reaches the cost through one number, how fast the GPU is, and that number is already on the spec sheet. I removed the quarks and kept a short layer on how chips are printed. @@ -33,14 +33,14 @@ gpu-stack estimate --params 7e9 --tokens 2e12 --gpu H100-SXM --gpus 1024 ```text 7 billion parameters, 2 trillion tokens, 1,024 x H100-SXM -(MFU 40%, PUE 1.2, electricity $0.0813/kWh) +(MFU 40%, PUE 1.2, electricity $0.0851/kWh) Training time 2.4 days GPU-hours 58,958 - Energy 49.5 MWh - Electricity cost $4,026 - Hardware cost (amortized) $67,090 - Total cost $71,116 - Cost per million tokens $0.0356 + Energy 67.8 MWh + Electricity cost $5,766 + Hardware cost (amortized) $71,910 + Total cost $77,675 + Cost per million tokens $0.0388 ``` Add `--explain` to see what each number is made of, down to the equation, and whether each input was something you entered, a cited hardware spec, or an assumption. The same from Python: diff --git a/docs/data/explainers.json b/docs/data/explainers.json index 11dc9a2..16c97a5 100644 --- a/docs/data/explainers.json +++ b/docs/data/explainers.json @@ -147,7 +147,7 @@ "id": "electricity", "term": "electricity", "title": "Electricity", - "body": "Power drawn times hours times the price per kilowatt-hour, multiplied by the building's PUE because the cooling is on the same bill. In the 7 billion parameter example on this page it comes to about 6 percent of the cost. The hardware is the expensive part.", + "body": "Power drawn times hours times the price per kilowatt-hour, multiplied by the building's PUE because the cooling is on the same bill. In the 7 billion parameter example on this page it comes to about 7.4 percent of the cost. The hardware is the expensive part.", "visual": "pue", "review": "edited" }, diff --git a/docs/index.html b/docs/index.html index 86baf07..feb94f8 100644 --- a/docs/index.html +++ b/docs/index.html @@ -115,7 +115,7 @@

    What actually moves the bill?

    Once everything was connected, most of the map turned out to collapse into one short line: the math per token, divided by how fast your GPUs really run, times what an hour of GPU costs you. The chip's speed on paper, the share of that speed you actually get, and the size of the model move the cost by the same amount, because each one shows up in that line exactly once. After those comes how long you keep the hardware before replacing it.

    The share of speed you actually get is called MFU, and it's the one I underestimated. The spec sheet number is a ceiling. Meta reports about 40 percent for Llama 3, and that counts as good.

    -

    Electricity was the surprise. For a 7 billion parameter model trained on 2 trillion tokens with 1,024 H100s, the power bill, cooling included (PUE), comes to about 6 percent of the cost. The rest is expensive hardware gradually becoming old hardware.

    +

    Electricity was the surprise. For a 7 billion parameter model trained on 2 trillion tokens with 1,024 H100s, the power bill, cooling included (PUE), comes to about 7.4 percent of the cost. I first wrote 6 percent; checking every price against 2026 sources, using measured server power and counting the building separately moved it. At the electricity prices of different places the same run goes from about 5 percent (New Mexico) to about 16 percent (California). The rest is expensive hardware gradually becoming old hardware.

     From 6da6e7ea186e3ee16e4fd05d42628db650962967 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 19:29:00 -0700 Subject: [PATCH 36/52] README and site: state which audit rows the regional range comes from (calculator defaults, 5.5-year life, PUE 1.2); sentence generated by Heatwave's build_headline.py Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- README.md | 2 +- docs/index.html | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 40d6291..e367244 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ The easiest way in is the site, which tells the story with pictures you can play **The share of speed you actually get is the one people underestimate.** The spec sheet number is a ceiling. Meta reports about 40 percent for Llama 3, and that counts as good. -**Electricity is a small slice, though not as small as I first wrote.** For a 7 billion parameter model trained on 2 trillion tokens on 1,024 H100s, power and cooling come to about 7.4 percent of the cost. Earlier versions of this page said about 6 percent; an audit against 2026 prices, measured server power and a separate term for the building moved it ([what changed and why](experiments/e003-heatwave/cost_audit/CORRECTION.md)). At the industrial electricity prices of different places the same run ranges from about 5 percent (New Mexico) to about 16 percent (California). The rest is expensive hardware gradually becoming old hardware. +**Electricity is a small slice, though not as small as I first wrote.** For a 7 billion parameter model trained on 2 trillion tokens on 1,024 H100s, power and cooling come to about 7.4 percent of the cost. Earlier versions of this page said about 6 percent; an audit against 2026 prices, measured server power and a separate term for the building moved it ([what changed and why](experiments/e003-heatwave/cost_audit/CORRECTION.md)). At the industrial electricity prices of different places, with the calculator's other defaults (5.5-year server life, PUE 1.2), the same run ranges from about 5 percent (New Mexico average) to about 16 percent (California average). The rest is expensive hardware gradually becoming old hardware. **How the chip is made doesn't change the bill.** The map once went down to lithography, atoms and even quarks. All of it reaches the cost through one number, how fast the GPU is, and that number is already on the spec sheet. I removed the quarks and kept a short layer on how chips are printed. diff --git a/docs/index.html b/docs/index.html index feb94f8..0e8e6e6 100644 --- a/docs/index.html +++ b/docs/index.html @@ -115,7 +115,7 @@

    What actually moves the bill?

    Once everything was connected, most of the map turned out to collapse into one short line: the math per token, divided by how fast your GPUs really run, times what an hour of GPU costs you. The chip's speed on paper, the share of that speed you actually get, and the size of the model move the cost by the same amount, because each one shows up in that line exactly once. After those comes how long you keep the hardware before replacing it.

    The share of speed you actually get is called MFU, and it's the one I underestimated. The spec sheet number is a ceiling. Meta reports about 40 percent for Llama 3, and that counts as good.

    -

    Electricity was the surprise. For a 7 billion parameter model trained on 2 trillion tokens with 1,024 H100s, the power bill, cooling included (PUE), comes to about 7.4 percent of the cost. I first wrote 6 percent; checking every price against 2026 sources, using measured server power and counting the building separately moved it. At the electricity prices of different places the same run goes from about 5 percent (New Mexico) to about 16 percent (California). The rest is expensive hardware gradually becoming old hardware.

    +

    Electricity was the surprise. For a 7 billion parameter model trained on 2 trillion tokens with 1,024 H100s, the power bill, cooling included (PUE), comes to about 7.4 percent of the cost. I first wrote 6 percent; checking every price against 2026 sources, using measured server power and counting the building separately moved it. At the industrial electricity prices of different places, with the calculator's other defaults (5.5-year server life, PUE 1.2), the same run goes from about 5 percent (New Mexico average) to about 16 percent (California average). The rest is expensive hardware gradually becoming old hardware.

    Try your own numbers in the Calculator

    From 0c74398cab3cfc287b707e3bae1e47cebbc2dd12 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 19:40:30 -0700 Subject: [PATCH 37/52] e003 README: outage v1 and cost audit results Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- experiments/e003-heatwave/README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/experiments/e003-heatwave/README.md b/experiments/e003-heatwave/README.md index 42690fe..d3b7bb5 100644 --- a/experiments/e003-heatwave/README.md +++ b/experiments/e003-heatwave/README.md @@ -3,7 +3,7 @@ Experiment e003 adds power and heat to the lab. A simulated two-site cluster (the e001 sites, site-a and site-b) runs training jobs whose GPUs draw power in the e002 shape: steady compute, a dip while workers wait on communication, a dip at every checkpoint. Each rack heats up through a lumped thermal model, cooling answers with a lag, breakers trip, the grid curtails, GPUs fail. Three decisions then act on 21-quantile forecasts from Mistral's Timestral (Oct 2026) and from classical baselines, against reacting without a forecast, against the e001 averaging rule, and against perfect foresight: 1. **Power-cap packing** (`run_packing.py`, `results/packing-v1.json`): admit queued jobs while the planned site power, a chosen forecast quantile, stays under the cap; a learned policy picks the quantile per situation. -2. **Outage response** (`run_outage.py`, `results/outage-v1.json`, PENDING v2): warn when the forecast probability of trouble within the horizon is high, checkpoint early, then move the work to the other site or pause it. +2. **Outage response** (`run_outage.py`, `results/outage-v1.json`; v2 with a per-GPU warning channel pending): warn when the forecast probability of trouble within the horizon is high, checkpoint early, then move the work to the other site or pause it. 3. **Cost timing** (`run_cost.py`, `results/cost-v1.json`): shift flexible work into cheaper and cleaner hours of real public price and carbon series; trace dollars per token against carbon per token. The forecasting studies, the measured laptop runs and the public data live in the companion repository Heatwave (github.com/Cuuper22/heatwave once published; it imports this branch for the simulator). @@ -35,7 +35,8 @@ Everything the simulator outputs is **simulated**. Its inputs are labeled in eac Numbers, intervals and caveats are in each result file; a curated list with file and key for every number is `results/HEADLINE.md` in the Heatwave repository. In short (all simulated except cost timing, which uses public price and carbon): - Packing, 6 held-out seeds: reacting to measured power packs 1,841 GPU-hours with 42.5 kWh above the cap; planning on Timestral-5M's 90th percentile packs 1,531 GPU-hours with 7.1 kWh above it. The learned quantile policy beats reacting by $747 per episode at an over-cap penalty of $40/kWh and by $5,575 at $160/kWh, and loses by $295 at $2.5/kWh. -- Outage response: PENDING (v2 with the per-GPU precursor channel). -- Cost timing, CAISO, last 26 weeks: Timestral-28M cuts the electricity cost of a 30% flexible share by 8.6% (perfect foresight 9.0%, seasonal naive 8.3%) and total cost per token by 0.22%. +- Outage response, v1, 10 held-out seeds, 65 scheduled events (41 with no precursor): reacting delivers 1,722 useful GPU-hours and loses 35.1 to faults; Timestral-5M warnings lose 23.9 (1,727 useful) with no false alarms; perfect foresight loses 3.5; the e001 averaging rule loses 0.7 to faults but delivers 1,267 useful GPU-hours. v2 adds each GPU's own temperature as a warning channel: PENDING. +- Cost timing, CAISO, last 26 weeks: Timestral-28M cuts the electricity cost of a 30% flexible share by 8.6% (perfect foresight 9.0%, seasonal naive 8.3%) and total cost per token by 0.22% (DE-LU: 12.6% and 1.18%). +- Cost audit (`cost_audit/`, `results/cost-audit-v1.json`): the calculator's example run is about 7.4 percent electricity after updating its inputs to cited 2026 figures (it was about 6); across regional industrial prices it ranges from about 5 to about 16 percent. `cost_audit/CORRECTION.md` lists every changed default and its source. Run commands are in each `run_*.py` docstring. From 0c4452b3842d47ac18eac8bf6129413511d494af Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 26 Oct 2026 19:48:23 -0700 Subject: [PATCH 38/52] Site: Heatwave window (packing replay with a quantile slider, outages, cost timing) and a Story chapter on forecasting, all read from e003 results lab-traces-v1.json replays packing test seed 500 for the 21 levels, reactive and the learned policy; every replay matched its committed eval part. Pending results (outage v2, ablation, fine-tuning) show as pending, never as numbers. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01V5UT7N4gZ6MK3NjZ9GGqek --- docs/app.js | 15 +- docs/data/e003-heatwave-lab-v1.json | 1 + docs/data/explainers.json | 56 + docs/heatwave-lab.js | 256 + docs/index.html | 33 + docs/styles/heatwave-lab.css | 54 + .../e003-heatwave/results/lab-traces-v1.json | 141166 +++++++++++++++ experiments/e003-heatwave/run_lab_traces.py | 147 + scripts/build_heatwave_lab_data.py | 181 + tests/test_heatwave_lab.mjs | 53 + 10 files changed, 141960 insertions(+), 2 deletions(-) create mode 100644 docs/data/e003-heatwave-lab-v1.json create mode 100644 docs/heatwave-lab.js create mode 100644 docs/styles/heatwave-lab.css create mode 100644 experiments/e003-heatwave/results/lab-traces-v1.json create mode 100644 experiments/e003-heatwave/run_lab_traces.py create mode 100644 scripts/build_heatwave_lab_data.py create mode 100644 tests/test_heatwave_lab.mjs diff --git a/docs/app.js b/docs/app.js index 804fc5b..f043137 100644 --- a/docs/app.js +++ b/docs/app.js @@ -1,4 +1,4 @@ -// CuperOS desktop: two windows (Story, Calculator) and a link to the Lab. +// CuperOS desktop: three windows (Story, Calculator, Heatwave) and a link to the Lab. // On phones the windows become plain full-width pages with a tab bar on top. (() => { "use strict"; @@ -31,6 +31,16 @@ } } + function mountHeatwave() { + const root = document.getElementById("heatwave-root"); + if (!root || root.dataset.mounted) return; + const api = window.GPUStackHeatwave; + if (api && typeof api.mount === "function") { + root.dataset.mounted = "1"; + api.mount(root); + } + } + function refresh() { tasks.forEach((button) => { const name = button.dataset.task; @@ -57,6 +67,7 @@ zCounter += 1; node.style.zIndex = String(zCounter); if (name === "calculator") mountCalculator(); + if (name === "heatwave") mountHeatwave(); refresh(); if (options.updateHash !== false && window.history && window.history.replaceState) { const hash = name === "story" && !window.location.hash ? "" : `#${name}`; @@ -118,7 +129,7 @@ } else { windows.story.hidden = false; open("story", { updateHash: false }); - if (wanted === "calculator") open("calculator", { updateHash: false }); + if (wanted === "calculator" || wanted === "heatwave") open(wanted, { updateHash: false }); } refresh(); } diff --git a/docs/data/e003-heatwave-lab-v1.json b/docs/data/e003-heatwave-lab-v1.json new file mode 100644 index 0000000..a313c4e --- /dev/null +++ b/docs/data/e003-heatwave-lab-v1.json @@ -0,0 +1 @@ +{"schema":"gpu-stack.e003-heatwave-lab.v1","note":"Generated by scripts/build_heatwave_lab_data.py from committed result files. 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hourly means, origins from 2026-05-01","wql":{"timestral-5M":0.1882,"timestral-28M":0.1853,"seasonal-naive":0.2476,"ets":0.2953,"ets-flat":0.2232,"arima":0.2841}},"sim_site_power_1h":{"data_kind":"simulated","description":"gpu_stack heatwave simulator sim_v3, TEST seeds, site_mean_power_w_1h","wql":{"timestral-5M":0.1252,"timestral-28M":0.1254,"seasonal-naive":0.1647,"ets":0.1335,"ets-flat":0.1389,"arima":0.1426}}}},"ablation":{"status":"pending"},"ablation_real":{"status":"pending"},"finetune":{"status":"pending"}} \ No newline at end of file diff --git a/docs/data/explainers.json b/docs/data/explainers.json index 16c97a5..6ee9a2b 100644 --- a/docs/data/explainers.json +++ b/docs/data/explainers.json @@ -410,6 +410,62 @@ "agents" ], "review": "edited" + }, + { + "id": "quantile-forecast", + "term": "quantile forecast", + "title": "Quantile forecast", + "body": "A forecast that gives a range instead of one number: the level the value stays under 1 percent of the time, 5 percent, 10 percent and so on up to 99. Planning on the 90th means you expect to be caught short about one time in ten.", + "visual": "rule", + "aliases": [ + "quantile forecasts", + "percentile forecast" + ], + "review": "draft" + }, + { + "id": "timestral", + "term": "Timestral", + "title": "Timestral", + "body": "An open forecasting model released by Mistral in October 2026. It reads a long history of numbers, optionally with things known in advance such as a schedule, and returns 21 quantiles for each future step. It runs on an ordinary CPU.", + "visual": "agents", + "aliases": [], + "review": "draft" + }, + { + "id": "thermal-throttling", + "term": "thermal throttling", + "title": "Thermal throttling", + "body": "When a chip gets too hot it slows its own clock to make less heat. The work still gets done, just slower, and the power bill per unit of work goes up. My laptop's GPU starts doing this around 84 degrees.", + "visual": "power", + "aliases": [ + "throttles itself", + "throttling" + ], + "review": "draft" + }, + { + "id": "power-cap", + "term": "power cap", + "title": "Power cap", + "body": "The most power a site may draw, set by its grid contract, its transformers or its cooling. Going over can mean penalty fees, a tripped breaker or overheated racks, so operators leave room under it, and room left unused is capacity paid for and wasted.", + "visual": "power", + "aliases": [ + "power caps", + "site power cap" + ], + "review": "draft" + }, + { + "id": "carbon-intensity", + "term": "carbon intensity", + "title": "Carbon intensity", + "body": "Grams of carbon dioxide emitted per kilowatt-hour of electricity on the grid at a given hour. It moves with the weather and the time of day, because sun and wind come and go and gas plants fill the gaps.", + "visual": "costpertoken", + "aliases": [ + "grid carbon" + ], + "review": "draft" } ] } diff --git a/docs/heatwave-lab.js b/docs/heatwave-lab.js new file mode 100644 index 0000000..114247e --- /dev/null +++ b/docs/heatwave-lab.js @@ -0,0 +1,256 @@ +/* Heatwave Lab window. Reads data/e003-heatwave-lab-v1.json (generated by + scripts/build_heatwave_lab_data.py from committed e003 results) and lets the + reader drag the forecast quantile a packing controller plans around, then + replays that simulated episode: site power against the cap, racks warming, + and the counters for that choice. Nothing here computes a result; every + number shown is read from the data file. Results not in yet say so. */ +(function (root) { + "use strict"; + var NS = "http://www.w3.org/2000/svg"; + var DATA_URL = "data/e003-heatwave-lab-v1.json"; + + function h(tag, attrs, kids) { + var n = document.createElement(tag); + for (var k in attrs || {}) { + if (k === "text") n.textContent = attrs[k]; + else if (k.slice(0, 2) === "on") n.addEventListener(k.slice(2), attrs[k]); + else if (attrs[k] !== null && attrs[k] !== undefined) n.setAttribute(k, attrs[k]); + } + (kids || []).forEach(function (c) { if (c) n.append(c); }); + return n; + } + function s(tag, attrs, kids) { + var n = document.createElementNS(NS, tag); + for (var k in attrs || {}) if (attrs[k] !== null && attrs[k] !== undefined) n.setAttribute(k, attrs[k]); + (kids || []).forEach(function (c) { if (c) n.append(c); }); + return n; + } + function fmt(x, d) { + if (x === null || x === undefined || isNaN(x)) return "n/a"; + return Number(x).toLocaleString("en-US", { minimumFractionDigits: d || 0, maximumFractionDigits: d || 0 }); + } + function pct(x, d) { return x === null || x === undefined ? "n/a" : fmt(100 * x, d === undefined ? 0 : d) + "%"; } + function ci(m, d) { + if (!m) return "n/a"; + return fmt(m.mean, d) + " [" + fmt(m.lo, d) + ", " + fmt(m.hi, d) + "]"; + } + function pending(text) { return h("p", { class: "hwl-pending", text: text || "Result pending." }); } + + function levelLabel(q) { + var p = Math.round(q * 100); + var suf = p % 10 === 1 && p !== 11 ? "st" : p % 10 === 2 && p !== 12 ? "nd" : p % 10 === 3 && p !== 13 ? "rd" : "th"; + return p + suf; + } + + /* ---------------- packing ---------------- */ + function packingPane(P) { + if (!P || P.status !== "ready") return pending("The packing result is not in yet."); + var levels = P.quantile_levels; + var penalties = P.penalties_usd_per_kwh; + var tr = P.trace; + var state = { i: levels.indexOf(0.9) >= 0 ? levels.indexOf(0.9) : 18, pen: penalties[2] || penalties[0], t: 0, playing: false }; + var nT = tr.runs.reactive.site_power_kw.length; + + var allTemps = []; + Object.keys(tr.runs).forEach(function (k) { + tr.runs[k].rack_max_gpu_c.forEach(function (row) { row.forEach(function (v) { if (v !== null) allTemps.push(v); }); }); + }); + var tMin = Math.min.apply(null, allTemps), tMax = Math.max.apply(null, allTemps); + var pMax = 0; + Object.keys(tr.runs).forEach(function (k) { + tr.runs[k].site_power_kw.forEach(function (row) { row.forEach(function (v) { if (v > pMax) pMax = v; }); }); + }); + pMax = Math.max(pMax, Math.max.apply(null, tr.site_caps_kw)) * 1.05; + + var label = h("label", { class: "hwl-label", for: "hwl-q" }); + var slider = h("input", { type: "range", id: "hwl-q", min: "0", max: String(levels.length - 1), step: "1", value: String(state.i), + "aria-describedby": "hwl-q-help" }); + var penSel = h("select", { id: "hwl-pen", "aria-label": "Price of going over the cap" }, + penalties.map(function (p) { return h("option", { value: String(p), text: "$" + p + " per kWh over the cap" }); })); + penSel.value = String(state.pen); + var play = h("button", { type: "button", class: "hwl-btn", text: "Play" }); + + var W = 640, H = 220, L = 46, R = 10, T = 12, B = 30; + var svg = s("svg", { viewBox: "0 0 " + W + " " + H, class: "hwl-chart", role: "img", + "aria-label": "Site power over the scored window against each site's power cap" }); + function x(i) { return L + (W - L - R) * i / (nT - 1); } + function y(v) { return T + (H - T - B) * (1 - v / pMax); } + for (var g = 0; g <= 4; g++) { + var gv = pMax * g / 4; + svg.append(s("line", { x1: L, x2: W - R, y1: y(gv), y2: y(gv), class: "hwl-grid" })); + var tx = s("text", { x: L - 6, y: y(gv) + 4, class: "hwl-axis", "text-anchor": "end" }); tx.textContent = fmt(gv); svg.append(tx); + } + var xl = s("text", { x: W - R, y: H - 6, class: "hwl-axis", "text-anchor": "end" }); + xl.textContent = "hours into the scored window, " + fmt(nT * tr.bin_s / 3600) + " h"; svg.append(xl); + var yl = s("text", { x: 4, y: 10, class: "hwl-axis" }); yl.textContent = "kW"; svg.append(yl); + var caps = tr.site_caps_kw.map(function (c, k) { + var ln = s("line", { x1: L, x2: W - R, y1: y(c), y2: y(c), class: "hwl-cap hwl-s" + k }); + svg.append(ln); + return ln; + }); + var lines = tr.site_ids.map(function (sid, k) { var p = s("polyline", { class: "hwl-line hwl-s" + k, fill: "none" }); svg.append(p); return p; }); + var cursor = s("line", { y1: T, y2: H - B, class: "hwl-cursor" }); svg.append(cursor); + + var racks = h("div", { class: "hwl-racks", role: "group", "aria-label": "Racks, colored by their hottest GPU" }); + var rackCells = tr.rack_ids.map(function (rid) { + var temp = h("span", { class: "hwl-rack-temp" }); + var cell = h("div", { class: "hwl-rack" }, [h("span", { class: "hwl-rack-id", text: rid }), temp]); + racks.append(cell); + return { cell: cell, temp: temp }; + }); + + var counters = h("table", { class: "hwl-table" }); + var best = h("p", { class: "hwl-note" }); + + function policyId() { return "timestral-5M/q" + String(levels[state.i]).replace(/^0\./, "0."); } + function key(q) { + var str = String(q); + return "timestral-5M/q" + str; + } + function heat(v) { + if (v === null || v === undefined) return "var(--chrome-well)"; + var f = Math.max(0, Math.min(1, (v - tMin) / Math.max(1e-6, tMax - tMin))); + return "oklch(" + (0.86 - 0.28 * f).toFixed(3) + " " + (0.04 + 0.16 * f).toFixed(3) + " " + (95 - 70 * f).toFixed(1) + ")"; + } + function draw() { + var q = levels[state.i]; + label.textContent = "Plan around the " + levelLabel(q) + " percentile of the Timestral power forecast"; + var run = tr.runs[key(q)]; + lines.forEach(function (ln, k) { + if (!run) { ln.setAttribute("points", ""); return; } + ln.setAttribute("points", run.site_power_kw.map(function (row, i) { return x(i).toFixed(1) + "," + y(row[k]).toFixed(1); }).join(" ")); + }); + cursor.setAttribute("x1", x(state.t)); cursor.setAttribute("x2", x(state.t)); + rackCells.forEach(function (rc, k) { + var v = run ? run.rack_max_gpu_c[state.t][k] : null; + rc.cell.style.background = heat(v); + rc.temp.textContent = v === null || v === undefined ? "off" : fmt(v, 1) + " °C"; + }); + var pen = String(state.pen); + var rows = [["Plan around the " + levelLabel(q) + " percentile", P.policies[key(q)]], + ["React to measured power", P.policies.reactive], + ["Learned quantile choice", P.policies["timestral-5M/learned@" + pen]]]; + counters.replaceChildren(h("thead", {}, [h("tr", {}, ["policy", "GPU-hours delivered", "energy over the cap (kWh)", "intervals over the cap", "net value at this price ($)"].map(function (t) { return h("th", { text: t }); }))])); + var tb = h("tbody"); + rows.forEach(function (r) { + var m = r[1]; + tb.append(h("tr", {}, [h("td", { text: r[0] }), h("td", { text: m ? ci(m.gpu_hours, 0) : "n/a" }), + h("td", { text: m ? ci(m.energy_above_cap_kwh, 1) : "n/a" }), h("td", { text: m ? pct(m.violation_rate.mean) : "n/a" }), + h("td", { text: m && m.net_value_usd && m.net_value_usd[pen] ? ci(m.net_value_usd[pen], 0) : "n/a" })])); + }); + counters.append(tb); + var b = P.best_fixed_by_penalty[pen]; + best.textContent = "At $" + pen + " per kWh over the cap, the fixed choice with the most net value on the test seeds was " + + (b ? b.replace("/q", ", planning at the ") .replace(/(0\.\d+|\d+)$/, function (m) { return levelLabel(Number(m)) + " percentile"; }) : "n/a") + "."; + } + var timer = null; + function stepPlay() { + state.t = (state.t + 1) % nT; + draw(); + } + play.addEventListener("click", function () { + state.playing = !state.playing; + play.textContent = state.playing ? "Pause" : "Play"; + if (state.playing) timer = root.setInterval(stepPlay, 120); else root.clearInterval(timer); + }); + slider.addEventListener("input", function () { state.i = Number(slider.value); draw(); }); + penSel.addEventListener("change", function () { state.pen = Number(penSel.value); draw(); }); + draw(); + return h("div", { class: "hwl-pane" }, [ + h("p", { class: "hwl-caption", text: "Simulated. Two sites, test seed " + tr.seed + " shown; the table gives means and 95% intervals over " + + P.test_seeds.length + " test seeds. Planning lower packs more jobs and goes over the cap more often." }), + h("div", { class: "hwl-controls" }, [label, slider, h("span", { id: "hwl-q-help", class: "hwl-help", text: "1st to 99th percentile, 21 steps" }), penSel, play]), + svg, racks, counters, best, + ]); + } + + /* ---------------- outage ---------------- */ + function outagePane(O) { + if (!O || O.status !== "ready") return pending(); + var box = h("div", { class: "hwl-pane" }, [h("p", { class: "hwl-caption", text: "Simulated. Warn before trouble, checkpoint early, then move or pause the work. Means over the held-out seeds." })]); + ["v2", "v1"].forEach(function (ver) { + var V = O.versions[ver]; + box.append(h("h3", { class: "hwl-h", text: ver === "v2" ? "With each GPU's own temperature as a warning sign" : "First version (rack-level warning signs)" })); + if (!V || V.status !== "ready") { box.append(pending()); return; } + var t = h("table", { class: "hwl-table" }, [h("thead", {}, [h("tr", {}, ["policy", "useful GPU-hours", "GPU-hours lost to faults", "events warned in time", "false alarms per day"].map(function (x) { return h("th", { text: x }); }))])]); + var tb = h("tbody"); + Object.keys(V.policies).forEach(function (k) { + var p = V.policies[k]; + tb.append(h("tr", {}, [h("td", { text: p.label }), h("td", { text: fmt(p.useful_gpu_h, 0) }), h("td", { text: fmt(p.lost_gpu_h, 1) }), + h("td", { text: p.recall === null ? "n/a" : pct(p.recall) }), h("td", { text: p.false_alarms_per_day === null ? "n/a" : fmt(p.false_alarms_per_day, 1) })])); + }); + t.append(tb); + box.append(t); + }); + return box; + } + + /* ---------------- timing ---------------- */ + var NAMES = { no_shift: "no shifting", reactive: "react to today's price", seasonal_naive_24: "yesterday's prices", ets_24: "ETS", + arima_seasonal: "ARIMA", timestral_28M: "Timestral 28M", perfect: "perfect foresight", offline_optimum: "best possible schedule" }; + function timingPane(C) { + if (!C || C.status !== "ready") return pending(); + var pair = "caiso"; + var holder = h("div"); + var seg = h("div", { class: "hwl-tabs", role: "tablist", "aria-label": "Grid region" }); + function render() { + var P = C.pairs[pair]; + var pols = Object.keys(P.curves); + var xs = [], ys = []; + pols.forEach(function (k) { xs = xs.concat(P.curves[k].electricity_usd_per_mtoken); ys = ys.concat(P.curves[k].co2_g_per_mtoken); }); + var x0 = Math.min.apply(null, xs), x1 = Math.max.apply(null, xs), y0 = Math.min.apply(null, ys), y1 = Math.max.apply(null, ys); + var W = 640, H = 260, L = 70, R = 150, T = 12, B = 36; + function X(v) { return L + (W - L - R) * (v - x0) / Math.max(1e-12, x1 - x0); } + function Y(v) { return T + (H - T - B) * (1 - (v - y0) / Math.max(1e-12, y1 - y0)); } + var svg = s("svg", { viewBox: "0 0 " + W + " " + H, class: "hwl-chart", role: "img", "aria-label": "Electricity dollars per million tokens against grams of CO2 per million tokens, for each scheduling policy" }); + var xl = s("text", { x: L, y: H - 6, class: "hwl-axis" }); xl.textContent = "electricity, $ per million tokens"; svg.append(xl); + var yl = s("text", { x: 4, y: 10, class: "hwl-axis" }); yl.textContent = "g CO2 per million tokens"; svg.append(yl); + pols.forEach(function (k, j) { + var c = P.curves[k]; + var pts = c.electricity_usd_per_mtoken.map(function (v, i) { return X(v).toFixed(1) + "," + Y(c.co2_g_per_mtoken[i]).toFixed(1); }); + svg.append(s("polyline", { points: pts.join(" "), class: "hwl-pareto hwl-p" + (j % 8), fill: "none" })); + var last = pts.length - 1; + var t = s("text", { x: W - R + 6, y: T + 14 + j * 16, class: "hwl-axis hwl-p" + (j % 8) + "-t" }); t.textContent = NAMES[k] || k; svg.append(t); + }); + var mid = P.curves.timestral_28M ? Math.floor(P.curves.timestral_28M.lambda.length / 2) : null; + var txt = mid === null ? "" : "Halfway between cheapest and cleanest, Timestral 28M cuts the electricity cost of the flexible work by " + + fmt(P.curves.timestral_28M.saving_pct_electricity[mid], 1) + "% and its carbon by " + fmt(P.curves.timestral_28M.saving_pct_co2[mid], 1) + + "%; the whole cost per token moves by " + fmt(P.curves.timestral_28M.saving_pct_total[mid], 2) + "%."; + holder.replaceChildren(svg, h("p", { class: "hwl-note", text: txt })); + } + ["caiso", "de"].forEach(function (p) { + var b = h("button", { type: "button", class: "hwl-btn", role: "tab", text: p === "caiso" ? "California (CAISO)" : "Germany (DE-LU)" }); + b.addEventListener("click", function () { pair = p; render(); }); + seg.append(b); + }); + render(); + return h("div", { class: "hwl-pane" }, [ + h("p", { class: "hwl-caption", text: "Public day-ahead prices and carbon intensity, last 26 weeks held out; 30% of the work may wait up to 24 hours. Each line sweeps from cheapest to cleanest." }), + seg, holder]); + } + + function mount(el) { + el.replaceChildren(h("p", { class: "hwl-pending", text: "Loading the Heatwave results." })); + fetch(DATA_URL).then(function (r) { return r.json(); }).then(function (D) { + var panes = { packing: packingPane(D.packing), outage: outagePane(D.outage), timing: timingPane(D.cost) }; + var names = { packing: "Pack jobs under a cap", outage: "Outages", timing: "Cheaper, cleaner hours" }; + var tabs = h("div", { class: "hwl-tabs", role: "tablist", "aria-label": "Heatwave experiments" }); + var body = h("div", { class: "hwl-body" }); + function show(k) { + body.replaceChildren(panes[k]); + Array.prototype.forEach.call(tabs.children, function (b) { b.setAttribute("aria-selected", String(b.dataset.k === k)); }); + } + Object.keys(panes).forEach(function (k) { + var b = h("button", { type: "button", role: "tab", class: "hwl-btn", "data-k": k, text: names[k] }); + b.addEventListener("click", function () { show(k); }); + tabs.append(b); + }); + el.replaceChildren(h("div", { class: "hwl" }, [tabs, body])); + show("packing"); + }).catch(function () { + el.replaceChildren(h("p", { class: "hwl-pending", text: "The Heatwave data file could not be loaded." })); + }); + } + + root.GPUStackHeatwave = { mount: mount }; +})(typeof window !== "undefined" ? window : globalThis); diff --git a/docs/index.html b/docs/index.html index 0e8e6e6..e7bbef2 100644 --- a/docs/index.html +++ b/docs/index.html @@ -28,7 +28,9 @@ + + @@ -46,6 +48,7 @@ @@ -59,6 +62,10 @@ Calculator + + + Heatwave + Lab @@ -153,6 +160,17 @@

    Can a training run survive flaky datacenters?

    Open the Lab

    +
    +

    What is a forecast worth to a GPU cluster?

    +

    A training run doesn't draw power like a kettle. Inside every step the GPUs compute, then wait on each other, then compute again, and every save is a dip. Over hours, jobs start and stop. The building heats up and the cooling answers late. The grid's price and carbon swing on their own schedule. I wanted to know what a forecast of all that is worth when you act on it, so I built a small project called Heatwave on top of this one.

    +

    A quantile forecast doesn't give one number. It gives 21: the level the power stays under 1 percent of the time, 5 percent, 10 percent and so on up to 99. Plan on the 90th and you expect to be caught short one time in ten. My forecaster was Timestral, the open forecasting model Mistral released in October 2026, used as it comes. On all eleven series I tried, from one-second GPU power to hourly grid prices, it had lower forecast loss than the textbook methods. On H100 power sampled every second, its loss was 0.158 against 0.259 for the best of them.

    +

    My laptop's GPU throttles itself from about 84 degrees and roughly halves its clock when it does. In a test run, a 30-second temperature forecast warned before 6 of the 7 slowdowns, 18.5 seconds ahead on median, where watching the thermometer gave 10.5 seconds. Plain statistics did just as well as Timestral here. Thirty seconds isn't long enough for a big model to matter.

    +

    The rest happened in a simulated two-site cluster with a power cap, with power levels taken from published H100 measurements. Letting jobs in whenever the meter shows room packs the most work and spends 72 percent of the time over the cap. Planning on the forecast's 90th percentile packs about 17 percent less work and goes over the cap with about a sixth of the energy. Which percentile is right depends on what going over costs: a small learned rule that picks it as it goes was worth $747 more per run than reacting when each kilowatt-hour over the cap costs $40, and $295 less when it costs $2.50.

    +

    Moving the flexible part of the work to cheaper, cleaner hours of real California prices cut its electricity bill by 8.6 percent and its carbon by 10.8 percent, close to the 9.0 percent perfect foresight gets. Copying yesterday's prices got 8.3 percent, so the daily price pattern does most of the work. The whole bill moved by 0.22 percent, because hardware is most of it.

    + +

    Open the Heatwave window

    +
    +

    How did I build it?

    @@ -180,6 +198,20 @@

    How did I build it?

    + +
    @@ -187,6 +219,7 @@

    How did I build it?

    Start + Lab
    12:00 PM
    diff --git a/docs/styles/heatwave-lab.css b/docs/styles/heatwave-lab.css new file mode 100644 index 0000000..aa801c2 --- /dev/null +++ b/docs/styles/heatwave-lab.css @@ -0,0 +1,54 @@ +/* Heatwave Lab window: CuperOS chrome, zero radius, Plex for reading, Plex Mono for numbers. */ +.heatwave-window { top: 56px; right: 24px; width: min(860px, 100% - 240px); height: calc(100% - 90px); } +@media (max-width: 760px) { + .heatwave-window { position: static; width: 100%; height: auto; border: 0; box-shadow: none; background: var(--window-body); } +} +.hwl { font-family: var(--font-body); color: var(--text-dark); font-size: 15px; line-height: 1.55; padding: 12px; } +.hwl-tabs { display: flex; flex-wrap: wrap; gap: 4px; margin: 0 0 10px; } +.hwl-btn { + font: 600 13px/1.2 var(--font-body); min-height: 36px; padding: 6px 12px; border-radius: 0; + background: var(--button-face); color: var(--text-dark); border: 2px outset var(--button-face); cursor: pointer; +} +.hwl-btn:hover { background: var(--button-hover); } +.hwl-btn:focus-visible, .hwl select:focus-visible, .hwl input:focus-visible { outline: 2px solid var(--accent-gold); outline-offset: 1px; } +.hwl-btn[aria-selected="true"] { background: var(--title-bar); color: var(--text-light); border-style: inset; } +.hwl-body { border: 2px inset var(--window-chrome); background: var(--pane-bright); padding: 12px; } +.hwl-caption { margin: 0 0 10px; font-size: 13px; color: var(--text-soft); } +.hwl-controls { display: flex; flex-wrap: wrap; align-items: center; gap: 8px 12px; margin-bottom: 10px; } +.hwl-label { font-weight: 600; flex: 1 1 100%; } +.hwl-controls input[type="range"] { flex: 1 1 260px; accent-color: var(--title-bar); } +.hwl-help { font-size: 13px; color: var(--text-soft); } +.hwl select { font: 500 13px var(--font-body); min-height: 32px; border-radius: 0; border: 2px inset var(--window-chrome); background: var(--pane-bright); } +.hwl-chart { display: block; width: 100%; min-width: 545px; height: auto; 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47.5, + 32.5, + 52.96, + 52.76, + 32.37, + 32.21 + ], + [ + 51.96, + 53.99, + 48.19, + 32.5, + 52.36, + 52.62, + 32.37, + 32.21 + ], + [ + 52.39, + 53.98, + 48.06, + 32.5, + 52.21, + 52.95, + 32.37, + 32.21 + ] + ], + "wall_s": 83.20853328704834 + } + ], + "note": "Replays of packing-v1 test seed 500 with the same code and settings; every replay's metrics were checked against the committed eval part where one exists (matches_committed_part)." + } +} diff --git a/experiments/e003-heatwave/run_lab_traces.py b/experiments/e003-heatwave/run_lab_traces.py new file mode 100644 index 0000000..4156f67 --- /dev/null +++ b/experiments/e003-heatwave/run_lab_traces.py @@ -0,0 +1,147 @@ +"""Replay one packing test episode per policy and keep downsampled traces for the site's Heatwave Lab window. + +SIMULATED data. For test seed 500 of the packing experiment it re-runs, with the +same code and settings as ``run_packing.py eval``: Timestral-5M planning at each +of the 21 quantile levels, the reactive baseline, and the learned quantile policy +at every penalty of the sweep. For each run it keeps, inside the scored window, +5-minute means of site power and busy GPUs, 5-minute maxima of each rack's +hottest GPU and means of rack air temperature, plus the episode metrics, and +checks that the metrics equal the committed eval part for the same policy and +seed (the replay must reproduce the experiment, or the script stops). + +Usage: python experiments/e003-heatwave/run_lab_traces.py [--workers 8] [--seed 500] +Writes experiments/e003-heatwave/results/lab-traces-v1.json. +""" + +from __future__ import annotations + +import argparse +import importlib.util +import json +import math +import sys +from concurrent.futures import ProcessPoolExecutor +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +OUT = HERE / "results" / "lab-traces-v1.json" +BIN_S = 300.0 + + +def _runner(): + spec = importlib.util.spec_from_file_location("run_packing", HERE / "run_packing.py") + mod = importlib.util.module_from_spec(spec) + sys.modules["run_packing"] = mod + spec.loader.exec_module(mod) + return mod + + +def policies(rp, packing): + out = [("reactive", "reactive", None, None)] + for q in rp.hp.QUANTILE_LEVELS: + out.append((rp.policy_id("timestral-5M", f"q{q:g}"), "fixed", "timestral-5M", float(q))) + for lam in packing["penalty"]["sweep_usd_per_kwh"]: + out.append((rp.policy_id("timestral-5M", f"learned@{lam:g}"), "learned", "timestral-5M", float(lam))) + return out + + +def _bin(t_s, values, a, b, how="mean"): + edges = np.arange(a, b + 1e-9, BIN_S) + idx = np.digitize(t_s, edges) - 1 + out = np.full((len(edges) - 1,) + values.shape[1:], np.nan) + for k in range(len(edges) - 1): + m = idx == k + if m.any(): + out[k] = values[m].mean(axis=0) if how == "mean" else values[m].max(axis=0) + return out + + +def one(job): + pid, kind, fcn, arg, seed = job + rp = _runner() + hp = rp.hp + packing, sim, settings = rp.setup() + models = rp.load_models() + use_cov = fcn in packing["policies"]["use_covariates"] if fcn else False + pen = float(packing["penalty"]["default_usd_per_kwh"]) + fc = rp.make_forecaster(fcn, packing, 1) if fcn else None + learned = None + if kind == "learned": + learned = hp.LearnedQ(hp.CostSensitiveModel.from_dict(json.loads(rp.MODEL_PATH.read_text())["result"]["model"])) + + def factory(ep, site_ids): + if kind == "reactive": + return hp.PackingController(ep.jobs, models, site_ids, settings, ep.hour0_local, mode="reactive") + policy = hp.FixedQ(arg) if kind == "fixed" else learned + penalty = float(arg) if kind == "learned" else pen + return hp.PackingController( + ep.jobs, models, site_ids, settings, ep.hour0_local, mode="forecast", forecaster=fc, policy=policy, + penalty_usd_per_kwh=penalty, use_covariates=use_cov, + forecast_refresh_bins=int(packing["policies"]["forecast_refresh_bins"].get(fcn, 1)), + ) + + ep, ctrl, res, wall = rp.run_one(packing, sim, settings, models, seed, factory) + metrics = hp.episode_metrics(res, ctrl, ep, settings) + part = rp.PARTS / "eval" / f"{pid.replace('/', '__').replace('@', '_at_')}__seed{seed}.json" + ref = json.loads(part.read_text())["result"]["metrics"] if part.exists() else None + if ref is not None: + for key in ("gpu_hours", "energy_above_cap_kwh", "violation_rate"): + if not math.isclose(ref[key], metrics[key], rel_tol=1e-6, abs_tol=1e-9): + raise RuntimeError(f"{pid} seed {seed}: replay {key} {metrics[key]} != committed {ref[key]}") + arr = res.to_arrays() + a, b = metrics["window_s"] + dt = float(ep.scenario["base_dt_s"]) + sp = np.asarray(arr["site_power_w"]) + t_sp = (np.arange(sp.shape[0]) + 0.5) * dt + keep = (t_sp >= a) & (t_sp < b) + site_power = _bin(t_sp[keep], sp[keep], a, b) + rt = np.asarray(arr["rack_temp_c_1min"]) + t_rt = (np.arange(rt.shape[0]) + 0.5) * 60.0 + k2 = (t_rt >= a) & (t_rt < b) + rack_air = _bin(t_rt[k2], rt[k2], a, b) + rmax = _bin(t_rt[k2], np.asarray(arr["rack_max_gpu_temp_c_1min"])[k2], a, b, how="max") + P, B = ctrl.minute_records() + t_c = (np.arange(len(B)) + 1.0) * settings.call_s - 0.5 * settings.call_s + k3 = (t_c >= a) & (t_c < b) + busy = _bin(t_c[k3], np.asarray(B)[k3], a, b) if k3.any() else None + return { + "policy": pid, "kind": kind, "seed": seed, "metrics": metrics, "matches_committed_part": ref is not None, + "bin_s": BIN_S, "window_s": [a, b], + "site_power_w": site_power.round(1).tolist(), + "busy_gpus": None if busy is None else busy.round(2).tolist(), + "rack_air_c": rack_air.round(2).tolist(), + "rack_max_gpu_c": rmax.round(2).tolist(), + "wall_s": wall, + } + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--workers", type=int, default=8) + ap.add_argument("--seed", type=int, default=500) + a = ap.parse_args() + rp = _runner() + packing, sim, settings = rp.setup() + models = rp.load_models() + jobs = [(*p, a.seed) for p in policies(rp, packing)] + with ProcessPoolExecutor(max_workers=a.workers) as ex: + runs = list(ex.map(one, jobs)) + site_ids = list(models) + rack_ids = [r["rack_id"] for item in sim["sites"] for r in item["racks"]] + result = { + "data_kind": "simulated", "seed": a.seed, "site_ids": site_ids, "rack_ids": rack_ids, + "site_caps_w": [models[s].cap_w for s in site_ids], + "quantile_levels": list(rp.hp.QUANTILE_LEVELS), + "runs": runs, + "note": ("Replays of packing-v1 test seed 500 with the same code and settings; every replay's metrics were checked " + "against the committed eval part where one exists (matches_committed_part)."), + } + record = {"provenance": rp.part_provenance(seed=a.seed, role="lab-traces"), "result": rp._clean(result)} + rp.write_json(OUT, record) + print(f"wrote {OUT} ({len(runs)} runs)") + + +if __name__ == "__main__": + main() diff --git a/scripts/build_heatwave_lab_data.py b/scripts/build_heatwave_lab_data.py new file mode 100644 index 0000000..2babe5f --- /dev/null +++ b/scripts/build_heatwave_lab_data.py @@ -0,0 +1,181 @@ +"""Build docs/data/e003-heatwave-lab-v1.json for the site's Heatwave window and Story chapter. + +Reads only committed result files and copies numbers from them (no number is +typed here). Optional inputs that do not exist yet become explicit "pending" +entries, which the page shows as "result pending", never as a number. + + packing experiments/e003-heatwave/results/packing-v1.json and lab-traces-v1.json (simulated) + outage experiments/e003-heatwave/results/outage-v1.json, outage-v2.json if present (simulated) + cost experiments/e003-heatwave/results/cost-v1.json (public price and carbon, modeled workload) + audit experiments/e003-heatwave/results/cost-audit-v1.json (published prices, calculator) + heatwave --heatwave PATH: the Heatwave repository checkout, for results/forecast/multiscale.json, + results/ablation/ablation_sim.json and ablation_real.json, results/finetune/*.json if present + +Usage: python scripts/build_heatwave_lab_data.py [--heatwave /home/claude/heatwave] +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +E3 = ROOT / "experiments" / "e003-heatwave" / "results" +OUT = ROOT / "docs" / "data" / "e003-heatwave-lab-v1.json" + + +def load(p: Path): + return json.loads(p.read_text()) if p.exists() else None + + +def src(p: Path, base: Path) -> dict: + return {"file": str(p.relative_to(base)) if p.is_relative_to(base) else str(p), + "sha256": hashlib.sha256(p.read_bytes()).hexdigest()} + + +def r3(x, n=4): + return None if x is None else round(float(x), n) + + +def packing_block() -> dict: + pk, tr = E3 / "packing-v1.json", E3 / "lab-traces-v1.json" + d, t = load(pk), load(tr) + if d is None or t is None: + return {"status": "pending"} + r, tr_r = d["result"], t["result"] + pol = {} + for pid, p in r["policies"].items(): + pol[pid] = {k: {"mean": r3(p[k]["mean"]), "lo": r3(p[k]["lo"]), "hi": r3(p[k]["hi"])} + for k in ("gpu_hours", "energy_above_cap_kwh", "violation_rate", "stranded_kwh", "value_usd") if k in p} + if "net_value_usd" in p: + pol[pid]["net_value_usd"] = {str(k): {"mean": r3(v["mean"]), "lo": r3(v["lo"]), "hi": r3(v["hi"])} + for k, v in p["net_value_usd"].items()} + runs = {} + for run in tr_r["runs"]: + runs[run["policy"]] = { + "site_power_kw": [[r3(v / 1000.0, 2) for v in row] for row in run["site_power_w"]], + "busy_gpus": run["busy_gpus"], + "rack_max_gpu_c": run["rack_max_gpu_c"], + "metrics": {k: r3(run["metrics"][k]) for k in ("gpu_hours", "energy_above_cap_kwh", "violation_rate", "stranded_kwh")}, + "matches_committed_part": run["matches_committed_part"], + } + paired = {pen: {"vs_reactive": {k: r3(v["net_value_difference_usd_vs"]["reactive"][k]) for k in ("mean", "lo", "hi")}} + for pen, v in r["paired_net_value_difference"].items()} + return { + "status": "ready", "data_kind": "simulated", + "sources": [src(pk, ROOT), src(tr, ROOT)], + "test_seeds": r["test_seeds_run"], + "penalties_usd_per_kwh": sorted(float(k) for k in r["paired_net_value_difference"]), + "quantile_levels": tr_r["quantile_levels"], + "policies": pol, + "learned_vs_reactive": paired, + "best_fixed_by_penalty": {k: v["best_fixed_policy_by_mean_net_value_on_test_seeds"] for k, v in r["best_fixed_by_penalty"].items()}, + "trace": {"seed": tr_r["seed"], "site_ids": tr_r["site_ids"], "rack_ids": tr_r["rack_ids"], + "site_caps_kw": [r3(c / 1000.0, 2) for c in tr_r["site_caps_w"]], + "bin_s": tr_r["runs"][0]["bin_s"], "runs": runs}, + } + + +def outage_block() -> dict: + out = {"status": "ready", "data_kind": "simulated", "versions": {}} + for ver in ("v1", "v2"): + p = E3 / f"outage-{ver}.json" + d = load(p) + if d is None: + out["versions"][ver] = {"status": "pending"} + continue + pol = {} + for k, v in d["result"]["policies"].items(): + o = v["overall"] + pol[k] = {"label": v.get("label", k), + "useful_gpu_h": r3(o["useful_gpu_h"]["mean"], 1), "lost_gpu_h": r3(o["lost_gpu_h"]["mean"], 1), + "deficit_gpu_h": r3(o["deficit_gpu_h"]["mean"], 1), + "recall": r3((o.get("recall") or {}).get("value"), 3), + "false_alarms_per_day": r3((o.get("false_alarms_per_day") or {}).get("mean"), 2)} + out["versions"][ver] = {"status": "ready", "source": src(p, ROOT), "policies": pol, + "n_test_seeds": len(d["result"]["seeds"]["test"])} + return out + + +def cost_block() -> dict: + p = E3 / "cost-v1.json" + d = load(p) + if d is None: + return {"status": "pending"} + r = d["result"] + pairs = {} + show = ("no_shift", "reactive", "seasonal_naive_24", "ets_24", "arima_seasonal", "timestral_28M", "perfect", "offline_optimum") + for pair, v in r["pairs"].items(): + curves = {} + for key, c in v["curves"].items(): + pol, f, w, rho, q = key.split("|") + if f != "f=0.3" or w != "W=24" or rho != "rho=0.7" or q not in ("q=0.5", "q=na") or pol not in show: + continue + curves[pol] = {"lambda": c["lambda"], + "electricity_usd_per_mtoken": [r3(x, 6) for x in c["electricity_usd_per_mtoken"]], + "co2_g_per_mtoken": [r3(x, 4) for x in c["co2_g_per_mtoken"]], + "saving_pct_electricity": [r3(x, 3) for x in c["saving_pct_electricity"]], + "saving_pct_co2": [r3(x, 3) for x in c["saving_pct_co2"]], + "saving_pct_total": [r3(x, 4) for x in c["saving_pct_total"]]} + pairs[pair] = {"same_region": v["same_region"], "curves": curves} + return {"status": "ready", "data_kind": "public price and carbon series, modeled workload", "source": src(p, ROOT), + "setting": {"flexible_fraction": 0.3, "window_hours": 24, "utilization": 0.7, "planning_quantile": 0.5}, + "pairs": pairs} + + +def audit_block() -> dict: + p = E3 / "cost-audit-v1.json" + d = load(p) + if d is None: + return {"status": "pending"} + r = d["result"] + base = r["base_case"] + regions = [{"setting": x["setting"], "electricity_share": r3(x["electricity_share"])} + for x in r["one_at_a_time"]["H100"] if x["parameter"] == "electricity price"] + return {"status": "ready", "source": src(p, ROOT), "base_electricity_share": r3(base["electricity_share"]), + "base_inputs": {k: base["inputs"][k] for k in ("useful_life_years", "pue", "electricity_price")}, + "regions_h100": regions} + + +def heatwave_block(hw: Path | None) -> dict: + out = {} + if hw is None: + return {"forecast": {"status": "pending"}, "ablation": {"status": "pending"}, "finetune": {"status": "pending"}} + ms = hw / "results" / "forecast" / "multiscale.json" + d = load(ms) + if d is None: + out["forecast"] = {"status": "pending"} + else: + rows = {} + for ds, v in d["result"]["datasets"].items(): + rows[ds] = {"data_kind": v["data_kind"], "description": v["meta"]["description"], + "wql": {m: r3(mm["buckets"]["all"]["wql"]["est"]) for m, mm in v["methods"].items() + if "all" in mm.get("buckets", {})}} + out["forecast"] = {"status": "ready", "source": src(ms, hw), "datasets": rows} + for key, rel in (("ablation", "results/ablation/ablation_sim.json"), ("ablation_real", "results/ablation/ablation_real.json")): + p = hw / rel + d = load(p) + ok = d is not None and d["result"].get("complete", True) + out[key] = {"status": "ready", "source": src(p, hw)} if ok else {"status": "pending"} + ft = sorted((hw / "results" / "finetune").glob("*.json")) if (hw / "results" / "finetune").exists() else [] + out["finetune"] = {"status": "ready", "sources": [src(p, hw) for p in ft]} if ft else {"status": "pending"} + return out + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--heatwave", default="/home/claude/heatwave") + a = ap.parse_args() + hw = Path(a.heatwave) if a.heatwave and Path(a.heatwave).exists() else None + doc = {"schema": "gpu-stack.e003-heatwave-lab.v1", + "note": "Generated by scripts/build_heatwave_lab_data.py from committed result files. 'pending' means the result is not in yet.", + "packing": packing_block(), "outage": outage_block(), "cost": cost_block(), "audit": audit_block(), + **heatwave_block(hw)} + OUT.write_text(json.dumps(doc, separators=(",", ":"), allow_nan=False)) + print(f"wrote {OUT} ({OUT.stat().st_size / 1024:.0f} KiB)") + + +if __name__ == "__main__": + main() diff --git a/tests/test_heatwave_lab.mjs b/tests/test_heatwave_lab.mjs new file mode 100644 index 0000000..383c4b2 --- /dev/null +++ b/tests/test_heatwave_lab.mjs @@ -0,0 +1,53 @@ +import assert from "node:assert/strict"; +import fs from "node:fs/promises"; +import path from "node:path"; +import test from "node:test"; +import { fileURLToPath } from "node:url"; + +const ROOT = path.resolve(path.dirname(fileURLToPath(import.meta.url)), ".."); +const DOCS = path.join(ROOT, "docs"); + +test("the Heatwave window module registers a mount function and keeps the house style", async () => { + const src = await fs.readFile(path.join(DOCS, "heatwave-lab.js"), "utf8"); + assert.ok(!/[—–]/.test(src), "no em or en dash"); + assert.ok(src.length < 40 * 1024, "module stays small"); + globalThis.window = globalThis; + await import(path.join(DOCS, "heatwave-lab.js")); + assert.equal(typeof globalThis.GPUStackHeatwave.mount, "function"); +}); + +test("the Heatwave data file has every quantile state and labels its data", async () => { + let raw; + try { + raw = await fs.readFile(path.join(DOCS, "data", "e003-heatwave-lab-v1.json"), "utf8"); + } catch { + return; // generated by scripts/build_heatwave_lab_data.py + } + const d = JSON.parse(raw); + assert.equal(d.schema, "gpu-stack.e003-heatwave-lab.v1"); + for (const key of ["packing", "outage", "cost", "audit"]) assert.ok(d[key] && d[key].status, key + " block"); + if (d.packing.status === "ready") { + assert.equal(d.packing.data_kind, "simulated"); + assert.equal(d.packing.quantile_levels.length, 21); + for (const q of d.packing.quantile_levels) { + const run = d.packing.trace.runs["timestral-5M/q" + String(q)]; + assert.ok(run, "trace for level " + q); + assert.equal(run.site_power_kw.length, d.packing.trace.runs.reactive.site_power_kw.length); + assert.ok(run.matches_committed_part, "replay matches the committed eval part for " + q); + assert.ok(d.packing.policies["timestral-5M/q" + String(q)], "summary for level " + q); + } + assert.ok(d.packing.policies.reactive); + } + assert.ok(raw.length < 1.5 * 1024 * 1024, "data file under 1.5 MB"); +}); + +test("the Story chapter on forecasting is in place and cites its numbers", async () => { + const html = await fs.readFile(path.join(DOCS, "index.html"), "utf8"); + const m = html.match(/
    ([\s\S]*?)<\/section>/); + assert.ok(m, "chapter exists"); + const body = m[1]; + assert.ok(!/[—–]/.test(body.replace(//g, "")), "no em dash in copy"); + const paragraphsWithNumbers = (body.match(/

    [^]*?<\/p>/g) || []).filter((p) => /\d/.test(p.replace(//g, ""))); + for (const p of paragraphsWithNumbers) assert.ok(p.includes("

    -

    My laptop's GPU throttles itself from about 84 degrees and roughly halves its clock when it does. In a test run, a 30-second temperature forecast warned before 6 of the 7 slowdowns, 18.5 seconds ahead on median, where watching the thermometer gave 10.5 seconds. Plain statistics did just as well as Timestral here. Thirty seconds isn't long enough for a big model to matter.

    -

    The rest happened in a simulated two-site cluster with a power cap, with power levels taken from published H100 measurements. Letting jobs in whenever the meter shows room packs the most work and spends 72 percent of the time over the cap. Planning on the forecast's 90th percentile packs about 17 percent less work and goes over the cap with about a sixth of the energy. Which percentile is right depends on what going over costs: a small learned rule that picks it as it goes was worth $747 more per run than reacting when each kilowatt-hour over the cap costs $40, and $295 less when it costs $2.50.

    -

    Moving the flexible part of the work to cheaper, cleaner hours of real California prices cut its electricity bill by 8.6 percent and its carbon by 10.8 percent, close to the 9.0 percent perfect foresight gets. Copying yesterday's prices got 8.3 percent, so the daily price pattern does most of the work. The whole bill moved by 0.22 percent, because hardware is most of it.

    - +

    A quantile forecast doesn't give one number. It gives 21: the level the power stays under 1 percent of the time, 5 percent, 10 percent and so on up to 99. Plan on the 90th and you expect to be caught short one time in ten. My forecaster was Timestral, the open forecasting model Mistral released in October 2026, used as it comes with no training on my data. I scored it with a quantile loss, which charges a forecast for missing in the wrong direction at each level, so lower is better. On all eleven series I tried, from one-second GPU power to hourly grid prices, it scored lower than the textbook methods. On GPU power from H100 and B200 servers, sampled every second, its loss was 0.158 against 0.259 for the best textbook method.

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    My laptop's GPU throttles itself: from about 84 degrees it slows its clock, from a median of 1,642 MHz to 900. In a test run, a 30-second temperature forecast from Timestral warned before 6 of the 7 slowdowns, 18.5 seconds ahead on median, where watching the thermometer gave 10.5 seconds. ETS, a plain textbook method, warned before the same 6, 16.5 seconds ahead. Thirty seconds isn't long enough for a big model to matter.

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    The rest happened in a simulated two-site cluster with a power cap, the most power a site may draw. Its GPU power levels come from published H100 measurements. The jobs, the weather and the outages are simulated, so nothing from here down was measured on a real cluster. Letting jobs in whenever the meter shows room packs the most work, 1,841 GPU-hours on average over six held-out episodes of 16 hours, and spends 72 percent of the intervals over the cap. Planning on the forecast's 90th percentile packs about 17 percent less work and goes over the cap with about a sixth of the energy, 7.1 kWh against 42.5 kWh.

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    Each dot is one percentile to plan on. Simulated cluster, not data from a real one.
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    Which percentile is right depends on what going over the cap costs. A small learned rule that picks the percentile as it goes was worth $746 more per episode than reacting when each kilowatt-hour over the cap costs $40, and $5,575 more when it costs $160. At $10 the difference sat inside its interval. At $2.50 it was worth $295 less.

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    Moving the flexible part of the work to cheaper, cleaner hours of real California prices cut its electricity bill by 8.6 percent and its carbon by 10.8 percent, close to the 9.0 percent perfect foresight gets. In this test 30 percent of the work could wait up to a day. Copying yesterday's prices got 8.3 percent, so the daily price pattern does most of the work. The whole bill moved by 0.22 percent, because hardware is most of it.

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    Then I made things go wrong. In ten held-out simulated episodes the cluster had 90 events: hardware faults, cooling failures, breaker trips, grid curtailments and thermal throttling. The simulator scheduled 65 of them, and 24 showed a warning sign in the data first while 41 showed nothing, which no forecast can warn about. The response to a warning was to save a checkpoint early, then move the work to the other site or pause it. Reacting without a forecast lost 35.1 GPU-hours to faults per episode. Timestral warnings lost 26.8, warned in time for 29 percent of all events and gave 0.4 false alarms a day, and they caught 17 of the 24 that showed a sign. Counting the GPU-hours the response itself uses, the cluster ended with 1,727 useful GPU-hours against 1,722 for reacting, which is 4.9 GPU-hours more, with a 95 percent interval from -1.3 to 16.0. That difference is inside its interval. The first version of the warning used rack-level signs only and warned in time for 21 percent; the second adds each GPU's own temperature compared with its neighbors.

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    One more question: what does a forecaster gain from things that are known ahead of time? A job schedule, a weather forecast and a day-ahead price are all published before the hour they describe, so Timestral can take them as extra inputs next to the history. In California the day-ahead price helped predict the real-time price: loss 0.189 without it and 0.169 with it, though the intervals overlap, so I treat that as a hint. A weather forecast alone did nothing I can measure (0.188). On my laptop the load schedule cut the loss on one-second power from 0.096 to 0.045. The same schedule at ten readings a second made the biggest model worse, 0.107 to 0.250 over 2,337 windows. In Great Britain an archived carbon forecast cut the carbon loss from 0.145 to 0.041, but nobody wrote down when that forecast was issued, so I don't trust that one yet.

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    One marker per input. Left of the dashed line means a lower loss than the same model with no inputs.
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    The same test on the simulated cluster, with its job schedule, weather forecast, price and carbon forecast and training-step phase, is still running. So is the fine-tuning of Timestral on this kind of data, which I'll compare against using it as it comes.

    Open the Heatwave window

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