diff --git a/campaigns.yaml b/campaigns.yaml
index 99c8e24..acdac10 100644
--- a/campaigns.yaml
+++ b/campaigns.yaml
@@ -1,5 +1,5 @@
 version: 1
-last_checkin: '2026-10-17T11:09:26Z'
+last_checkin: '2026-10-17T15:12:59Z'
 campaigns:
 - id: setup-baseline
   title: Setup baseline collection and scientific acceptance
@@ -25,24 +25,24 @@ campaigns:
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04
 - id: point-source-image-gpu
   title: Point-source image plane · A100
-  status: needs-decision
+  status: parked
   updated: '2026-10-17'
-  next: 'No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have
-    release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs
-    authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.
-    Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond
-    autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no
-    new measurement.'
+  next: 'Parked as research, human decision 2026-10-17 (chat) ("just research to file"). Reference measurement autolens_profiling#434
+    (issue #427, OPEN): job 412519 euclid-ral-gpu-2 exclusive A100, jax 0.10.2, fp64, 2026.10.17.1 private clone:
+    single call 0.934 ms (pre-#768 0.910), vmap-256 0.0125 ms (0.0120), compile 6.54 s; forward gradient finite 16/16,
+    equal to reverse to 1.6e-11; logL bit-exact; +2.7% not attributed (different node/date). A100 per-step breakdown
+    rows unusable. Phase-2 inference fit measurement dropped; no baseline admitted.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_gpu_breakdown.md
 - id: point-source-source
   title: Point-source source plane
-  status: parked
+  status: complete
   updated: '2026-10-17'
-  next: 'Core remains parked; autolens_inference#21 reorganized setup evidence but added no point-source gradient
-    admission measurement. #408 merged median fields into 11 runtime cells; #412 merged 2026-10-13, completing
-    headline/repeat support. No new runtime sweep; keep Blackjax parked. Check-in 2026-10-17: no project
-    wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only);
-    releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.'
+  next: 'Closed as research, human decision 2026-10-17 (chat): source-plane speed-ups "not worth pursuing again".
+    Evidence: autolens_inference#29 (issue #25) + autolens_profiling#432 (issue #428), both OPEN at PR-open: MultiStartAdam
+    forward gradients, 48 starts, 5 seeds, 2026.10.17.1 wheels (jax 0.11.2), fp64; likelihood+gradient ≤3.7% of fit
+    wall, A100 slower end to end than 8 CPU cores; default 300 steps stops 4/5 seeds on the limit, 3000 converges.
+    Runtime cell medians A100 0.233 ms / RAL CPU 0.147 ms. Blackjax stays parked; Adam step limit recorded as a PyAutoFit
+    search-settings note.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_source_plane.md
 - id: cluster-pointsolver
   title: Cluster PointSolver robustness and performance
@@ -56,20 +56,18 @@ campaigns:
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/cluster_pointsolver.md
 - id: interferometer
   title: Interferometer likelihood and streaming
-  status: active
+  status: parked
   updated: '2026-10-17'
-  next: 'Aris large-mask (PyAutoPulse#62): library PRs PyAutoArray#630/#631/#632/#634 RELEASED in 2026.10.16.1
-    (2026-10-16; 2026.10.16.2 and 2026.10.17.1 followed with no further PyAutoArray change). Phase-2 route-gate
-    record autolens_profiling#420 merged 2026-10-15; #422 (gate-60 wording historical) and #424 (lint re-pin
-    after #634) merged 2026-10-16; project main fd29902. L1 Cholesky reuse (#634) measured on the NumPy path:
-    solve+log-det 144.5→50.9 ms at 1,405 vertices, 1,268→373 ms at 2,947 (full call −22–24% / −51%). L2
-    direct_conv symmetry (PyAutoArray#635) PAUSED by the human 2026-10-16, uncommitted on worktree
-    feature/direct-conv-symmetric (base e59d5fa9), no PR: synthetic Aris-512 serial RAL witness (job 411835)
-    1.81×/1.85×/1.90× at S 1,428/2,540/5,712, computed entries bit-identical; real Aris data on the laptop 1.32×
-    (centroid order) / 1.46× (ymin order), work fraction 0.59/0.51; human decision 2026-10-16: rank by ascending
-    ymin, default gate 0.5→0.7; v3 (ymin + 0.7) present in the worktree diff, its test run, the RAL real-data
-    witness (≥1.3× curvature step), ship and write-up are owed. Streaming 1e8 CPU/A100 rows, the #332 memo
-    after-measurement, curvature preload (fixed-mapper science decision) and FFT levers unchanged.'
+  next: 'Evening 2026-10-17: human decision 2026-10-17 (chat): interferometer CPU work is exhaustive and closed; curvature
+    preload parked ("we will come back to it soon"); GPU levers "file wiki research but not make source code changes
+    for now", so no library prompt for JAX FFT pruning. Parked at knowledge-complete. Open PRs (human''s to merge
+    or close): L2 symmetric direct_conv PyAutoArray#636 (pending-release; real Aris RAL 1.49×/1.53×/1.58× at 1,459/2,577/5,746
+    vertices, logL to round-off; decision record direct-conv-symmetric-gate) + record autolens_profiling#425; A100
+    FFT levers #433 (pruned FFT 1.08×/1.18×/1.41× whole likelihood at alma/alma_high/jvla; pixel-scale coarsening
+    fails the 0.5-nat bar); streaming 1e8 CPU/A100 + memo after-measurement #435. Route-constant refit (RAL FFT/full
+    direct_conv 0.68 vs 0.89 predicted) deferred. Merge #425 after #636. Six open autolens_profiling PRs (#425, #431,
+    #432, #433, #434, #435) all regenerate the shared catalogue/dashboard: each merge after the first needs a rebase
+    + re-render; suggested order #425, #432, #431, #433, #434, #435. #425 after PyAutoArray#636.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09f/wiki/campaigns/interferometer_likelihood.md
 - id: linear-solver
   title: Linear-solver accuracy and cost
@@ -86,25 +84,33 @@ campaigns:
   title: Production imaging and likelihood breakdown
   status: active
   updated: '2026-10-17'
-  next: 'No new production-over-sampling or post-certified measurements. #408 now adds median headline fields to
-    runtime cells; #412 merged 2026-10-13 and extends remaining headline surfaces. Production presets,
-    memo-blind harness, cumulative MGE steps and quick-update costs remain separate pending work. Scope
-    production-preset phase 1 (CPU smoke only); A100 measurement separately authorized. New related library
-    evidence: PyAutoArray#624/#625 (numba Sibson kernel; DelaunayNN within 2× Delaunay on Aris 1,405) released
-    2026.10.16.1; follow-up PyAutoArray#629 (imaging DelaunayNN still ~2.7× Delaunay at sub_size 4:
-    unique_mappings 0.30 vs 0.10 s, adaptive_pixel_signals 0.16 vs 0.01 s; laptop, threads=1, load 3–5, not a
-    controlled measurement) is open and its Mind draft expects a Pulse campaign delaunay-nn-cpu that does not
-    yet exist — human decision whether to open one.'
+  next: 'Production over-sampling phase 1 at PR-open: autolens_profiling#431 (issue #430, OPEN, CPU smoke 8/8): --over-sampling
+    {flat1,production} on the 4 JAX Delaunay/DelaunayNN cells, euclid/hst presets. Findings: HST flat-1 adapt_split
+    pin fails on unmodified main (29155.000 vs 29140.296); numba cells'' production threshold never reaches sub 4
+    for HST; rectangular JAX cells uncovered. Phase 2 (A100 production re-measure) needs-decision (human decision
+    2026-10-17 (chat): undecided). DelaunayNN imaging moved to the parked delaunay-nn-cpu campaign. Post-certified,
+    quick-update plotting and other tasks unchanged.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/post_certified_breakdown.md
+- id: delaunay-nn-cpu
+  title: DelaunayNN (Sibson) mesh at Delaunay cost on the numba/NumPy CPU path
+  status: parked
+  updated: '2026-10-17'
+  next: 'Created parked, human decision 2026-10-17 (chat): "file in research to come back to in the future". PyAutoArray#637
+    (issue #629, OPEN, pending-release, CI 3 legs green): RAL compute-10-4 exclusive 1 thread A-B-B-A ×2, 1,030 vertices
+    / 15,380 px: NN sub4 0.569→0.392 s (2.20× Delaunay, was 3.00×), sub2 0.235→0.189 s (1.75×); logL bit-identical;
+    fixes the size-0 Sibson NaN case. ~1.5× target not met; remaining cost is Sibson (~0.16 s) + wider curvature rows.
+    Target and two hazards recorded as future work, not filed.'
+  evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/637
 - id: measurement-tools
   title: Measurement reliability and profiling tools
   status: active
   updated: '2026-10-17'
   next: 'Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415 closed.
-    No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo
-    interpretation, fresh runtime repeats and other tools remain pending. Check-in 2026-10-17:
-    autolens_profiling#418 (pilot qualification design) open since 2026-10-13 with CI green and no review; #417
-    open; no new measurements.'
+    No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo interpretation,
+    fresh runtime repeats and other tools remain pending. #418 and #417 open. Evening 2026-10-17 merge-order note:
+    Six open autolens_profiling PRs (#425, #431, #432, #433, #434, #435) all regenerate the shared catalogue/dashboard:
+    each merge after the first needs a rebase + re-render; suggested order #425, #432, #431, #433, #434, #435. #425
+    after PyAutoArray#636.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/pull/416
 - id: autofit
   title: PyAutoFit profiling bootstrap
@@ -166,13 +172,12 @@ tasks:
   campaign: point-source-source
   title: Runtime cells' A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady
     0.267 ms on the same node
-  status: active
+  status: complete
   priority: low
   path: tasks/runtime_cell_single_jit_gpu_warmup.md
-  updated: '2026-10-13'
-  next: '#408 merged 2026-10-09: median headline fields now wired into 11 runtime cells, superseding the old
-    imaging-not-wired note. #412 merged 2026-10-13 with remaining headline/repeat support. No new runtime sweep;
-    fresh median evidence still missing.'
+  updated: '2026-10-17'
+  next: 'Fresh medians at PR-open, autolens_profiling#432 (OPEN), 2026.10.17.1 wheels: A100 0.233 ms (single_jit 0.347),
+    RAL CPU 0.147 ms (0.177); pins pass. Remaining release-sweep cells gain the field at the next full sweep.'
   issue: https://github.com/PyAutoLabs/autolens_profiling/issues/371
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/scripts/point_source_source/source_plane/likelihood_runtime_solved.py
 - id: timing_noise_audit
@@ -289,12 +294,14 @@ tasks:
 - id: imaging_production_over_sampling
   campaign: imaging
   title: Profile imaging pixelizations at production over-sampling
-  status: ready
+  status: needs-decision
   priority: normal
   path: tasks/imaging_production_over_sampling.md
-  updated: '2026-10-13'
-  next: 'Phase 1 (workspace-only): --instrument euclid|hst presets for the JAX Delaunay runtime/breakdown cells,
-    keeping flat-1 selectable; CPU smoke only. A100 re-measure is a separate compute-authorized phase.'
+  updated: '2026-10-17'
+  next: 'Phase 1 at PR-open: autolens_profiling#431 (issue #430, OPEN, CPU smoke 8/8). Phase 2 A100 production re-measure
+    needs-decision (human decision 2026-10-17 (chat): undecided); requires #431 merged and compute authorization.
+    Findings: HST flat-1 adapt_split pin fails on main; numba HST threshold never reaches sub 4; rectangular JAX cells
+    uncovered.'
   evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/606
 - id: interferometer_decision_matrix_last_cell
   campaign: interferometer
@@ -312,35 +319,38 @@ tasks:
 - id: interferometer_fixed_mapper_curvature_preload
   campaign: interferometer
   title: 'Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`'
-  status: ready
+  status: parked
   priority: normal
   path: tasks/interferometer_fixed_mapper_curvature_preload.md
-  updated: '2026-10-13'
-  next: Phase 1 Mind draft (2026-10-04) still unstarted; run it through start_dev. Human science call first
-    - no production SLaM stage holds the mapper fixed today.
+  updated: '2026-10-17'
+  next: 'Parked, human decision 2026-10-17 (chat): interferometer CPU work closed; "we will come back to it soon".
+    Phase-1 Mind draft unstarted.'
   evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/55a453cf/draft/feature/autoarray/interferometer_curvature_preload_phase1.md
 - id: interferometer_nnls_memo_scattered_stream_guard
   campaign: interferometer
   title: 'fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)'
-  status: active
+  status: parked
   priority: medium
   path: tasks/interferometer_nnls_memo_scattered_stream_guard.md
-  updated: '2026-10-13'
-  next: 'Library half shipped: PyAutoArray#615 (closes #613) merged 2026-10-06, in 2026.10.7.1. Local solver-only
-    witness (n=576): iid memo on/off 1.49x → 1.17x; walk 0.18x unchanged. Owed: re-run the autolens_profiling#332
-    ALMA Delaunay 2.17x after-measurement (needs compute authorization).'
+  updated: '2026-10-17'
+  next: 'After-measurement at PR-open autolens_profiling#435 (issue #426, OPEN; job 412526, ral compute-10-2, load
+    ~10, 1 thread, 6 rounds, paired bootstrap 90% CI): Delaunay iid #332 flags 1.918 [1.887,1.925] (2.17 in #332);
+    15 timed solves 1.018 [1.013,1.023]; Delaunay walk 0.46; rect iid 0.887/0.987; rect walk 0.15; FoM identical.
+    Not evidence #613 is fixed. Research owed: Nautilus-replay witness, varied-seed rounds. Parked, human decision
+    2026-10-17 (chat).'
   issue: https://github.com/PyAutoLabs/PyAutoArray/issues/613
   evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/615
 - id: interferometer_streaming_scaling
   campaign: interferometer
   title: 'Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities'
-  status: active
+  status: parked
   priority: high
   path: tasks/interferometer_streaming_scaling.md
-  updated: '2026-10-13'
-  next: 'Phase 1 CPU cells + results/streaming_scaling rows merged (autolens_profiling#375, 2026-10-04); wiki/campaigns/interferometer_streaming.md.
-    Optional: 1e8 CPU row and A100 rows (compute authorization). Library candidate unfiled: chunk transformer.image_from
-    in apply_sparse_operator (memory).'
+  updated: '2026-10-17'
+  next: 'Phase 2 at PR-open autolens_profiling#435 (issue #368 reopened, OPEN): from_stream chunk 65536 fp64, 1e8
+    vis RAL CPU 707.5 s / 1.78 GB RSS, A100 157.3 s / 1.48 GB device; compile ~3 s constant. In-memory fails at 2.5e5
+    (RAL 10 GB cap) and 4e6 (A100); uncapped CPU ~7.5 kB/vis. Optional 2e8 rows are research; library candidate (chunk
+    transformer.image_from) unfiled per human decision 2026-10-17 (chat). Parked.'
   issue: https://github.com/PyAutoLabs/autolens_profiling/issues/368
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/interferometer_streaming.md
 - id: interferometer_large_mask_aris
@@ -350,24 +360,24 @@ tasks:
   priority: high
   path: tasks/interferometer_large_mask_aris.md
   updated: '2026-10-17'
-  next: 'Released: #630/#631/#632/#634 in 2026.10.16.1 (2026-10-16). Phase-2 record #420 merged 2026-10-15 (table
-    re-pinned at fd29902); #422/#424 merged 2026-10-16. L2 (PyAutoArray#635) paused 2026-10-16 with v3 (ymin
-    order, gate 0.7) uncommitted in worktree feature/direct-conv-symmetric; synthetic witness PASS 1.81–1.90×
-    (RAL 411835), real-data laptop 1.46× at ymin order; owed on resume: verify v3 + full suite, RAL real-data
-    witness ≥1.3× (data and private library clones staged under scratch/aris_l2_20261016), Heart check,
-    ship_library PR, write-up (results note, Knowledge section, decision record direct-conv-symmetric-gate).
-    κ=1.32 means the #632 cost-model constant for direct_conv is effectively refit when L2 ships.'
+  next: 'L2 shipped to PR: PyAutoArray#636 (head 62a1e3ac, pending-release) + autolens_profiling#425 (results note),
+    both OPEN, CI green. RAL real-data witness 1.49×/1.53×/1.58× at 1,459/2,577/5,746 vertices (pred 1.48×/1.50×/1.53×),
+    logL to round-off; peak RSS 43 GB without NUFFT chunking. Decision record direct-conv-symmetric-gate added. Route-constant
+    refit (0.68 measured vs 0.89 predicted) deferred: CPU lane closed, human decision 2026-10-17 (chat). Remaining:
+    human merge of #636 then #425, and release.'
   issue: https://github.com/PyAutoLabs/PyAutoPulse/issues/62
   evidence: https://github.com/PyAutoLabs/PyAutoArray/issues/626
 - id: interferometer_w_tilde_fft_size_levers
   campaign: interferometer
   title: 'Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space
     pixel scale on the A100'
-  status: ready
+  status: parked
   priority: normal
   path: tasks/interferometer_w_tilde_fft_size_levers.md
-  updated: '2026-10-13'
-  next: Unstarted (no PR/issue/branch/results, 2026-10-04). Select one bounded step when prioritised.
+  updated: '2026-10-17'
+  next: 'A100 research filed at PR-open autolens_profiling#433 (issue #429, OPEN; job 412515): pruned padded FFT 1.08×/1.18×/1.41×
+    whole likelihood at alma/alma_high/jvla, F exact to 1.7e-15; pixel-scale coarsening shifts log evidence by +1,310
+    to +2,757 nats (fails). No library prompt, human decision 2026-10-17 (chat). Parked.'
 - id: point_solver_profiling_cells
   campaign: cluster-pointsolver
   title: 'PointSolver profiling cells: lensed quasar → cluster runtime tier → single/multi-source → multiplane'
@@ -380,24 +390,24 @@ tasks:
 - id: point_source_image_plane_gpu_breakdown
   campaign: point-source-image-gpu
   title: 'Point-source A100 speed-up campaign: profile and optimize with the shared breakdown'
-  status: needs-decision
+  status: parked
   priority: high
   path: tasks/point_source_image_plane_gpu_breakdown.md
-  updated: '2026-10-13'
-  next: 'No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries
-    now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still
-    needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.'
+  updated: '2026-10-17'
+  next: 'Parked, human decision 2026-10-17 (chat). Reference A100 measurement autolens_profiling#434 (OPEN, job 412519):
+    0.934 ms single call, 0.0125 ms at vmap-256, forward gradient fixed; phase-2 inference fit measurement dropped.'
   issue: https://github.com/PyAutoLabs/PyAutoLens/issues/767
   evidence: https://github.com/PyAutoLabs/PyAutoLens/pull/768
 - id: point_source_source_plane_chi_squared_speed
   campaign: point-source-source
   title: Point-source source-plane chi-squared speed-up campaign — remaining candidates (phases 1–2e shipped)
-  status: blocked
+  status: complete
   priority: low
   path: tasks/point_source_source_plane_chi_squared_speed.md
-  updated: '2026-10-13'
-  next: 'Still blocked on a point-source gradient-sampler admission measurement. Inference #21 is setup organization,
-    not that measurement. Keep parked.'
+  updated: '2026-10-17'
+  next: 'Complete as research, human decision 2026-10-17 (chat). Admission evidence autolens_inference#29 + autolens_profiling#432
+    (OPEN): likelihood+gradient ≤3.7% of MultiStartAdam fit wall, so a likelihood speed-up caps at a few percent.
+    Blackjax stays parked.'
   evidence: https://github.com/PyAutoLabs/autolens_inference/pull/17
 - id: pointsolver_cpu_speed_campaign_remainder
   campaign: point-source-image-cpu
@@ -459,3 +469,15 @@ tasks:
   next: Unchanged; targets PyAutoLens/PyAutoGalaxy (PyAutoLens#680 closed 2026-07-31, no follow-up issue).
     Select one bounded step when prioritised.
   evidence: https://github.com/PyAutoLabs/PyAutoLens/issues/680
+- id: delaunay_nn_cpu_imaging
+  campaign: delaunay-nn-cpu
+  title: DelaunayNN (Sibson) imaging likelihood at close to Delaunay cost on the numba/NumPy CPU path
+  status: parked
+  priority: normal
+  path: tasks/delaunay_nn_cpu_imaging.md
+  updated: '2026-10-17'
+  next: 'Parked, human decision 2026-10-17 (chat). PyAutoArray#637 (OPEN, pending-release) at PR-open: sub4 2.20×
+    Delaunay (was 3.00×), sub2 1.75×; logL bit-identical. Future (unfiled): ~1.5× target; hazards sub_slim_indexes_for_pix_index
+    padding and Sibson rows with mid-row gaps.'
+  issue: https://github.com/PyAutoLabs/PyAutoArray/issues/629
+  evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/637
diff --git a/checkins/2026-10-17.md b/checkins/2026-10-17.md
new file mode 100644
index 0000000..e373f58
--- /dev/null
+++ b/checkins/2026-10-17.md
@@ -0,0 +1,90 @@
+# Profiling check-in — 2026-10-17
+
+Reviewed at 2026-10-17T15:12:59Z; morning check-in 2026-10-17T11:09:26Z (Pulse PR #66, unmerged at this
+check-in: the human's `!` merge commands did not take effect, so this update is stacked on it).
+Review time and board generation are not measurement times. This check-in records verified
+outcomes and the human's decisions of 2026-10-17 (chat). It authorizes no compute, default or
+baseline change, campaign freeze, scientific acceptance, release or merge.
+
+## Human decisions 2026-10-17 (chat)
+
+These are recorded in the ledger `next:` texts and task check-in sections. They are not new
+decision records unless stated.
+
+1. Interferometer CPU work is exhaustive and closed. Curvature preload parked: "we will come back to it soon".
+2. Interferometer GPU levers: "file wiki research but not make source code changes for now". No library prompt for JAX FFT pruning; campaign parked at knowledge-complete.
+3. DelaunayNN: "file in research to come back to in the future". New campaign `delaunay-nn-cpu`, parked; the ~1.5× target and two hazards recorded as future work, not filed.
+4. Point-source source-plane speed-ups "not worth pursuing again, file as research". Campaign closed as research.
+5. Point-source image plane: "just research to file". Campaign parked; A100 numbers recorded as a reference measurement; phase-2 inference fit measurement dropped.
+6. Imaging phase 2 (A100 production over-sampling re-measure): undecided → needs-decision.
+
+New decision record: [`direct-conv-symmetric-gate`](../decisions/direct-conv-symmetric-gate.md),
+capturing the human decision of 2026-10-16 on [PyAutoArray#635](https://github.com/PyAutoLabs/PyAutoArray/issues/635)
+(ymin column ordering, default gate 0.5 → 0.7).
+
+## Verified availability
+
+- All PRs below are OPEN, CI green and unmerged. Every Heart read was STALE ("release validation incomplete: no rehearsal for current source"); no override was used anywhere.
+- Mind: all related tasks are awaiting-merge. Cortex run log pushed for RAL jobs 412495/412498.
+- Measurements were made by the project workers today; this check-in did not re-run them.
+
+## Campaign review
+
+| Campaign | State | Change / next bounded step |
+|---|---|---|
+| Interferometer likelihood and streaming | parked | Decisions 1–2. L2 symmetric `direct_conv` PR-open (PyAutoArray#636 + record #425): real Aris RAL 1.49× / 1.53× / 1.58× at 1,459 / 2,577 / 5,746 vertices (pred 1.48× / 1.50× / 1.53×), logL to round-off, peak RSS 43 GB without NUFFT chunking; gain over the old default route at 1,459 only ~1.02× (FFT 15.32 s). RAL FFT/full direct_conv 0.68 measured vs 0.89 predicted: constants refit deferred. A100 FFT levers (#433): pruned FFT 1.08× / 1.18× / 1.41× whole likelihood at alma / alma_high / jvla, F exact to 1.7e-15; pixel-scale coarsening shifts log evidence +1,310 / +1,807 / +2,757 nats (fails 0.5-nat bar). Streaming (#435): 1e8 vis CPU 707.5 s / 1.78 GB RSS, A100 157.3 s / 1.48 GB device, compile ~3 s; in-memory fails at 2.5e5 (RAL 10 GB cap) and 4e6 (A100). Memo after-measurement (#435, job 412526): Delaunay iid 1.918 [1.887, 1.925] vs 2.17 in #332; 15 solves 1.018 [1.013, 1.023]; walk 0.46; rect iid 0.887 / 0.987; rect walk 0.15; FoM identical — an after-measurement, not a fix. |
+| Point-source source plane | complete | Decision 4. autolens_inference#29 + autolens_profiling#432: MultiStartAdam forward gradients, 48 starts, 5 seeds, 2026.10.17.1 wheels (jax 0.11.2), fp64. Default 300 steps: 4/5 seeds hit the limit, two 1.4σ off; 3000 steps: all converge into the Nautilus logL range, ≤ 0.8σ. Likelihood+gradient ≤ 3.7 % of fit wall; per step 1.7 ms CPU / 4.4 ms A100 (0.24 / 0.35 ms likelihood+grad); A100 slower end to end than 8 CPU cores. Runtime cell medians A100 0.233 ms (single_jit 0.347), RAL CPU 0.147 ms (0.177), pins pass. Blackjax stays parked; Adam step-limit is a PyAutoFit search-settings note, not a task. |
+| Point-source image plane · A100 | parked | Decision 5. #434, job 412519 euclid-ral-gpu-2 exclusive, A100 80GB, jax 0.10.2, fp64: single call 0.934 ms (pre-#768 0.910), vmap-256 0.0125 ms (0.0120), compile 6.54 s; forward gradient finite 16/16, equal to reverse to 1.6e-11 (was NaN); reverse/likelihood 1.889; FD 4/6 (same points as before); logL bit-exact; within-run MDI 1.50 %; +2.7 % not attributed (different node/date). A100 per-step breakdown rows unusable (11/43 negative). |
+| Production imaging and likelihood breakdown | active | Production over-sampling phase 1 PR-open (#431, smoke 8/8): `--over-sampling {flat1,production}` on the 4 JAX Delaunay/DelaunayNN cells; euclid [4,4,2]@[0.1,0.3] and hst [4,2,2]@[0.1,0.3], sub 4 where source S/N > 3 else 2; HST 2,022 / 15,361 px at sub 4, Euclid 1,057 / 3,841. Findings: HST flat-1 adapt_split pin fails on unmodified main (29155.000 vs 29140.296, 5e-4); numba HST threshold never reaches sub 4; rectangular JAX cells uncovered. Phase 2 A100: needs-decision (decision 6). |
+| DelaunayNN mesh on the CPU path (new) | parked | Decision 3. PyAutoArray#637 (issue #629, pending-release, CI 3 legs green): RAL compute-10-4 exclusive, 1 thread, A-B-B-A ×2, 1,030 vertices / 15,380 px: NN sub4 0.569 → 0.392 s (2.20× Delaunay, was 3.00×); NN sub2 0.235 → 0.189 s (1.75×, was 2.10×); Delaunay sub4 0.189 → 0.178 s; logL bit-identical; adaptive signals 158 → 6.5 ms; unique_mappings 22.5 → 17.0 ms; Sibson ~0.16 s (~41 %). ~1.5× target not met; fixes the size-0 Sibson NaN edge case. |
+| Measurement reliability and profiling tools | active | Merge-order note added (below). #417/#418 open; no new measurements. |
+| Setup baseline, Point-source image CPU, Cluster PointSolver, Linear-solver, PyAutoFit, Critical curves, Certified solver | unchanged | Carried from the morning check-in; no new evidence this evening. |
+
+## Task changes
+
+| Task | State | Note |
+|---|---|---|
+| [interferometer_large_mask_aris](../tasks/interferometer_large_mask_aris.md) | active | L2 at PR-open; Knowledge (f) L2 row, (g) bullet and (h) added. Open only until #636/#425 merge and release. |
+| [interferometer_w_tilde_fft_size_levers](../tasks/interferometer_w_tilde_fft_size_levers.md) | parked | A100 research filed (#433); no library prompt. |
+| [interferometer_streaming_scaling](../tasks/interferometer_streaming_scaling.md) | parked | 1e8 CPU/A100 rows at PR-open (#435). |
+| [interferometer_nnls_memo_scattered_stream_guard](../tasks/interferometer_nnls_memo_scattered_stream_guard.md) | parked | After-measurement at PR-open (#435); Nautilus-replay witness and varied-seed rounds owed as research. |
+| [interferometer_fixed_mapper_curvature_preload](../tasks/interferometer_fixed_mapper_curvature_preload.md) | parked | Decision 1. |
+| [point_source_source_plane_chi_squared_speed](../tasks/point_source_source_plane_chi_squared_speed.md) | complete | Closed as research. |
+| [runtime_cell_single_jit_gpu_warmup](../tasks/runtime_cell_single_jit_gpu_warmup.md) | complete | Fresh medians (#432). |
+| [point_source_image_plane_gpu_breakdown](../tasks/point_source_image_plane_gpu_breakdown.md) | parked | Reference measurement (#434). |
+| [imaging_production_over_sampling](../tasks/imaging_production_over_sampling.md) | needs-decision | Phase 1 PR-open (#431); phase 2 undecided. |
+| [delaunay_nn_cpu_imaging](../tasks/delaunay_nn_cpu_imaging.md) (new) | parked | #637 at PR-open. |
+
+## Open PRs (the human's to merge or close)
+
+| PR | Content |
+|---|---|
+| [PyAutoArray#636](https://github.com/PyAutoLabs/PyAutoArray/pull/636) | L2 symmetric direct_conv (head 62a1e3ac, pending-release) |
+| [PyAutoArray#637](https://github.com/PyAutoLabs/PyAutoArray/pull/637) | DelaunayNN padded-row and size-0 Sibson fixes (pending-release) |
+| [autolens_profiling#425](https://github.com/PyAutoLabs/autolens_profiling/pull/425) | L2 results note |
+| [autolens_profiling#431](https://github.com/PyAutoLabs/autolens_profiling/pull/431) | Imaging production over-sampling phase 1 |
+| [autolens_profiling#432](https://github.com/PyAutoLabs/autolens_profiling/pull/432) | Point-source source-plane gradient fit share + runtime medians |
+| [autolens_profiling#433](https://github.com/PyAutoLabs/autolens_profiling/pull/433) | A100 FFT size levers (titles still name the dropped CPU constants) |
+| [autolens_profiling#434](https://github.com/PyAutoLabs/autolens_profiling/pull/434) | Point-source image-plane A100 reference measurement |
+| [autolens_profiling#435](https://github.com/PyAutoLabs/autolens_profiling/pull/435) | Interferometer streaming 1e8 rows + memo after-measurement |
+| [autolens_inference#29](https://github.com/PyAutoLabs/autolens_inference/pull/29) | MultiStartAdam point-source source-plane fits |
+| [PyAutoPulse#66](https://github.com/PyAutoLabs/PyAutoPulse/pull/66) | Morning check-in (this update is stacked on it) |
+
+**Merge order.** The six autolens_profiling PRs (#425, #431, #432, #433, #434, #435) all regenerate
+the shared catalogue/dashboard, so each merge after the first needs a rebase and re-render before the
+next. Suggested order: #425, #432, #431, #433, #434, #435. Library PRs #636 and #637 are file-disjoint
+and independent; merge #425 after #636.
+
+## Unavailable or unverified
+
+- No release contains #636 or #637; all "speed-up" figures are PR-open measurements, not released behaviour or scientific acceptance.
+- RAL jax 0.10.2 (used for the A100 image-plane and FFT-lever jobs) is outside 2026.10.17.1's declared jax range; source SHAs are the provenance.
+- Owed research, not tasks: memo Nautilus-replay witness, varied-seed rounds, optional 2e8 streaming rows, RAL route-constant refit.
+
+## RAL cleanup owed (recorded, not deleted)
+
+- `/mnt/ral/jnightin/scratch/fft_levers_20261017/` (abandoned partial clones)
+- `/mnt/ral/jnightin/scratch/fft_levers_a100_20261017/` (partial extract)
+- `/mnt/ral/jnightin/scratch/fft_levers_stack.tgz`, `fft_levers_jobs/`, `fft_levers_results/` (results JSON is committed in #433)
+- `/mnt/ral/jnightin/profiling/autolens_profiling_wt/ps-image-admission_stale_*`
+- Worker private clones under `profiling/` and `inference/` are kept for reuse.
diff --git a/dashboard.html b/dashboard.html
index 56a21d9..d848496 100644
--- a/dashboard.html
+++ b/dashboard.html
@@ -646,7 +646,7 @@
 - PyAutoPulse: https://github.com/PyAutoLabs/PyAutoPulse
 - autolens_profiling: https://github.com/PyAutoLabs/autolens_profiling
 - autofit_profiling: https://github.com/PyAutoLabs/autofit_profiling</textarea></details><div class="orchestration-freshness" data-freshness="grey"><details data-freshness-stamp data-refreshed-at="2026-10-17T08:07:26Z"><summary><span data-freshness-label>Last updated 2026-10-17 08:07:26 UTC</span></summary><time datetime="2026-10-17T08:07:26Z">2026-10-17 08:07:26 UTC</time></details><a data-refresh-link href="https://github.com/PyAutoLabs/PyAutoPulse/actions/workflows/dashboard_refresh.yml" title="Open the dashboard refresh controls">↻ Update</a></div></div><p class="orchestration-status" role="status" aria-live="polite"></p></section>
-<nav class="board-nav" aria-label="Board sections"><a class="board-nav-card" href="#campaigns"><span class="board-nav-label">Active campaigns</span></a><a class="board-nav-card" href="#evidence"><span class="board-nav-label">Profiling Results</span></a><a class="board-nav-card" href="#decision-history"><span class="board-nav-label">Decision History</span></a></nav><main><!-- pulse-campaigns:19faf009965e3df26e5113e7c9ff26b4dcb4a5df3d156bf43e8838997abca73d --><details class="board-section"><summary><h2 id="campaigns">Active campaigns</h2></summary><div class="board-section-body"><div class="campaign-table"><table><thead><tr><th>Campaign</th><th>Status</th><th>Links</th></tr></thead><tbody><tr><td><strong>Setup baseline collection and scientific acceptance</strong><details id="campaign-setup-baseline" class="campaign-detail"><summary>2 tasks · next step</summary><p>Review the Pulse collection choices (candidate input hashes and 31-worker resource plan verified without execution) alongside project correctness audit #417. Resolve data origin/suitability, named host/stack/full settings, witness/tolerances and background-load method. No freeze, compute or acceptance authorized. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/setup_baseline_campaign.md">Freeze the setup baseline specification, then collect and review evidence</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/384" aria-label="Issue for Freeze the setup baseline specification, then collect and review evidence" title="Issue">↗</a></h4><p class="review-meta">needs-decision · high</p><p>HST/MGE raw fixed-parameter CPU runner merged in #416; Pulse handoff #55 merged and Mind implementation closed. Resolve exact production host/stack/data, full settings, independent correctness witness/tolerances and background-load method. No collection, campaign freeze or acceptance authorized; global compile dispatch remains unavailable.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/hst_mge_cpu_pilot.md">Prepare and review the HST/MGE fp64 CPU pilot</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/415" aria-label="Issue for Prepare and review the HST/MGE fp64 CPU pilot" title="Issue">↗</a></h4><p class="review-meta">needs-decision · high</p><p>Review the Pulse collection choices (candidate input hashes and 31-worker resource plan verified without execution) alongside project correctness audit #417. Resolve data origin/suitability, named host/stack/full settings, witness/tolerances and background-load method. No freeze, compute or acceptance authorized.</p></article></details></td><td><span class="pill">needs-decision</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/pull/416" aria-label="Evidence for Setup baseline collection and scientific acceptance" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-setup-baseline" data-open="campaign-setup-baseline" aria-label="Tasks for Setup baseline collection and scientific acceptance" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source image plane · CPU</strong><details id="campaign-point-source-image-cpu" class="campaign-detail"><summary>1 task · next step</summary><p>No new committed campaign measurements. Phases 1–4 remain shipped; epic closure is a human decision. Carried leftovers remain pending. RAL queue empty at review, but transfer preview timed out: cleanup folders and unpulled results unverified. Select one owed cleanup/measurement phase only after review. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement. RAL queue holds only euclid_dr1 jobs; cleanup folders and unpulled results still unverified.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pointsolver_cpu_speed_campaign_remainder.md">Point-source (single-source) CPU campaign — carried leftovers and completion evidence</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/370" aria-label="Issue for Point-source (single-source) CPU campaign — carried leftovers and completion evidence" title="Issue">↗</a></h4><p class="review-meta">ready · low</p><p>No new committed campaign measurements. Phases 1–4 remain shipped; epic closure is a human decision. Carried leftovers remain pending. RAL queue empty at review, but transfer preview timed out: cleanup folders and unpulled results unverified. Select one owed cleanup/measurement phase only after review.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04" aria-label="Evidence for Point-source image plane · CPU" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-image-cpu" data-open="campaign-point-source-image-cpu" aria-label="Tasks for Point-source image plane · CPU" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source image plane · A100</strong><details id="campaign-point-source-image-gpu" class="campaign-detail"><summary>1 task · next step</summary><p>No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_image_plane_gpu_breakdown.md">Point-source A100 speed-up campaign: profile and optimize with the shared breakdown</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoLens/issues/767" aria-label="Issue for Point-source A100 speed-up campaign: profile and optimize with the shared breakdown" title="Issue">↗</a></h4><p class="review-meta">needs-decision · high</p><p>No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.</p></article></details></td><td><span class="pill">needs-decision</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_gpu_breakdown.md" aria-label="Evidence for Point-source image plane · A100" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-image-gpu" data-open="campaign-point-source-image-gpu" aria-label="Tasks for Point-source image plane · A100" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source source plane</strong><details id="campaign-point-source-source" class="campaign-detail"><summary>2 tasks · next step</summary><p>Core remains parked; autolens_inference#21 reorganized setup evidence but added no point-source gradient admission measurement. #408 merged median fields into 11 runtime cells; #412 merged 2026-10-13, completing headline/repeat support. No new runtime sweep; keep Blackjax parked. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/runtime_cell_single_jit_gpu_warmup.md">Runtime cells&#x27; A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/371" aria-label="Issue for Runtime cells&#x27; A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node" title="Issue">↗</a></h4><p class="review-meta">active · low</p><p>#408 merged 2026-10-09: median headline fields now wired into 11 runtime cells, superseding the old imaging-not-wired note. #412 merged 2026-10-13 with remaining headline/repeat support. No new runtime sweep; fresh median evidence still missing.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_source_plane_chi_squared_speed.md">Point-source source-plane chi-squared speed-up campaign — remaining candidates (phases 1–2e shipped)</a></h4><p class="review-meta">blocked · low</p><p>Still blocked on a point-source gradient-sampler admission measurement. Inference #21 is setup organization, not that measurement. Keep parked.</p></article></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_source_plane.md" aria-label="Evidence for Point-source source plane" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-source" data-open="campaign-point-source-source" aria-label="Tasks for Point-source source plane" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Cluster PointSolver robustness and performance</strong><details id="campaign-cluster-pointsolver" class="campaign-detail"><summary>2 tasks · next step</summary><p>No campaign wiki/results change or new matching implementation claim found. Representative cluster baseline and observable-overflow policy remain unstarted. Choose one bounded phase; inherited single-source results do not qualify a representative cluster baseline. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/cluster_pointsolver_speed.md">Cluster PointSolver — robustness, analysis settings and performance</a></h4><p class="review-meta">ready · normal</p><p>Unissued, no claim. Pick one bounded phase per Mind epics.md: observable-overflow invalid-result policy + CI witness, or a representative-data baseline cell.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_solver_profiling_cells.md">PointSolver profiling cells: lensed quasar → cluster runtime tier → single/multi-source → multiplane</a></h4><p class="review-meta">ready · normal</p><p>Unissued, no claim. Candidate first cell: a representative-data cluster likelihood_runtime baseline (single-source preset).</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/cluster_pointsolver.md" aria-label="Evidence for Cluster PointSolver robustness and performance" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-cluster-pointsolver" data-open="campaign-cluster-pointsolver" aria-label="Tasks for Cluster PointSolver robustness and performance" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Interferometer likelihood and streaming</strong><details id="campaign-interferometer" class="campaign-detail"><summary>5 tasks · next step</summary><p>Aris large-mask (PyAutoPulse#62): library PRs PyAutoArray#630/#631/#632/#634 RELEASED in 2026.10.16.1 (2026-10-16; 2026.10.16.2 and 2026.10.17.1 followed with no further PyAutoArray change). Phase-2 route-gate record autolens_profiling#420 merged 2026-10-15; #422 (gate-60 wording historical) and #424 (lint re-pin after #634) merged 2026-10-16; project main fd29902. L1 Cholesky reuse (#634) measured on the NumPy path: solve+log-det 144.5→50.9 ms at 1,405 vertices, 1,268→373 ms at 2,947 (full call −22–24% / −51%). L2 direct_conv symmetry (PyAutoArray#635) PAUSED by the human 2026-10-16, uncommitted on worktree feature/direct-conv-symmetric (base e59d5fa9), no PR: synthetic Aris-512 serial RAL witness (job 411835) 1.81×/1.85×/1.90× at S 1,428/2,540/5,712, computed entries bit-identical; real Aris data on the laptop 1.32× (centroid order) / 1.46× (ymin order), work fraction 0.59/0.51; human decision 2026-10-16: rank by ascending ymin, default gate 0.5→0.7; v3 (ymin + 0.7) present in the worktree diff, its test run, the RAL real-data witness (≥1.3× curvature step), ship and write-up are owed. Streaming 1e8 CPU/A100 rows, the #332 memo after-measurement, curvature preload (fixed-mapper science decision) and FFT levers unchanged.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_fixed_mapper_curvature_preload.md">Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`</a></h4><p class="review-meta">ready · normal</p><p>Phase 1 Mind draft (2026-10-04) still unstarted; run it through start_dev. Human science call first - no production SLaM stage holds the mapper fixed today.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_nnls_memo_scattered_stream_guard.md">fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoArray/issues/613" aria-label="Issue for fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)" title="Issue">↗</a></h4><p class="review-meta">active · medium</p><p>Library half shipped: PyAutoArray#615 (closes #613) merged 2026-10-06, in 2026.10.7.1. Local solver-only witness (n=576): iid memo on/off 1.49x → 1.17x; walk 0.18x unchanged. Owed: re-run the autolens_profiling#332 ALMA Delaunay 2.17x after-measurement (needs compute authorization).</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_streaming_scaling.md">Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/368" aria-label="Issue for Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities" title="Issue">↗</a></h4><p class="review-meta">active · high</p><p>Phase 1 CPU cells + results/streaming_scaling rows merged (autolens_profiling#375, 2026-10-04); wiki/campaigns/interferometer_streaming.md. Optional: 1e8 CPU row and A100 rows (compute authorization). Library candidate unfiled: chunk transformer.image_from in apply_sparse_operator (memory).</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_large_mask_aris.md">Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoPulse/issues/62" aria-label="Issue for Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model" title="Issue">↗</a></h4><p class="review-meta">active · high</p><p>Released: #630/#631/#632/#634 in 2026.10.16.1 (2026-10-16). Phase-2 record #420 merged 2026-10-15 (table re-pinned at fd29902); #422/#424 merged 2026-10-16. L2 (PyAutoArray#635) paused 2026-10-16 with v3 (ymin order, gate 0.7) uncommitted in worktree feature/direct-conv-symmetric; synthetic witness PASS 1.81–1.90× (RAL 411835), real-data laptop 1.46× at ymin order; owed on resume: verify v3 + full suite, RAL real-data witness ≥1.3× (data and private library clones staged under scratch/aris_l2_20261016), Heart check, ship_library PR, write-up (results note, Knowledge section, decision record direct-conv-symmetric-gate). κ=1.32 means the #632 cost-model constant for direct_conv is effectively refit when L2 ships.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_w_tilde_fft_size_levers.md">Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space pixel scale on the A100</a></h4><p class="review-meta">ready · normal</p><p>Unstarted (no PR/issue/branch/results, 2026-10-04). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09f/wiki/campaigns/interferometer_likelihood.md" aria-label="Evidence for Interferometer likelihood and streaming" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-interferometer" data-open="campaign-interferometer" aria-label="Tasks for Interferometer likelihood and streaming" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Linear-solver accuracy and cost</strong><details id="campaign-linear-solver" class="campaign-detail"><summary>0 tasks · next step</summary><p>Retain the 2026-10-08 human decision: Jacobi for Mapper, raw+polish for MGE-only, no default change. Project wiki still says decision pending (stale). No new solver measurements after the recorded phase-5 results; release 2026.10.12.1 does not supply a rerun. Wide-prior/interferometer gaps remain evidence gaps, not authorized tasks. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md" aria-label="Evidence for Linear-solver accuracy and cost" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-linear-solver" data-open="campaign-linear-solver" aria-label="Tasks for Linear-solver accuracy and cost" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Production imaging and likelihood breakdown</strong><details id="campaign-imaging" class="campaign-detail"><summary>3 tasks · next step</summary><p>No new production-over-sampling or post-certified measurements. #408 now adds median headline fields to runtime cells; #412 merged 2026-10-13 and extends remaining headline surfaces. Production presets, memo-blind harness, cumulative MGE steps and quick-update costs remain separate pending work. Scope production-preset phase 1 (CPU smoke only); A100 measurement separately authorized. New related library evidence: PyAutoArray#624/#625 (numba Sibson kernel; DelaunayNN within 2× Delaunay on Aris 1,405) released 2026.10.16.1; follow-up PyAutoArray#629 (imaging DelaunayNN still ~2.7× Delaunay at sub_size 4: unique_mappings 0.30 vs 0.10 s, adaptive_pixel_signals 0.16 vs 0.01 s; laptop, threads=1, load 3–5, not a controlled measurement) is open and its Mind draft expects a Pulse campaign delaunay-nn-cpu that does not yet exist — human decision whether to open one.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/imaging_production_over_sampling.md">Profile imaging pixelizations at production over-sampling</a></h4><p class="review-meta">ready · normal</p><p>Phase 1 (workspace-only): --instrument euclid|hst presets for the JAX Delaunay runtime/breakdown cells, keeping flat-1 selectable; CPU smoke only. A100 re-measure is a separate compute-authorized phase.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/post_certified_solver_likelihood_breakdown.md">Assess remaining likelihood bottlenecks after certified solver integration on CPU and GPUs</a></h4><p class="review-meta">needs-slicing · normal</p><p>Blocked-by is stale: resolved 2026-09-27 (PyAutoArray#567 in 2026.9.26.1). Unissued; scope one bounded phase from the full task contract.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/quick_update_plotting_cost.md">Quick-update plotting cost — minutes per update, and it is not JAX compile</a></h4><p class="review-meta">ready · medium</p><p>Unchanged; targets PyAutoLens/PyAutoGalaxy (PyAutoLens#680 closed 2026-07-31, no follow-up issue). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/post_certified_breakdown.md" aria-label="Evidence for Production imaging and likelihood breakdown" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-imaging" data-open="campaign-imaging" aria-label="Tasks for Production imaging and likelihood breakdown" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Measurement reliability and profiling tools</strong><details id="campaign-measurement-tools" class="campaign-detail"><summary>8 tasks · next step</summary><p>Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415 closed. No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo interpretation, fresh runtime repeats and other tools remain pending. Check-in 2026-10-17: autolens_profiling#418 (pilot qualification design) open since 2026-10-13 with CI green and no review; #417 open; no new measurements.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/mge_likelihood_breakdown_steps_are_cumulative_an.md">MGE likelihood_breakdown steps are cumulative and `linear_gaussians` is reported as 0</a></h4><p class="review-meta">ready · low</p><p>Unchanged and unissued (no GitHub refs, 2026-10-04). Select one bounded step when measurement-tools is prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/gradient_cost_probe.md">A gradient-cost probe: forward vs `value_and_grad` ms/eval and a strict FD check, on any registry cell</a></h4><p class="review-meta">ready · low</p><p>Unissued. New context 2026-10-07: Heart unit-test timing flagged PyAutoLens forward-gradient tests +150%/+96% after PyAutoLens#768 rewrote that test file; not a controlled comparison, and this probe is the right instrument. Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/numba_breakdown_harness_memo_blind.md">Numba breakdown harness: perturb the instance so the operated-matrix memo cannot hide a step</a></h4><p class="review-meta">ready · medium</p><p>Unchanged and unissued (PyAutoArray#496 closed 2026-08-27). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/search_settings_estimation_infrastructure.md">Search settings-estimation + profiling infrastructure (n_starts / batch_size / n_batch)</a></h4><p class="review-meta">ready · normal</p><p>Unchanged and unissued (autolens_profiling#82 closed 2026-08-18). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/jax_compile_probe_needs_own_cell_builder.md">jax_compile/probe.py lost its cell builder with the searches tier — give profiling its own</a></h4><p class="review-meta">active · high</p><p>Manual HST/MGE fp64 jit builder #414 and separate raw pilot runner #416 merged. Bounded Mind implementations complete; wider builder coverage/global compile dispatch still unavailable. Agree a next capability or separately authorize a qualified pilot; no production compute authorized. Check-in 2026-10-17: autolens_profiling#418 (pilot qualification design) open since 2026-10-13 with CI green and no review; #417 open; no new measurements.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/profile_lens_aggregator_needs_config_dir.md">profile_lens_aggregator.py cannot run from the autolens_workspace_developer root: no config/ directory</a></h4><p class="review-meta">ready · low</p><p>Unchanged: workspace_developer main still has no root config/ (bug presumed live, not re-run). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/gradient_pixelization_pin_residual_drift.md">jax_profiling/gradient/imaging/pixelization.py: 3.2% of its pin move is unattributed</a></h4><p class="review-meta">ready · normal</p><p>Unchanged and unissued (PyAutoArray#490 merged 2026-08-26). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pair_jax_xla_env_vars_with_measured.md">Pair JAX/XLA env vars with measured compile and run times, per backend</a></h4><p class="review-meta">ready · high</p><p>Unchanged and unissued. Context: the jax-version policy is now released (2026.10.7.1 libraries require Nerves with the JAX deadlock exclusions). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/pull/416" aria-label="Evidence for Measurement reliability and profiling tools" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-measurement-tools" data-open="campaign-measurement-tools" aria-label="Tasks for Measurement reliability and profiling tools" title="Open tasks">☷</a></div></td></tr><tr><td><strong>PyAutoFit profiling bootstrap</strong><details id="campaign-autofit" class="campaign-detail"><summary>1 task · next step</summary><p>B4a (autofit_profiling#3) and B4b (autofit_profiling#5, issue #4 closed, merged 2026-10-15) shipped: search.fit breakdown cell, profiling-summary@2 exporter, EP/graphical N3/N10/N30 baselines and analytic parity cell; #6 repointed RAL paths to the category layout (2026-10-15). Pulse fit fixture/campaign merged (#60, #61); fit receipt ok at autofit_profiling 58e56b9 (31 records, fetched 2026-10-15T22:58Z), so the post-deploy refresh is done. Epic 2 not opened: next bounded step is a human-scoped epic-2 campaign row; B5c inference work belongs to Insight, not Pulse.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/autofit_profiling_bootstrap.md">autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic</a></h4><p class="review-meta">active · normal</p><p>B4a (autofit_profiling#3) and B4b (autofit_profiling#5, issue #4 closed, merged 2026-10-15) shipped: search.fit breakdown cell, profiling-summary@2 exporter, EP/graphical N3/N10/N30 baselines and analytic parity cell; #6 repointed RAL paths to the category layout (2026-10-15). Pulse fit fixture/campaign merged (#60, #61); fit receipt ok at autofit_profiling 58e56b9 (31 records, fetched 2026-10-15T22:58Z), so the post-deploy refresh is done. Epic 2 not opened: next bounded step is a human-scoped epic-2 campaign row; B5c inference work belongs to Insight, not Pulse.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autofit_profiling/issues/4" aria-label="Evidence for PyAutoFit profiling bootstrap" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-autofit" data-open="campaign-autofit" aria-label="Tasks for PyAutoFit profiling bootstrap" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Critical curves and evaluation grids</strong><details id="campaign-critical-curves" class="campaign-detail"><summary>0 tasks · next step</summary><p>No new campaign measurement. Phase 3b cap fix remains shipped; project wiki still incorrectly says #646 awaits merge/release. Seed coverage and path completion remain unresolved; retain marching-squares decision and ask the human whether to scope the next phase. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">needs-decision</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/critical_curves.md" aria-label="Evidence for Critical curves and evaluation grids" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-critical-curves" data-open="campaign-critical-curves" aria-label="Tasks for Critical curves and evaluation grids" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Certified positive solver</strong><details id="campaign-certified-solver" class="campaign-detail"><summary>0 tasks · next step</summary><p>Parked unchanged. No new campaign evidence; existing C2/library/workspace drafts remain pending. Do not infer admission from solver timing, and do not change default or restart the campaign. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/certified_positive_solver.md" aria-label="Evidence for Certified positive solver" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-certified-solver" data-open="campaign-certified-solver" aria-label="Tasks for Certified positive solver" title="Open tasks">☷</a></div></td></tr></tbody></table></div><p class="review-meta">Open means tracked, not necessarily running. <a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/campaigns.yaml">Full ledger ↗</a></p></div></details><details class="board-section"><summary><h2 id="evidence">Profiling Results</h2></summary><div class="board-section-body"><section class="setup-browser" data-setup-browser="lens" aria-label="autolens_profiling setups"><script type="application/json" class="setup-payload">{"catalogue":{"archive_record_count":20709,"comparison_policy":{"id":"no-temporal-comparisons"},"comparisons":[],"coverage":{"excluded":[],"expected":{"baseline_cells":29,"cells":101,"planned_slots":435},"observed":{"records":112,"selected":98,"setups":540}},"evidence_shards":[{"axes":["breakdown"],"axis_devices":{"breakdown":["cpu"]},"devices":["cpu"],"path":"catalogue/shards/eb112c2ba02f1d8956d3.json","precisions":["precision not 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recorded"],"records":12,"setup_id":"cluster/source_plane/unspecified/de80334a2c42ad6dc3c0","sha256":"ff9c73687f52fdf4a7993ea03ec9a04f6600e846491f1fe91616a0f2e379009e"},{"axes":["breakdown"],"axis_devices":{"breakdown":["cpu"]},"devices":["cpu"],"path":"catalogue/shards/af3685283b1327e0cee4.json","precisions":["precision not recorded"],"records":8,"setup_id":"cluster/source_plane/unspecified/f8ed0d4a341f22830f33","sha256":"64b6bb7a5f2fae3b66e0868acded64eb31fead28f3969bbe405620d705b96db2"},{"axes":["breakdown"],"axis_devices":{"breakdown":["a100"]},"devices":["a100"],"path":"catalogue/shards/8d08365c80db809fe6ef.json","precisions":["float64"],"records":1,"setup_id":"datacube/delaunay/alma_high/6861c7695d52974b0ea3","sha256":"84b00147ab4b14c5071b236aa1f0b118e850eae4f9d1ef47144eb5e293353329"},{"axes":["breakdown"],"axis_devices":{"breakdown":["a100"]},"devices":["a100"],"path":"catalogue/shards/9ed9973b39d5e1a1cbf6.json","precisions":["mixed"],"records":1,"setup_id":"datacube/delaunay/alma_high/f7e1c1f32cc47c5229ea","sha256":"511b9b2a28bc29a2c618766a11eb259da0f40e07a797e925f3b8dd5c3e4f5728"},{"axes":["breakdown"],"axis_devices":{"breakdown":["cpu"]},"devices":["cpu"],"path":"catalogue/shards/162eaf4183b703aa2ae7.json","precisions":["precision not 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no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"point_source_source","id":"baseline/point_source_source/source_plane/unspecified","instrument":null,"label":"point_source_source/source_plane/unspecified · baseline pending","model":"source_plane","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}},{"configuration":{"image_pixels_masked":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"image_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"n_vis":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"oversampling":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"dimensionless","value":null},"preloads":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"cluster","id":"baseline/cluster/image_plane/unspecified","instrument":null,"label":"cluster/image_plane/unspecified · baseline pending","model":"image_plane","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}},{"configuration":{"image_pixels_masked":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"image_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"n_vis":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"oversampling":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"dimensionless","value":null},"preloads":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"multi_dataset","id":"baseline/multi_dataset/delaunay/unspecified","instrument":null,"label":"multi_dataset/delaunay/unspecified · baseline pending","model":"delaunay","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}}],"static_memory_estimates":[{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_delaunay.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay","per_replica":{"unit":"MiB","value":921.913},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":62},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_delaunay_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay","per_replica":{"unit":"MiB","value":131.505},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_mge.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"mge","per_replica":{"unit":"MiB","value":16.411},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_mge_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"mge","per_replica":{"unit":"MiB","value":16.411},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_pixelization.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"rectangular","per_replica":{"unit":"MiB","value":931.004},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":62},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_pixelization_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"rectangular","per_replica":{"unit":"MiB","value":94.906},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/delaunay_nn/vmap_probe_delaunay_nn_batch64_cufft_failed.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay_nn","per_replica":{"unit":"MiB","value":770.97},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"point_source_source","evidence":{"fragment":null,"path":"results/runtime/point_source_source/source_plane_solved/vmap_probe_source_plane_solved.json"},"instrument":"simple","kind":"xla_static_buffer_estimate","model":"source_plane_solved","per_replica":{"unit":"MiB","value":0.0},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64}],"unbound_findings":[{"description":"The conversion maps every magnitude at or above 0.999 to q=0.000500250125062532. NumPy reaches the thin valid annulus below one but its constructor guard blocks the beyond-unit region; JAX arrays and tracers bypass that concrete-scalar guard.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.ell_comps.magnitude-saturation","path":"results/hazards/hazards_index.json"},"id":"component.ell_comps.magnitude-saturation","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"ell_comps","title":"ell_comps magnitude saturates at 0.999"},{"description":"A second, load-bearing saturation prevents the q=1 divisions in the elliptical Isothermal deflection from becoming 0/0. Its affected ell_comps disk is too small for prior-sampling discovery.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.isothermal.near-spherical-saturation","path":"results/hazards/hazards_index.json"},"id":"component.isothermal.near-spherical-saturation","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"isothermal","title":"Isothermal caps near-spherical axis ratios at 0.99999"},{"description":"`_wofz_rational` selects one of three rational branches with `xp.where` on |z|^2 and Im(z)^2. The value is continuous to ~1e-6 across a seam but the derivative is not, so `jax.grad` of any MGE deflection carries a kink wherever a grid point crosses a boundary. Autodiff derivative error just above the r2=2.5 seam reaches 7.71e-06 relative, against 1.3e-14 for the SciPy path the numpy backend uses.","discovery":{"models":[],"reason":"Shared MGE mass-profile deflection component study, not specifically the imaging MGE light-model likelihood. Consult the recorded component and revision before applying this finding.","shared":true},"evidence":{"fragment":"/findings/component.mge.faddeeva-seam-gradient","path":"results/hazards/hazards_index.json"},"id":"component.mge.faddeeva-seam-gradient","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"mge","title":"The JAX Faddeeva routine's region seams make its derivative discontinuous"},{"description":"The elliptical Faddeeva form is singular at q = 1, so the MGE machinery clamps the axis ratio to 0.9999. The numpy path escapes through an exact radial branch; the JAX path cannot, and every *Sph MGE-routed profile is biased by up to 6.18e-05 relative with a spurious cross-axis deflection of 0.000139 arcsec.","discovery":{"models":[],"reason":"Shared MGE mass-profile deflection component study, not specifically the imaging MGE light-model likelihood. Consult the recorded component and revision before applying this finding.","shared":true},"evidence":{"fragment":"/findings/component.mge.spherical-clamp-bias","path":"results/hazards/hazards_index.json"},"id":"component.mge.spherical-clamp-bias","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"mge","title":"Spherical MGE profiles are evaluated as ellipses at the q = 0.9999 clamp on JAX"},{"description":"The JAX backend replaces the exact SciPy hyp2f1 angular factor with a fixed 20-term omega series. Relative deflection error grows continuously to 0.297 over the measured factor curve.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.power-law.series-vs-hyp2f1-divergence","path":"results/hazards/hazards_index.json"},"id":"component.power-law.series-vs-hyp2f1-divergence","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"power_law","title":"PowerLaw's 20-term JAX series diverges from hyp2f1 at high ellipticity"},{"description":"The spherical radial coordinate returns the correct finite value zero at the profile centre, but JAX differentiation of sqrt(y^2+x^2) returns a non-finite gradient there.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.spherical-geometry.radial-sqrt-gradient-at-zero","path":"results/hazards/hazards_index.json"},"id":"component.spherical-geometry.radial-sqrt-gradient-at-zero","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"spherical_geometry","title":"Radial sqrt has a non-finite gradient at r=0"},{"description":"Using only the diagonal entries the policy actually touches, the absolute floor reaches 4.457e-05 of their scale. A reference-calibrated scale-aware counterfactual holds its fraction fixed with maximum relative output error 3.395e-05.","discovery":{"models":[{"dataset":"imaging","model":"rectangular"}],"reason":"The reproducer is the imaging rectangular-source likelihood fixture. This is related model evidence; it does not establish applicability to other configurations or measured versions.","shared":false},"evidence":{"fragment":"/findings/likelihood.imaging-pixelization.absolute-conditioning-floors","path":"results/hazards/hazards_index.json"},"id":"likelihood.imaging-pixelization.absolute-conditioning-floors","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"imaging_pixelization","title":"Absolute inversion floors move with dataset scale"},{"description":"The NumPy full-likelihood scan changes active support at 5 bounded Einstein-radius location(s). The report measures explicit epsilon-neighbourhood mass, not exact-kink prior mass.","discovery":{"models":[{"dataset":"imaging","model":"rectangular"}],"reason":"The reproducer is the imaging rectangular-source likelihood fixture. This is related model evidence; it does not establish applicability to other configurations or measured versions.","shared":false},"evidence":{"fragment":"/findings/likelihood.imaging-pixelization.nnls-active-set-kinks","path":"results/hazards/hazards_index.json"},"id":"likelihood.imaging-pixelization.nnls-active-set-kinks","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"imaging_pixelization","title":"NNLS support changes create likelihood kinks"},{"description":"2 argmax-switch location(s) found in [0.0, 3.0] (4 truth positions, 0 of them where max_sep is already over threshold — i.e. a second, threshold-independent gradient kink in the ACTIVE penalty region, from max_sep itself being a max of several smooth per-position functions).","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.argmax-switch","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.argmax-switch","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"Which position-pair sets max_sep switches discontinuously across the sweep"},{"description":"Across 40/401 grid points with max_sep \u003c threshold, penalty value and gradient are exactly zero (max |value|=0.000e+00, max |grad|=0.000e+00) — a gradient-search lane deep inside the fence gets NO signal at all from the positions term, by construction.","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.interior-plateau","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.interior-plateau","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"PositionsLH penalty is an exact-zero, zero-gradient plateau inside threshold"},{"description":"Sweeping einstein_radius in [0.0, 3.0] (401 pts, threshold=0.3, factor=1e+08), a tight (+/-1e-6) probe at each of 2 max_sep==threshold crossing(s) shows the penalty VALUE is continuous there (max gap 2.006e+02) while its jax-autodiff GRADIENT jumps by up to 2.006e+08 — zero on the interior side, nonzero on the exterior side. A gradient optimizer sees a step-function force switching on at the fence, not a smooth potential.","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.threshold-hinge","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.threshold-hinge","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"PositionsLH penalty has a C0 hinge at the threshold crossing"},{"description":"The configured 1.0e-03 diagonal add ranges from dominant to negligible as synthetic curvature scale changes. Detection needs no dataset; judging scientific relevance does.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/matrix.curvature.absolute-diagonal-floor","path":"results/hazards/hazards_index.json"},"id":"matrix.curvature.absolute-diagonal-floor","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"matrix","subject_name":"curvature_matrix","title":"Absolute curvature-diagonal floor is scale dependent"}],"valid_until":null,"version":2},"instance":"lens","label":"PyAutoLens","repo":"https://github.com/PyAutoLabs/autolens_profiling","commit":"fd29902bd09ff5defcab5e9007a547423048e8aa","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autolens_profiling/fd29902bd09ff5defcab5e9007a547423048e8aa/dashboard/","local":false,"remote_shards":true}</script><p data-id="load-status" role="status" aria-live="polite"></p><div data-id="navigation"></div><section data-id="results" aria-label="Selected setup" hidden></section><noscript><details><summary>Original setup evidence (JavaScript disabled)</summary><ul><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_breakdown_v2026.7.23.1.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_breakdown_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_breakdown_v2026.7.6.649.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_breakdown_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_hpc_ral_cpu_fp64.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_local_cpu_fp64.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/source_plane_breakdown_v2026.7.23.1.json">cluster / source_plane / unspecified — results/breakdown/cluster/source_plane_breakdown_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/source_plane_breakdown_v2026.7.6.649.json">cluster / source_plane / unspecified — results/breakdown/cluster/source_plane_breakdown_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/datacube/delaunay_hpc_a100_fp64.json">datacube / delaunay / alma_high — results/breakdown/datacube/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/datacube/delaunay_hpc_a100_mp.json">datacube / delaunay / alma_high — results/breakdown/datacube/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_autotune4.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_autotune4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1_control.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_recon_split.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_mp.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_mp_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_breakdown_hst_v2026.8.17.1.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly_control.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk1024.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk2048.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk2048.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk4096.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk512.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk512.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_control.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_recon_split.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_vmap64.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_vmap64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_numba_breakdown_euclid_v2026.8.17.1.json">imaging / delaunay / euclid — results/breakdown/imaging/delaunay_numba_breakdown_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_numba_breakdown_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_numba_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2_control.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3_control.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n3000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n3000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_nn_hpc_a100_fp64_fixed_light.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_delaunay_nn_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_hpc_a100_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_t1.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_tall.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_hpc_a100_fp64_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_t1.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_tall.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n1225_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n1225_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n484_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n484_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_hpc_a100_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_t1.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_tall.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library_vmap2.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library_vmap2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_mp_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n1225_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n1225_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n484_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n484_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3025_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3025_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n12000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n3000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n5000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_nn_hpc_a100_fp64_matrix_free.json">imaging / delaunay_nn / hst — results/breakdown/imaging/matrix_free_delaunay_nn_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n12000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n3000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n5000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.5.29.4.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.7.6.649.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.8.17.1.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_hpc_a100_fp64.json">imaging / mge / hst — results/breakdown/imaging/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_recon_split.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_mp.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_mp_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_euclid_v2026.8.17.1.json">imaging / rectangular / euclid — results/breakdown/imaging/pixelization_numba_breakdown_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_mp.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64_sparse.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_mp.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_mp.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay / sma — results/breakdown/interferometer/delaunay_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_constant_split.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_mp.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay / sma — results/breakdown/interferometer/delaunay_numba_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_levers.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_levers.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r4.25.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r4.25.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r6.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r6.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_jax_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_jax_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_jax_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_jax_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_reference_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_reference_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_reference_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_reference_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_source_loop_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_source_loop_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_symmetric_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_symmetric_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_two_stage_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_two_stage_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/delaunay_hpc_a100_mp.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_fp64.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_fp64_sparse.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_mp.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/pixelization_hpc_a100_fp64.json">interferometer / rectangular / jvla — results/breakdown/interferometer/jvla/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/pixelization_hpc_a100_mp.json">interferometer / rectangular / jvla — results/breakdown/interferometer/jvla/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_high_v2026.8.17.1.json">interferometer / mge / alma_high — results/breakdown/interferometer/mge_breakdown_alma_high_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1.json">interferometer / mge / alma — results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1_sparse.json">interferometer / mge / alma — results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1.json">interferometer / mge / sma — results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1_sparse.json">interferometer / mge / sma — results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_dft_breakdown_sma_v2026.8.17.1.json">interferometer / mge / sma — results/breakdown/interferometer/mge_dft_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_hpc_a100_fp64.json">interferometer / mge / alma — results/breakdown/interferometer/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_hpc_a100_fp64_sparse.json">interferometer / mge / alma — results/breakdown/interferometer/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n1000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n1000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n2500.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n4000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n4000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n1000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n1000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n2500.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n4000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n4000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n1024.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n2500.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n4096.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n1024.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n2500.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n4096.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular / sma — results/breakdown/interferometer/pixelization_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_mp.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular / sma — results/breakdown/interferometer/pixelization_numba_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_levers.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_levers.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r4.25.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r4.25.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r6.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r6.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_jax_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_jax_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_jax_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_jax_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_reference_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_reference_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_reference_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_reference_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/delaunay_hpc_a100_fp64.json">interferometer / delaunay / sdp81 — results/breakdown/interferometer/sdp81/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / sdp81 — results/breakdown/interferometer/sdp81/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64.json">interferometer / mge / sdp81 — results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64_sparse.json">interferometer / mge / sdp81 — results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/pixelization_hpc_a100_fp64.json">interferometer / rectangular / sdp81 — results/breakdown/interferometer/sdp81/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / sdp81 — results/breakdown/interferometer/sdp81/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_mp.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter_main_control.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter_main_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_mp.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64_p4.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64_p4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_local_cpu_fp64.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_hpc_a100_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_hpc_ral_cpu_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_local_cpu_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_constant_split.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64_sparse.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp_sparse.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly_control.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_autotune4.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_autotune4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_control.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_sparse.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_euclid_v2026.8.17.1.json">imaging / delaunay / euclid — results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/comparison.json">imaging / other / ao — results/runtime/imaging/mge/ao/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/comparison.json">imaging / other / ao — results/runtime/imaging/mge/ao/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/mge_local_cpu_fp64.json">imaging / mge / ao — results/runtime/imaging/mge/ao/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/mge_local_cpu_mp.json">imaging / mge / ao — results/runtime/imaging/mge/ao/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_fp64.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_fp64_sparse.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_mp.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_mp_sparse.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_fp64.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_fp64_sparse.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_mp.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_mp_sparse.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/mge_likelihood_summary_hst_v2026.8.17.1.json">imaging / mge / hst — results/runtime/imaging/mge/mge_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge_mass_jax/mge_mass_jax_likelihood_summary_hst_v2026.8.17.1.json">imaging / mge / hst — results/runtime/imaging/mge_mass_jax/mge_mass_jax_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64_sparse.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp_sparse.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_recon_split.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_euclid_v2026.8.17.1.json">imaging / rectangular / euclid — results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba_mge_mass/pixelization_numba_mge_mass_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization_numba_mge_mass/pixelization_numba_mge_mass_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/alma/comparison.json">interferometer / delaunay / alma — results/runtime/interferometer/delaunay/alma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/alma/delaunay_local_cpu_mp.json">interferometer / delaunay / alma — results/runtime/interferometer/delaunay/alma/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/comparison.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/comparison.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_fp64.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_mp.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/comparison.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/comparison.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/mge_local_cpu_fp64.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/mge_local_cpu_mp.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/comparison.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/comparison.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_fp64.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_mp.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_image/image_plane_solved/image_plane_solved_summary_v2026.7.23.1.json">point_source_image / image_plane / unspecified — results/runtime/point_source_image/image_plane_solved/image_plane_solved_summary_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/comparison.json">point_source_source / other / unspecified — results/runtime/point_source_source/source_plane_solved/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/comparison.json">point_source_source / other / unspecified — results/runtime/point_source_source/source_plane_solved/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_a100_fp64.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_ral_cpu_fp64.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_summary_v2026.7.23.1.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_summary_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/export_probe.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/export_probe.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/export_probe.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/export_probe.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">experiments / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">experiments / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/trace_profile.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/trace_profile.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json">imaging / 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scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json</a></li></ul></details></noscript></section><section class="setup-browser" data-setup-browser="fit" aria-label="autofit_profiling setups"><script type="application/json" 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none"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/sampler_bookkeeping","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"sampler_bookkeeping","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/bare_sampler/breakdown/sampler_bookkeeping","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/other","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"other","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/bare_sampler/breakdown/other","reason":"the cell's committed median row"}],"hazards":[],"recommendations":[],"comparisons":[],"limitations":["Laptop CPU only, pinned to one host (DESKTOP-H143S82, WSL2) so Pulse's same-host rule pairs every record; the epic's release-sweep pin (euclid-ral-gpu-2, an A100 node) does not fit CPU Dynesty runs. Cross-machine reproduction on RAL is a later campaign.","Wall times vary up to ±40 % between sessions on this host, so breakdown shares are the comparable quantity, not seconds; the EP / graphical / parity rows are single fresh-process fits (repetitions 1).","Component times are exclusive monkey-patched wall-clock intervals; 'other' is wall minus the instrumented total, not a measured phase.","The EP / graphical rows carry a producer-side reproduction note against the committed autofit_workspace_developer baselines (runtime-drift-2x-1ms, in each row's `reference`); it is not a Pulse comparison, because those baselines recorded no host and no record pair exists.","The N30 EP and graphical rows reproduce the committed sanity FAIL; their wall and memory are real measurements of a fit whose parameter recovery fails, not of a correct fit.","Every record is unreviewed: the producer declares measurements, the Brain's profiling conductor judges them; nothing here is a verdict.","No temporal comparisons: one baseline per setup."]},"instance":"fit","label":"autofit","repo":"https://github.com/PyAutoLabs/autofit_profiling","commit":"58e56b9646da2181061b2e8d7907d6db2542ee07","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autofit_profiling/58e56b9646da2181061b2e8d7907d6db2542ee07/dashboard/","local":false,"remote_shards":true}</script><p data-id="load-status" role="status" aria-live="polite"></p><div data-id="navigation"></div><section data-id="results" aria-label="Selected setup" hidden></section><noscript><details><summary>Original setup evidence (JavaScript disabled)</summary><ul></ul></details></noscript></section><!-- decision-history:20f0256fc1d56eda82a910f81ddb71d393e76eeaf26e486d9c836b00282b99f9 --></div></details><details class="board-section"><summary><h2>Decision History</h2></summary><div class="board-section-body"><section id="decision-history"><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/interferometer-route-gate-cost-model.md" target="_blank" rel="noopener" title="Opens in a new tab">Route interferometer curvature by a calibrated two-constant cost model</a><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/adaptsplit-zero-weight-delaunay-only.md" target="_blank" rel="noopener" title="Opens in a new tab">Exclude zero-weight mappings from AdaptSplit pixel signals on Delaunay meshes only</a><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md" target="_blank" rel="noopener" title="Opens in a new tab">Keep Jacobi PDIP as the Mapper default and raw+polish as the MGE-only default</a></section></div></details><ul class="boards"><li class="muted" style="padding:.3rem 0">Boards:</li><li><a data-organ="organism" href="https://pyautolabs.github.io/PyAutoScientist/">Scientist</a></li><li><a data-organ="brain" href="https://pyautolabs.github.io/PyAutoBrain/">Brain</a></li><li><a data-organ="mind" href="https://pyautolabs.github.io/PyAutoMind/">Mind</a></li><li><a data-organ="cortex" href="https://pyautolabs.github.io/PyAutoCortex/">Cortex</a></li><li><a data-organ="memory" href="https://pyautolabs.github.io/PyAutoMemory/">Memory</a></li><li><a data-organ="eyes" href="https://pyautolabs.github.io/PyAutoEyes/">Eyes</a></li><li><a data-organ="ears" href="https://pyautolabs.github.io/PyAutoEars/">Ears</a></li><li><a data-organ="heart" href="https://pyautolabs.github.io/PyAutoHeart/">Heart</a></li><li><a data-organ="hands" href="https://pyautolabs.github.io/PyAutoHands/">Hands</a></li><li><a data-organ="insight" href="https://pyautolabs.github.io/PyAutoInsight/">Insight</a></li><li><a data-organ="dna" href="https://pyautolabs.github.io/PyAutoDNA/">DNA</a></li><li><a data-organ="nerves" href="https://pyautolabs.github.io/PyAutoNerves/">Nerves</a></li><li><a data-organ="broca" href="https://pyautolabs.github.io/PyAutoBroca/">Broca</a></li><li><a data-organ="gut" href="https://pyautolabs.github.io/PyAutoGut/">Gut</a></li></ul></main><script>const MAX_PROMPT_CHARS = 50000;
+<nav class="board-nav" aria-label="Board sections"><a class="board-nav-card" href="#campaigns"><span class="board-nav-label">Active campaigns</span></a><a class="board-nav-card" href="#evidence"><span class="board-nav-label">Profiling Results</span></a><a class="board-nav-card" href="#decision-history"><span class="board-nav-label">Decision History</span></a></nav><main><!-- pulse-campaigns:4e7b5e1126147b4593a7e870235fd9a5d367256202aaeb9408c0254298791675 --><details class="board-section"><summary><h2 id="campaigns">Active campaigns</h2></summary><div class="board-section-body"><div class="campaign-table"><table><thead><tr><th>Campaign</th><th>Status</th><th>Links</th></tr></thead><tbody><tr><td><strong>Setup baseline collection and scientific acceptance</strong><details id="campaign-setup-baseline" class="campaign-detail"><summary>2 tasks · next step</summary><p>Review the Pulse collection choices (candidate input hashes and 31-worker resource plan verified without execution) alongside project correctness audit #417. Resolve data origin/suitability, named host/stack/full settings, witness/tolerances and background-load method. No freeze, compute or acceptance authorized. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/setup_baseline_campaign.md">Freeze the setup baseline specification, then collect and review evidence</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/384" aria-label="Issue for Freeze the setup baseline specification, then collect and review evidence" title="Issue">↗</a></h4><p class="review-meta">needs-decision · high</p><p>HST/MGE raw fixed-parameter CPU runner merged in #416; Pulse handoff #55 merged and Mind implementation closed. Resolve exact production host/stack/data, full settings, independent correctness witness/tolerances and background-load method. No collection, campaign freeze or acceptance authorized; global compile dispatch remains unavailable.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/hst_mge_cpu_pilot.md">Prepare and review the HST/MGE fp64 CPU pilot</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/415" aria-label="Issue for Prepare and review the HST/MGE fp64 CPU pilot" title="Issue">↗</a></h4><p class="review-meta">needs-decision · high</p><p>Review the Pulse collection choices (candidate input hashes and 31-worker resource plan verified without execution) alongside project correctness audit #417. Resolve data origin/suitability, named host/stack/full settings, witness/tolerances and background-load method. No freeze, compute or acceptance authorized.</p></article></details></td><td><span class="pill">needs-decision</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/pull/416" aria-label="Evidence for Setup baseline collection and scientific acceptance" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-setup-baseline" data-open="campaign-setup-baseline" aria-label="Tasks for Setup baseline collection and scientific acceptance" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source image plane · CPU</strong><details id="campaign-point-source-image-cpu" class="campaign-detail"><summary>1 task · next step</summary><p>No new committed campaign measurements. Phases 1–4 remain shipped; epic closure is a human decision. Carried leftovers remain pending. RAL queue empty at review, but transfer preview timed out: cleanup folders and unpulled results unverified. Select one owed cleanup/measurement phase only after review. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement. RAL queue holds only euclid_dr1 jobs; cleanup folders and unpulled results still unverified.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pointsolver_cpu_speed_campaign_remainder.md">Point-source (single-source) CPU campaign — carried leftovers and completion evidence</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/370" aria-label="Issue for Point-source (single-source) CPU campaign — carried leftovers and completion evidence" title="Issue">↗</a></h4><p class="review-meta">ready · low</p><p>No new committed campaign measurements. Phases 1–4 remain shipped; epic closure is a human decision. Carried leftovers remain pending. RAL queue empty at review, but transfer preview timed out: cleanup folders and unpulled results unverified. Select one owed cleanup/measurement phase only after review.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04" aria-label="Evidence for Point-source image plane · CPU" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-image-cpu" data-open="campaign-point-source-image-cpu" aria-label="Tasks for Point-source image plane · CPU" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source image plane · A100</strong><details id="campaign-point-source-image-gpu" class="campaign-detail"><summary>1 task · next step</summary><p>Parked as research, human decision 2026-10-17 (chat) (&quot;just research to file&quot;). Reference measurement autolens_profiling#434 (issue #427, OPEN): job 412519 euclid-ral-gpu-2 exclusive A100, jax 0.10.2, fp64, 2026.10.17.1 private clone: single call 0.934 ms (pre-#768 0.910), vmap-256 0.0125 ms (0.0120), compile 6.54 s; forward gradient finite 16/16, equal to reverse to 1.6e-11; logL bit-exact; +2.7% not attributed (different node/date). A100 per-step breakdown rows unusable. Phase-2 inference fit measurement dropped; no baseline admitted.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_image_plane_gpu_breakdown.md">Point-source A100 speed-up campaign: profile and optimize with the shared breakdown</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoLens/issues/767" aria-label="Issue for Point-source A100 speed-up campaign: profile and optimize with the shared breakdown" title="Issue">↗</a></h4><p class="review-meta">parked · high</p><p>Parked, human decision 2026-10-17 (chat). Reference A100 measurement autolens_profiling#434 (OPEN, job 412519): 0.934 ms single call, 0.0125 ms at vmap-256, forward gradient fixed; phase-2 inference fit measurement dropped.</p></article></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_gpu_breakdown.md" aria-label="Evidence for Point-source image plane · A100" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-image-gpu" data-open="campaign-point-source-image-gpu" aria-label="Tasks for Point-source image plane · A100" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Cluster PointSolver robustness and performance</strong><details id="campaign-cluster-pointsolver" class="campaign-detail"><summary>2 tasks · next step</summary><p>No campaign wiki/results change or new matching implementation claim found. Representative cluster baseline and observable-overflow policy remain unstarted. Choose one bounded phase; inherited single-source results do not qualify a representative cluster baseline. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/cluster_pointsolver_speed.md">Cluster PointSolver — robustness, analysis settings and performance</a></h4><p class="review-meta">ready · normal</p><p>Unissued, no claim. Pick one bounded phase per Mind epics.md: observable-overflow invalid-result policy + CI witness, or a representative-data baseline cell.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_solver_profiling_cells.md">PointSolver profiling cells: lensed quasar → cluster runtime tier → single/multi-source → multiplane</a></h4><p class="review-meta">ready · normal</p><p>Unissued, no claim. Candidate first cell: a representative-data cluster likelihood_runtime baseline (single-source preset).</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/cluster_pointsolver.md" aria-label="Evidence for Cluster PointSolver robustness and performance" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-cluster-pointsolver" data-open="campaign-cluster-pointsolver" aria-label="Tasks for Cluster PointSolver robustness and performance" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Interferometer likelihood and streaming</strong><details id="campaign-interferometer" class="campaign-detail"><summary>5 tasks · next step</summary><p>Evening 2026-10-17: human decision 2026-10-17 (chat): interferometer CPU work is exhaustive and closed; curvature preload parked (&quot;we will come back to it soon&quot;); GPU levers &quot;file wiki research but not make source code changes for now&quot;, so no library prompt for JAX FFT pruning. Parked at knowledge-complete. Open PRs (human&#x27;s to merge or close): L2 symmetric direct_conv PyAutoArray#636 (pending-release; real Aris RAL 1.49×/1.53×/1.58× at 1,459/2,577/5,746 vertices, logL to round-off; decision record direct-conv-symmetric-gate) + record autolens_profiling#425; A100 FFT levers #433 (pruned FFT 1.08×/1.18×/1.41× whole likelihood at alma/alma_high/jvla; pixel-scale coarsening fails the 0.5-nat bar); streaming 1e8 CPU/A100 + memo after-measurement #435. Route-constant refit (RAL FFT/full direct_conv 0.68 vs 0.89 predicted) deferred. Merge #425 after #636. Six open autolens_profiling PRs (#425, #431, #432, #433, #434, #435) all regenerate the shared catalogue/dashboard: each merge after the first needs a rebase + re-render; suggested order #425, #432, #431, #433, #434, #435. #425 after PyAutoArray#636.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_fixed_mapper_curvature_preload.md">Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`</a></h4><p class="review-meta">parked · normal</p><p>Parked, human decision 2026-10-17 (chat): interferometer CPU work closed; &quot;we will come back to it soon&quot;. Phase-1 Mind draft unstarted.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_nnls_memo_scattered_stream_guard.md">fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoArray/issues/613" aria-label="Issue for fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)" title="Issue">↗</a></h4><p class="review-meta">parked · medium</p><p>After-measurement at PR-open autolens_profiling#435 (issue #426, OPEN; job 412526, ral compute-10-2, load ~10, 1 thread, 6 rounds, paired bootstrap 90% CI): Delaunay iid #332 flags 1.918 [1.887,1.925] (2.17 in #332); 15 timed solves 1.018 [1.013,1.023]; Delaunay walk 0.46; rect iid 0.887/0.987; rect walk 0.15; FoM identical. Not evidence #613 is fixed. Research owed: Nautilus-replay witness, varied-seed rounds. Parked, human decision 2026-10-17 (chat).</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_streaming_scaling.md">Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/368" aria-label="Issue for Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities" title="Issue">↗</a></h4><p class="review-meta">parked · high</p><p>Phase 2 at PR-open autolens_profiling#435 (issue #368 reopened, OPEN): from_stream chunk 65536 fp64, 1e8 vis RAL CPU 707.5 s / 1.78 GB RSS, A100 157.3 s / 1.48 GB device; compile ~3 s constant. In-memory fails at 2.5e5 (RAL 10 GB cap) and 4e6 (A100); uncapped CPU ~7.5 kB/vis. Optional 2e8 rows are research; library candidate (chunk transformer.image_from) unfiled per human decision 2026-10-17 (chat). Parked.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_large_mask_aris.md">Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoPulse/issues/62" aria-label="Issue for Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model" title="Issue">↗</a></h4><p class="review-meta">active · high</p><p>L2 shipped to PR: PyAutoArray#636 (head 62a1e3ac, pending-release) + autolens_profiling#425 (results note), both OPEN, CI green. RAL real-data witness 1.49×/1.53×/1.58× at 1,459/2,577/5,746 vertices (pred 1.48×/1.50×/1.53×), logL to round-off; peak RSS 43 GB without NUFFT chunking. Decision record direct-conv-symmetric-gate added. Route-constant refit (0.68 measured vs 0.89 predicted) deferred: CPU lane closed, human decision 2026-10-17 (chat). Remaining: human merge of #636 then #425, and release.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_w_tilde_fft_size_levers.md">Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space pixel scale on the A100</a></h4><p class="review-meta">parked · normal</p><p>A100 research filed at PR-open autolens_profiling#433 (issue #429, OPEN; job 412515): pruned padded FFT 1.08×/1.18×/1.41× whole likelihood at alma/alma_high/jvla, F exact to 1.7e-15; pixel-scale coarsening shifts log evidence by +1,310 to +2,757 nats (fails). No library prompt, human decision 2026-10-17 (chat). Parked.</p></article></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09f/wiki/campaigns/interferometer_likelihood.md" aria-label="Evidence for Interferometer likelihood and streaming" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-interferometer" data-open="campaign-interferometer" aria-label="Tasks for Interferometer likelihood and streaming" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Linear-solver accuracy and cost</strong><details id="campaign-linear-solver" class="campaign-detail"><summary>0 tasks · next step</summary><p>Retain the 2026-10-08 human decision: Jacobi for Mapper, raw+polish for MGE-only, no default change. Project wiki still says decision pending (stale). No new solver measurements after the recorded phase-5 results; release 2026.10.12.1 does not supply a rerun. Wide-prior/interferometer gaps remain evidence gaps, not authorized tasks. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md" aria-label="Evidence for Linear-solver accuracy and cost" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-linear-solver" data-open="campaign-linear-solver" aria-label="Tasks for Linear-solver accuracy and cost" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Production imaging and likelihood breakdown</strong><details id="campaign-imaging" class="campaign-detail"><summary>3 tasks · next step</summary><p>Production over-sampling phase 1 at PR-open: autolens_profiling#431 (issue #430, OPEN, CPU smoke 8/8): --over-sampling {flat1,production} on the 4 JAX Delaunay/DelaunayNN cells, euclid/hst presets. Findings: HST flat-1 adapt_split pin fails on unmodified main (29155.000 vs 29140.296); numba cells&#x27; production threshold never reaches sub 4 for HST; rectangular JAX cells uncovered. Phase 2 (A100 production re-measure) needs-decision (human decision 2026-10-17 (chat): undecided). DelaunayNN imaging moved to the parked delaunay-nn-cpu campaign. Post-certified, quick-update plotting and other tasks unchanged.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/imaging_production_over_sampling.md">Profile imaging pixelizations at production over-sampling</a></h4><p class="review-meta">needs-decision · normal</p><p>Phase 1 at PR-open: autolens_profiling#431 (issue #430, OPEN, CPU smoke 8/8). Phase 2 A100 production re-measure needs-decision (human decision 2026-10-17 (chat): undecided); requires #431 merged and compute authorization. Findings: HST flat-1 adapt_split pin fails on main; numba HST threshold never reaches sub 4; rectangular JAX cells uncovered.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/post_certified_solver_likelihood_breakdown.md">Assess remaining likelihood bottlenecks after certified solver integration on CPU and GPUs</a></h4><p class="review-meta">needs-slicing · normal</p><p>Blocked-by is stale: resolved 2026-09-27 (PyAutoArray#567 in 2026.9.26.1). Unissued; scope one bounded phase from the full task contract.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/quick_update_plotting_cost.md">Quick-update plotting cost — minutes per update, and it is not JAX compile</a></h4><p class="review-meta">ready · medium</p><p>Unchanged; targets PyAutoLens/PyAutoGalaxy (PyAutoLens#680 closed 2026-07-31, no follow-up issue). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/post_certified_breakdown.md" aria-label="Evidence for Production imaging and likelihood breakdown" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-imaging" data-open="campaign-imaging" aria-label="Tasks for Production imaging and likelihood breakdown" title="Open tasks">☷</a></div></td></tr><tr><td><strong>DelaunayNN (Sibson) mesh at Delaunay cost on the numba/NumPy CPU path</strong><details id="campaign-delaunay-nn-cpu" class="campaign-detail"><summary>1 task · next step</summary><p>Created parked, human decision 2026-10-17 (chat): &quot;file in research to come back to in the future&quot;. PyAutoArray#637 (issue #629, OPEN, pending-release, CI 3 legs green): RAL compute-10-4 exclusive 1 thread A-B-B-A ×2, 1,030 vertices / 15,380 px: NN sub4 0.569→0.392 s (2.20× Delaunay, was 3.00×), sub2 0.235→0.189 s (1.75×); logL bit-identical; fixes the size-0 Sibson NaN case. ~1.5× target not met; remaining cost is Sibson (~0.16 s) + wider curvature rows. Target and two hazards recorded as future work, not filed.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/delaunay_nn_cpu_imaging.md">DelaunayNN (Sibson) imaging likelihood at close to Delaunay cost on the numba/NumPy CPU path</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoArray/issues/629" aria-label="Issue for DelaunayNN (Sibson) imaging likelihood at close to Delaunay cost on the numba/NumPy CPU path" title="Issue">↗</a></h4><p class="review-meta">parked · normal</p><p>Parked, human decision 2026-10-17 (chat). PyAutoArray#637 (OPEN, pending-release) at PR-open: sub4 2.20× Delaunay (was 3.00×), sub2 1.75×; logL bit-identical. Future (unfiled): ~1.5× target; hazards sub_slim_indexes_for_pix_index padding and Sibson rows with mid-row gaps.</p></article></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoArray/pull/637" aria-label="Evidence for DelaunayNN (Sibson) mesh at Delaunay cost on the numba/NumPy CPU path" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-delaunay-nn-cpu" data-open="campaign-delaunay-nn-cpu" aria-label="Tasks for DelaunayNN (Sibson) mesh at Delaunay cost on the numba/NumPy CPU path" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Measurement reliability and profiling tools</strong><details id="campaign-measurement-tools" class="campaign-detail"><summary>8 tasks · next step</summary><p>Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415 closed. No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo interpretation, fresh runtime repeats and other tools remain pending. #418 and #417 open. Evening 2026-10-17 merge-order note: Six open autolens_profiling PRs (#425, #431, #432, #433, #434, #435) all regenerate the shared catalogue/dashboard: each merge after the first needs a rebase + re-render; suggested order #425, #432, #431, #433, #434, #435. #425 after PyAutoArray#636.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/mge_likelihood_breakdown_steps_are_cumulative_an.md">MGE likelihood_breakdown steps are cumulative and `linear_gaussians` is reported as 0</a></h4><p class="review-meta">ready · low</p><p>Unchanged and unissued (no GitHub refs, 2026-10-04). Select one bounded step when measurement-tools is prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/gradient_cost_probe.md">A gradient-cost probe: forward vs `value_and_grad` ms/eval and a strict FD check, on any registry cell</a></h4><p class="review-meta">ready · low</p><p>Unissued. New context 2026-10-07: Heart unit-test timing flagged PyAutoLens forward-gradient tests +150%/+96% after PyAutoLens#768 rewrote that test file; not a controlled comparison, and this probe is the right instrument. Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/numba_breakdown_harness_memo_blind.md">Numba breakdown harness: perturb the instance so the operated-matrix memo cannot hide a step</a></h4><p class="review-meta">ready · medium</p><p>Unchanged and unissued (PyAutoArray#496 closed 2026-08-27). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/search_settings_estimation_infrastructure.md">Search settings-estimation + profiling infrastructure (n_starts / batch_size / n_batch)</a></h4><p class="review-meta">ready · normal</p><p>Unchanged and unissued (autolens_profiling#82 closed 2026-08-18). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/jax_compile_probe_needs_own_cell_builder.md">jax_compile/probe.py lost its cell builder with the searches tier — give profiling its own</a></h4><p class="review-meta">active · high</p><p>Manual HST/MGE fp64 jit builder #414 and separate raw pilot runner #416 merged. Bounded Mind implementations complete; wider builder coverage/global compile dispatch still unavailable. Agree a next capability or separately authorize a qualified pilot; no production compute authorized. Check-in 2026-10-17: autolens_profiling#418 (pilot qualification design) open since 2026-10-13 with CI green and no review; #417 open; no new measurements.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/profile_lens_aggregator_needs_config_dir.md">profile_lens_aggregator.py cannot run from the autolens_workspace_developer root: no config/ directory</a></h4><p class="review-meta">ready · low</p><p>Unchanged: workspace_developer main still has no root config/ (bug presumed live, not re-run). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/gradient_pixelization_pin_residual_drift.md">jax_profiling/gradient/imaging/pixelization.py: 3.2% of its pin move is unattributed</a></h4><p class="review-meta">ready · normal</p><p>Unchanged and unissued (PyAutoArray#490 merged 2026-08-26). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pair_jax_xla_env_vars_with_measured.md">Pair JAX/XLA env vars with measured compile and run times, per backend</a></h4><p class="review-meta">ready · high</p><p>Unchanged and unissued. Context: the jax-version policy is now released (2026.10.7.1 libraries require Nerves with the JAX deadlock exclusions). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/pull/416" aria-label="Evidence for Measurement reliability and profiling tools" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-measurement-tools" data-open="campaign-measurement-tools" aria-label="Tasks for Measurement reliability and profiling tools" title="Open tasks">☷</a></div></td></tr><tr><td><strong>PyAutoFit profiling bootstrap</strong><details id="campaign-autofit" class="campaign-detail"><summary>1 task · next step</summary><p>B4a (autofit_profiling#3) and B4b (autofit_profiling#5, issue #4 closed, merged 2026-10-15) shipped: search.fit breakdown cell, profiling-summary@2 exporter, EP/graphical N3/N10/N30 baselines and analytic parity cell; #6 repointed RAL paths to the category layout (2026-10-15). Pulse fit fixture/campaign merged (#60, #61); fit receipt ok at autofit_profiling 58e56b9 (31 records, fetched 2026-10-15T22:58Z), so the post-deploy refresh is done. Epic 2 not opened: next bounded step is a human-scoped epic-2 campaign row; B5c inference work belongs to Insight, not Pulse.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/autofit_profiling_bootstrap.md">autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic</a></h4><p class="review-meta">active · normal</p><p>B4a (autofit_profiling#3) and B4b (autofit_profiling#5, issue #4 closed, merged 2026-10-15) shipped: search.fit breakdown cell, profiling-summary@2 exporter, EP/graphical N3/N10/N30 baselines and analytic parity cell; #6 repointed RAL paths to the category layout (2026-10-15). Pulse fit fixture/campaign merged (#60, #61); fit receipt ok at autofit_profiling 58e56b9 (31 records, fetched 2026-10-15T22:58Z), so the post-deploy refresh is done. Epic 2 not opened: next bounded step is a human-scoped epic-2 campaign row; B5c inference work belongs to Insight, not Pulse.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autofit_profiling/issues/4" aria-label="Evidence for PyAutoFit profiling bootstrap" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-autofit" data-open="campaign-autofit" aria-label="Tasks for PyAutoFit profiling bootstrap" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Critical curves and evaluation grids</strong><details id="campaign-critical-curves" class="campaign-detail"><summary>0 tasks · next step</summary><p>No new campaign measurement. Phase 3b cap fix remains shipped; project wiki still incorrectly says #646 awaits merge/release. Seed coverage and path completion remain unresolved; retain marching-squares decision and ask the human whether to scope the next phase. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">needs-decision</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/critical_curves.md" aria-label="Evidence for Critical curves and evaluation grids" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-critical-curves" data-open="campaign-critical-curves" aria-label="Tasks for Critical curves and evaluation grids" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Certified positive solver</strong><details id="campaign-certified-solver" class="campaign-detail"><summary>0 tasks · next step</summary><p>Parked unchanged. No new campaign evidence; existing C2/library/workspace drafts remain pending. Do not infer admission from solver timing, and do not change default or restart the campaign. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/certified_positive_solver.md" aria-label="Evidence for Certified positive solver" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-certified-solver" data-open="campaign-certified-solver" aria-label="Tasks for Certified positive solver" title="Open tasks">☷</a></div></td></tr></tbody></table></div><p class="review-meta">Open means tracked, not necessarily running. <a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/campaigns.yaml">Full ledger ↗</a></p></div></details><details class="board-section"><summary><h2 id="evidence">Profiling Results</h2></summary><div class="board-section-body"><section class="setup-browser" data-setup-browser="lens" aria-label="autolens_profiling setups"><script type="application/json" class="setup-payload">{"catalogue":{"archive_record_count":20709,"comparison_policy":{"id":"no-temporal-comparisons"},"comparisons":[],"coverage":{"excluded":[],"expected":{"baseline_cells":29,"cells":101,"planned_slots":435},"observed":{"records":112,"selected":98,"setups":540}},"evidence_shards":[{"axes":["breakdown"],"axis_devices":{"breakdown":["cpu"]},"devices":["cpu"],"path":"catalogue/shards/eb112c2ba02f1d8956d3.json","precisions":["precision not 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no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"point_source_source","id":"baseline/point_source_source/source_plane/unspecified","instrument":null,"label":"point_source_source/source_plane/unspecified · baseline pending","model":"source_plane","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}},{"configuration":{"image_pixels_masked":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"image_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"n_vis":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"oversampling":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"dimensionless","value":null},"preloads":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"cluster","id":"baseline/cluster/image_plane/unspecified","instrument":null,"label":"cluster/image_plane/unspecified · baseline pending","model":"image_plane","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}},{"configuration":{"image_pixels_masked":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"image_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"n_vis":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"oversampling":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"dimensionless","value":null},"preloads":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"multi_dataset","id":"baseline/multi_dataset/delaunay/unspecified","instrument":null,"label":"multi_dataset/delaunay/unspecified · baseline pending","model":"delaunay","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}}],"static_memory_estimates":[{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_delaunay.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay","per_replica":{"unit":"MiB","value":921.913},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":62},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_delaunay_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay","per_replica":{"unit":"MiB","value":131.505},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_mge.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"mge","per_replica":{"unit":"MiB","value":16.411},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_mge_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"mge","per_replica":{"unit":"MiB","value":16.411},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_pixelization.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"rectangular","per_replica":{"unit":"MiB","value":931.004},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":62},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_pixelization_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"rectangular","per_replica":{"unit":"MiB","value":94.906},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/delaunay_nn/vmap_probe_delaunay_nn_batch64_cufft_failed.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay_nn","per_replica":{"unit":"MiB","value":770.97},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"point_source_source","evidence":{"fragment":null,"path":"results/runtime/point_source_source/source_plane_solved/vmap_probe_source_plane_solved.json"},"instrument":"simple","kind":"xla_static_buffer_estimate","model":"source_plane_solved","per_replica":{"unit":"MiB","value":0.0},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64}],"unbound_findings":[{"description":"The conversion maps every magnitude at or above 0.999 to q=0.000500250125062532. NumPy reaches the thin valid annulus below one but its constructor guard blocks the beyond-unit region; JAX arrays and tracers bypass that concrete-scalar guard.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.ell_comps.magnitude-saturation","path":"results/hazards/hazards_index.json"},"id":"component.ell_comps.magnitude-saturation","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"ell_comps","title":"ell_comps magnitude saturates at 0.999"},{"description":"A second, load-bearing saturation prevents the q=1 divisions in the elliptical Isothermal deflection from becoming 0/0. Its affected ell_comps disk is too small for prior-sampling discovery.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.isothermal.near-spherical-saturation","path":"results/hazards/hazards_index.json"},"id":"component.isothermal.near-spherical-saturation","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"isothermal","title":"Isothermal caps near-spherical axis ratios at 0.99999"},{"description":"`_wofz_rational` selects one of three rational branches with `xp.where` on |z|^2 and Im(z)^2. The value is continuous to ~1e-6 across a seam but the derivative is not, so `jax.grad` of any MGE deflection carries a kink wherever a grid point crosses a boundary. Autodiff derivative error just above the r2=2.5 seam reaches 7.71e-06 relative, against 1.3e-14 for the SciPy path the numpy backend uses.","discovery":{"models":[],"reason":"Shared MGE mass-profile deflection component study, not specifically the imaging MGE light-model likelihood. Consult the recorded component and revision before applying this finding.","shared":true},"evidence":{"fragment":"/findings/component.mge.faddeeva-seam-gradient","path":"results/hazards/hazards_index.json"},"id":"component.mge.faddeeva-seam-gradient","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"mge","title":"The JAX Faddeeva routine's region seams make its derivative discontinuous"},{"description":"The elliptical Faddeeva form is singular at q = 1, so the MGE machinery clamps the axis ratio to 0.9999. The numpy path escapes through an exact radial branch; the JAX path cannot, and every *Sph MGE-routed profile is biased by up to 6.18e-05 relative with a spurious cross-axis deflection of 0.000139 arcsec.","discovery":{"models":[],"reason":"Shared MGE mass-profile deflection component study, not specifically the imaging MGE light-model likelihood. Consult the recorded component and revision before applying this finding.","shared":true},"evidence":{"fragment":"/findings/component.mge.spherical-clamp-bias","path":"results/hazards/hazards_index.json"},"id":"component.mge.spherical-clamp-bias","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"mge","title":"Spherical MGE profiles are evaluated as ellipses at the q = 0.9999 clamp on JAX"},{"description":"The JAX backend replaces the exact SciPy hyp2f1 angular factor with a fixed 20-term omega series. Relative deflection error grows continuously to 0.297 over the measured factor curve.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.power-law.series-vs-hyp2f1-divergence","path":"results/hazards/hazards_index.json"},"id":"component.power-law.series-vs-hyp2f1-divergence","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"power_law","title":"PowerLaw's 20-term JAX series diverges from hyp2f1 at high ellipticity"},{"description":"The spherical radial coordinate returns the correct finite value zero at the profile centre, but JAX differentiation of sqrt(y^2+x^2) returns a non-finite gradient there.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.spherical-geometry.radial-sqrt-gradient-at-zero","path":"results/hazards/hazards_index.json"},"id":"component.spherical-geometry.radial-sqrt-gradient-at-zero","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"spherical_geometry","title":"Radial sqrt has a non-finite gradient at r=0"},{"description":"Using only the diagonal entries the policy actually touches, the absolute floor reaches 4.457e-05 of their scale. A reference-calibrated scale-aware counterfactual holds its fraction fixed with maximum relative output error 3.395e-05.","discovery":{"models":[{"dataset":"imaging","model":"rectangular"}],"reason":"The reproducer is the imaging rectangular-source likelihood fixture. This is related model evidence; it does not establish applicability to other configurations or measured versions.","shared":false},"evidence":{"fragment":"/findings/likelihood.imaging-pixelization.absolute-conditioning-floors","path":"results/hazards/hazards_index.json"},"id":"likelihood.imaging-pixelization.absolute-conditioning-floors","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"imaging_pixelization","title":"Absolute inversion floors move with dataset scale"},{"description":"The NumPy full-likelihood scan changes active support at 5 bounded Einstein-radius location(s). The report measures explicit epsilon-neighbourhood mass, not exact-kink prior mass.","discovery":{"models":[{"dataset":"imaging","model":"rectangular"}],"reason":"The reproducer is the imaging rectangular-source likelihood fixture. This is related model evidence; it does not establish applicability to other configurations or measured versions.","shared":false},"evidence":{"fragment":"/findings/likelihood.imaging-pixelization.nnls-active-set-kinks","path":"results/hazards/hazards_index.json"},"id":"likelihood.imaging-pixelization.nnls-active-set-kinks","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"imaging_pixelization","title":"NNLS support changes create likelihood kinks"},{"description":"2 argmax-switch location(s) found in [0.0, 3.0] (4 truth positions, 0 of them where max_sep is already over threshold — i.e. a second, threshold-independent gradient kink in the ACTIVE penalty region, from max_sep itself being a max of several smooth per-position functions).","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.argmax-switch","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.argmax-switch","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"Which position-pair sets max_sep switches discontinuously across the sweep"},{"description":"Across 40/401 grid points with max_sep \u003c threshold, penalty value and gradient are exactly zero (max |value|=0.000e+00, max |grad|=0.000e+00) — a gradient-search lane deep inside the fence gets NO signal at all from the positions term, by construction.","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.interior-plateau","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.interior-plateau","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"PositionsLH penalty is an exact-zero, zero-gradient plateau inside threshold"},{"description":"Sweeping einstein_radius in [0.0, 3.0] (401 pts, threshold=0.3, factor=1e+08), a tight (+/-1e-6) probe at each of 2 max_sep==threshold crossing(s) shows the penalty VALUE is continuous there (max gap 2.006e+02) while its jax-autodiff GRADIENT jumps by up to 2.006e+08 — zero on the interior side, nonzero on the exterior side. A gradient optimizer sees a step-function force switching on at the fence, not a smooth potential.","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.threshold-hinge","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.threshold-hinge","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"PositionsLH penalty has a C0 hinge at the threshold crossing"},{"description":"The configured 1.0e-03 diagonal add ranges from dominant to negligible as synthetic curvature scale changes. Detection needs no dataset; judging scientific relevance does.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/matrix.curvature.absolute-diagonal-floor","path":"results/hazards/hazards_index.json"},"id":"matrix.curvature.absolute-diagonal-floor","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"matrix","subject_name":"curvature_matrix","title":"Absolute curvature-diagonal floor is scale dependent"}],"valid_until":null,"version":2},"instance":"lens","label":"PyAutoLens","repo":"https://github.com/PyAutoLabs/autolens_profiling","commit":"fd29902bd09ff5defcab5e9007a547423048e8aa","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autolens_profiling/fd29902bd09ff5defcab5e9007a547423048e8aa/dashboard/","local":false,"remote_shards":true}</script><p data-id="load-status" role="status" aria-live="polite"></p><div data-id="navigation"></div><section data-id="results" aria-label="Selected setup" hidden></section><noscript><details><summary>Original setup evidence (JavaScript disabled)</summary><ul><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_breakdown_v2026.7.23.1.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_breakdown_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_breakdown_v2026.7.6.649.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_breakdown_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_hpc_ral_cpu_fp64.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_local_cpu_fp64.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/source_plane_breakdown_v2026.7.23.1.json">cluster / source_plane / unspecified — results/breakdown/cluster/source_plane_breakdown_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/source_plane_breakdown_v2026.7.6.649.json">cluster / source_plane / unspecified — results/breakdown/cluster/source_plane_breakdown_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/datacube/delaunay_hpc_a100_fp64.json">datacube / delaunay / alma_high — results/breakdown/datacube/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/datacube/delaunay_hpc_a100_mp.json">datacube / delaunay / alma_high — results/breakdown/datacube/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_autotune4.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_autotune4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1_control.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_recon_split.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_mp.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_mp_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_breakdown_hst_v2026.8.17.1.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly_control.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk1024.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk2048.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk2048.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk4096.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk512.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk512.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_control.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_recon_split.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_vmap64.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_vmap64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_numba_breakdown_euclid_v2026.8.17.1.json">imaging / delaunay / euclid — results/breakdown/imaging/delaunay_numba_breakdown_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_numba_breakdown_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_numba_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2_control.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3_control.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n3000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n3000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_nn_hpc_a100_fp64_fixed_light.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_delaunay_nn_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_hpc_a100_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_t1.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_tall.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_hpc_a100_fp64_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_t1.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_tall.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n1225_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n1225_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n484_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n484_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_hpc_a100_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_t1.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_tall.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library_vmap2.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library_vmap2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_mp_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n1225_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n1225_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n484_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n484_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3025_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3025_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n12000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n3000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n5000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_nn_hpc_a100_fp64_matrix_free.json">imaging / delaunay_nn / hst — results/breakdown/imaging/matrix_free_delaunay_nn_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n12000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n3000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n5000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.5.29.4.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.7.6.649.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.8.17.1.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_hpc_a100_fp64.json">imaging / mge / hst — results/breakdown/imaging/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_recon_split.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_mp.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_mp_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_euclid_v2026.8.17.1.json">imaging / rectangular / euclid — results/breakdown/imaging/pixelization_numba_breakdown_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_mp.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64_sparse.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_mp.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_mp.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay / sma — results/breakdown/interferometer/delaunay_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_constant_split.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_mp.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay / sma — results/breakdown/interferometer/delaunay_numba_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_levers.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_levers.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r4.25.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r4.25.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r6.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r6.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_jax_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_jax_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_jax_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_jax_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_reference_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_reference_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_reference_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_reference_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_source_loop_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_source_loop_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_symmetric_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_symmetric_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_two_stage_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_two_stage_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/delaunay_hpc_a100_mp.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_fp64.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_fp64_sparse.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_mp.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/pixelization_hpc_a100_fp64.json">interferometer / rectangular / jvla — results/breakdown/interferometer/jvla/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/pixelization_hpc_a100_mp.json">interferometer / rectangular / jvla — results/breakdown/interferometer/jvla/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_high_v2026.8.17.1.json">interferometer / mge / alma_high — results/breakdown/interferometer/mge_breakdown_alma_high_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1.json">interferometer / mge / alma — results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1_sparse.json">interferometer / mge / alma — results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1.json">interferometer / mge / sma — results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1_sparse.json">interferometer / mge / sma — results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_dft_breakdown_sma_v2026.8.17.1.json">interferometer / mge / sma — results/breakdown/interferometer/mge_dft_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_hpc_a100_fp64.json">interferometer / mge / alma — results/breakdown/interferometer/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_hpc_a100_fp64_sparse.json">interferometer / mge / alma — results/breakdown/interferometer/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n1000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n1000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n2500.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n4000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n4000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n1000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n1000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n2500.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n4000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n4000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n1024.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n2500.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n4096.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n1024.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n2500.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n4096.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular / sma — results/breakdown/interferometer/pixelization_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_mp.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular / sma — results/breakdown/interferometer/pixelization_numba_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_levers.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_levers.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r4.25.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r4.25.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r6.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r6.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_jax_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_jax_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_jax_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_jax_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_reference_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_reference_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_reference_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_reference_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/delaunay_hpc_a100_fp64.json">interferometer / delaunay / sdp81 — results/breakdown/interferometer/sdp81/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / sdp81 — results/breakdown/interferometer/sdp81/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64.json">interferometer / mge / sdp81 — results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64_sparse.json">interferometer / mge / sdp81 — results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/pixelization_hpc_a100_fp64.json">interferometer / rectangular / sdp81 — results/breakdown/interferometer/sdp81/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / sdp81 — results/breakdown/interferometer/sdp81/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_mp.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter_main_control.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter_main_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_mp.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64_p4.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64_p4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_local_cpu_fp64.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_hpc_a100_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_hpc_ral_cpu_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_local_cpu_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_constant_split.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64_sparse.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp_sparse.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly_control.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_autotune4.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_autotune4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_control.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_sparse.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_euclid_v2026.8.17.1.json">imaging / delaunay / euclid — results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/comparison.json">imaging / other / ao — results/runtime/imaging/mge/ao/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/comparison.json">imaging / other / ao — results/runtime/imaging/mge/ao/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/mge_local_cpu_fp64.json">imaging / mge / ao — results/runtime/imaging/mge/ao/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/mge_local_cpu_mp.json">imaging / mge / ao — results/runtime/imaging/mge/ao/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_fp64.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_fp64_sparse.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_mp.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_mp_sparse.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_fp64.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_fp64_sparse.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_mp.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_mp_sparse.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/mge_likelihood_summary_hst_v2026.8.17.1.json">imaging / mge / hst — results/runtime/imaging/mge/mge_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge_mass_jax/mge_mass_jax_likelihood_summary_hst_v2026.8.17.1.json">imaging / mge / hst — results/runtime/imaging/mge_mass_jax/mge_mass_jax_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64_sparse.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp_sparse.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_recon_split.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_euclid_v2026.8.17.1.json">imaging / rectangular / euclid — results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba_mge_mass/pixelization_numba_mge_mass_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization_numba_mge_mass/pixelization_numba_mge_mass_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/alma/comparison.json">interferometer / delaunay / alma — results/runtime/interferometer/delaunay/alma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/alma/delaunay_local_cpu_mp.json">interferometer / delaunay / alma — results/runtime/interferometer/delaunay/alma/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/comparison.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/comparison.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_fp64.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_mp.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/comparison.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/comparison.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/mge_local_cpu_fp64.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/mge_local_cpu_mp.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/comparison.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/comparison.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_fp64.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_mp.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_image/image_plane_solved/image_plane_solved_summary_v2026.7.23.1.json">point_source_image / image_plane / unspecified — results/runtime/point_source_image/image_plane_solved/image_plane_solved_summary_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/comparison.json">point_source_source / other / unspecified — results/runtime/point_source_source/source_plane_solved/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/comparison.json">point_source_source / other / unspecified — results/runtime/point_source_source/source_plane_solved/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_a100_fp64.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_ral_cpu_fp64.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_summary_v2026.7.23.1.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_summary_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/export_probe.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/export_probe.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/export_probe.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/export_probe.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">experiments / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">experiments / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/trace_profile.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/trace_profile.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json">imaging / 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scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json</a></li></ul></details></noscript></section><section class="setup-browser" data-setup-browser="fit" aria-label="autofit_profiling setups"><script type="application/json" 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row"},{"id":"select/gaussian_x1/nautilus/search_fit/breakdown/proposal_sampling","setup_id":"gaussian_x1/nautilus/search_fit","axis":"breakdown","metric":"proposal_sampling","unit":"s","method_id":"search-fit-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/search_fit/breakdown/proposal_sampling","reason":"the cell's committed median 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row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/checkpoint_io","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"checkpoint_io","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"inapplicable","record_id":null,"reason":"the bare sampler is run without a checkpoint file, so it writes none"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/sampler_bookkeeping","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"sampler_bookkeeping","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/bare_sampler/breakdown/sampler_bookkeeping","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/other","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"other","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/bare_sampler/breakdown/other","reason":"the cell's committed median row"}],"hazards":[],"recommendations":[],"comparisons":[],"limitations":["Laptop CPU only, pinned to one host (DESKTOP-H143S82, WSL2) so Pulse's same-host rule pairs every record; the epic's release-sweep pin (euclid-ral-gpu-2, an A100 node) does not fit CPU Dynesty runs. Cross-machine reproduction on RAL is a later campaign.","Wall times vary up to ±40 % between sessions on this host, so breakdown shares are the comparable quantity, not seconds; the EP / graphical / parity rows are single fresh-process fits (repetitions 1).","Component times are exclusive monkey-patched wall-clock intervals; 'other' is wall minus the instrumented total, not a measured phase.","The EP / graphical rows carry a producer-side reproduction note against the committed autofit_workspace_developer baselines (runtime-drift-2x-1ms, in each row's `reference`); it is not a Pulse comparison, because those baselines recorded no host and no record pair exists.","The N30 EP and graphical rows reproduce the committed sanity FAIL; their wall and memory are real measurements of a fit whose parameter recovery fails, not of a correct fit.","Every record is unreviewed: the producer declares measurements, the Brain's profiling conductor judges them; nothing here is a verdict.","No temporal comparisons: one baseline per setup."]},"instance":"fit","label":"autofit","repo":"https://github.com/PyAutoLabs/autofit_profiling","commit":"58e56b9646da2181061b2e8d7907d6db2542ee07","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autofit_profiling/58e56b9646da2181061b2e8d7907d6db2542ee07/dashboard/","local":false,"remote_shards":true}</script><p data-id="load-status" role="status" aria-live="polite"></p><div data-id="navigation"></div><section data-id="results" aria-label="Selected setup" hidden></section><noscript><details><summary>Original setup evidence (JavaScript disabled)</summary><ul></ul></details></noscript></section><!-- decision-history:731ca0edc0c01337b75841f88d79bd1838b31ba05910928a6353dc43150ca0ea --></div></details><details class="board-section"><summary><h2>Decision History</h2></summary><div class="board-section-body"><section id="decision-history"><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/direct-conv-symmetric-gate.md" target="_blank" rel="noopener" title="Opens in a new tab">Gate the symmetric direct_conv kernel at work fraction 0.7, with ymin column ordering</a><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/interferometer-route-gate-cost-model.md" target="_blank" rel="noopener" title="Opens in a new tab">Route interferometer curvature by a calibrated two-constant cost model</a><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/adaptsplit-zero-weight-delaunay-only.md" target="_blank" rel="noopener" title="Opens in a new tab">Exclude zero-weight mappings from AdaptSplit pixel signals on Delaunay meshes only</a><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md" target="_blank" rel="noopener" title="Opens in a new tab">Keep Jacobi PDIP as the Mapper default and raw+polish as the MGE-only default</a></section></div></details><ul class="boards"><li class="muted" style="padding:.3rem 0">Boards:</li><li><a data-organ="organism" href="https://pyautolabs.github.io/PyAutoScientist/">Scientist</a></li><li><a data-organ="brain" href="https://pyautolabs.github.io/PyAutoBrain/">Brain</a></li><li><a data-organ="mind" href="https://pyautolabs.github.io/PyAutoMind/">Mind</a></li><li><a data-organ="cortex" href="https://pyautolabs.github.io/PyAutoCortex/">Cortex</a></li><li><a data-organ="memory" href="https://pyautolabs.github.io/PyAutoMemory/">Memory</a></li><li><a data-organ="eyes" href="https://pyautolabs.github.io/PyAutoEyes/">Eyes</a></li><li><a data-organ="ears" href="https://pyautolabs.github.io/PyAutoEars/">Ears</a></li><li><a data-organ="heart" href="https://pyautolabs.github.io/PyAutoHeart/">Heart</a></li><li><a data-organ="hands" href="https://pyautolabs.github.io/PyAutoHands/">Hands</a></li><li><a data-organ="insight" href="https://pyautolabs.github.io/PyAutoInsight/">Insight</a></li><li><a data-organ="dna" href="https://pyautolabs.github.io/PyAutoDNA/">DNA</a></li><li><a data-organ="nerves" href="https://pyautolabs.github.io/PyAutoNerves/">Nerves</a></li><li><a data-organ="broca" href="https://pyautolabs.github.io/PyAutoBroca/">Broca</a></li><li><a data-organ="gut" href="https://pyautolabs.github.io/PyAutoGut/">Gut</a></li></ul></main><script>const MAX_PROMPT_CHARS = 50000;
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diff --git a/dashboard.md b/dashboard.md
index 2e06da1..730aa31 100644
--- a/dashboard.md
+++ b/dashboard.md
@@ -1,6 +1,6 @@
 # PyAutoPulse — profiling dashboard
 
-<!-- pulse-campaigns:19faf009965e3df26e5113e7c9ff26b4dcb4a5df3d156bf43e8838997abca73d -->
+<!-- pulse-campaigns:4e7b5e1126147b4593a7e870235fd9a5d367256202aaeb9408c0254298791675 -->
 ## Profiling Check In
 
 <details><summary>Full check-in prompt</summary>
@@ -25,7 +25,7 @@ After taking action, report what changed, what the evidence supports and what re
 
 </details>
 
-Last check-in: 2026-10-17T11:09:26Z (review date, not measurement freshness).
+Last check-in: 2026-10-17T15:12:59Z (review date, not measurement freshness).
 
 ## Active campaigns
 
@@ -54,28 +54,15 @@ Reviewed 2026-10-17. No new committed campaign measurements. Phases 1–4 remain
 
 </details>
 
-<details><summary>Point-source image plane · A100 — needs-decision</summary>
+<details><summary>Point-source image plane · A100 — parked</summary>
 
-Reviewed 2026-10-17. No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.
+Reviewed 2026-10-17. Parked as research, human decision 2026-10-17 (chat) (&quot;just research to file&quot;). Reference measurement autolens_profiling#434 (issue #427, OPEN): job 412519 euclid-ral-gpu-2 exclusive A100, jax 0.10.2, fp64, 2026.10.17.1 private clone: single call 0.934 ms (pre-#768 0.910), vmap-256 0.0125 ms (0.0120), compile 6.54 s; forward gradient finite 16/16, equal to reverse to 1.6e-11; logL bit-exact; +2.7% not attributed (different node/date). A100 per-step breakdown rows unusable. Phase-2 inference fit measurement dropped; no baseline admitted.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_gpu_breakdown.md)
 
 ### Active tasks
 
-- [Point-source A100 speed-up campaign: profile and optimize with the shared breakdown](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_image_plane_gpu_breakdown.md) — needs-decision: No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.
-
-</details>
-
-<details><summary>Point-source source plane — parked</summary>
-
-Reviewed 2026-10-17. Core remains parked; autolens_inference#21 reorganized setup evidence but added no point-source gradient admission measurement. #408 merged median fields into 11 runtime cells; #412 merged 2026-10-13, completing headline/repeat support. No new runtime sweep; keep Blackjax parked. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.
-
-[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_source_plane.md)
-
-### Active tasks
-
-- [Runtime cells&#x27; A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/runtime_cell_single_jit_gpu_warmup.md) — active: #408 merged 2026-10-09: median headline fields now wired into 11 runtime cells, superseding the old imaging-not-wired note. #412 merged 2026-10-13 with remaining headline/repeat support. No new runtime sweep; fresh median evidence still missing.
-- [Point-source source-plane chi-squared speed-up campaign — remaining candidates (phases 1–2e shipped)](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_source_plane_chi_squared_speed.md) — blocked: Still blocked on a point-source gradient-sampler admission measurement. Inference #21 is setup organization, not that measurement. Keep parked.
+- [Point-source A100 speed-up campaign: profile and optimize with the shared breakdown](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_image_plane_gpu_breakdown.md) — parked: Parked, human decision 2026-10-17 (chat). Reference A100 measurement autolens_profiling#434 (OPEN, job 412519): 0.934 ms single call, 0.0125 ms at vmap-256, forward gradient fixed; phase-2 inference fit measurement dropped.
 
 </details>
 
@@ -92,19 +79,19 @@ Reviewed 2026-10-17. No campaign wiki/results change or new matching implementat
 
 </details>
 
-<details><summary>Interferometer likelihood and streaming — active</summary>
+<details><summary>Interferometer likelihood and streaming — parked</summary>
 
-Reviewed 2026-10-17. Aris large-mask (PyAutoPulse#62): library PRs PyAutoArray#630/#631/#632/#634 RELEASED in 2026.10.16.1 (2026-10-16; 2026.10.16.2 and 2026.10.17.1 followed with no further PyAutoArray change). Phase-2 route-gate record autolens_profiling#420 merged 2026-10-15; #422 (gate-60 wording historical) and #424 (lint re-pin after #634) merged 2026-10-16; project main fd29902. L1 Cholesky reuse (#634) measured on the NumPy path: solve+log-det 144.5→50.9 ms at 1,405 vertices, 1,268→373 ms at 2,947 (full call −22–24% / −51%). L2 direct_conv symmetry (PyAutoArray#635) PAUSED by the human 2026-10-16, uncommitted on worktree feature/direct-conv-symmetric (base e59d5fa9), no PR: synthetic Aris-512 serial RAL witness (job 411835) 1.81×/1.85×/1.90× at S 1,428/2,540/5,712, computed entries bit-identical; real Aris data on the laptop 1.32× (centroid order) / 1.46× (ymin order), work fraction 0.59/0.51; human decision 2026-10-16: rank by ascending ymin, default gate 0.5→0.7; v3 (ymin + 0.7) present in the worktree diff, its test run, the RAL real-data witness (≥1.3× curvature step), ship and write-up are owed. Streaming 1e8 CPU/A100 rows, the #332 memo after-measurement, curvature preload (fixed-mapper science decision) and FFT levers unchanged.
+Reviewed 2026-10-17. Evening 2026-10-17: human decision 2026-10-17 (chat): interferometer CPU work is exhaustive and closed; curvature preload parked (&quot;we will come back to it soon&quot;); GPU levers &quot;file wiki research but not make source code changes for now&quot;, so no library prompt for JAX FFT pruning. Parked at knowledge-complete. Open PRs (human&#x27;s to merge or close): L2 symmetric direct_conv PyAutoArray#636 (pending-release; real Aris RAL 1.49×/1.53×/1.58× at 1,459/2,577/5,746 vertices, logL to round-off; decision record direct-conv-symmetric-gate) + record autolens_profiling#425; A100 FFT levers #433 (pruned FFT 1.08×/1.18×/1.41× whole likelihood at alma/alma_high/jvla; pixel-scale coarsening fails the 0.5-nat bar); streaming 1e8 CPU/A100 + memo after-measurement #435. Route-constant refit (RAL FFT/full direct_conv 0.68 vs 0.89 predicted) deferred. Merge #425 after #636. Six open autolens_profiling PRs (#425, #431, #432, #433, #434, #435) all regenerate the shared catalogue/dashboard: each merge after the first needs a rebase + re-render; suggested order #425, #432, #431, #433, #434, #435. #425 after PyAutoArray#636.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09f/wiki/campaigns/interferometer_likelihood.md)
 
 ### Active tasks
 
-- [Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_fixed_mapper_curvature_preload.md) — ready: Phase 1 Mind draft (2026-10-04) still unstarted; run it through start_dev. Human science call first - no production SLaM stage holds the mapper fixed today.
-- [fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_nnls_memo_scattered_stream_guard.md) — active: Library half shipped: PyAutoArray#615 (closes #613) merged 2026-10-06, in 2026.10.7.1. Local solver-only witness (n=576): iid memo on/off 1.49x → 1.17x; walk 0.18x unchanged. Owed: re-run the autolens_profiling#332 ALMA Delaunay 2.17x after-measurement (needs compute authorization).
-- [Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_streaming_scaling.md) — active: Phase 1 CPU cells + results/streaming_scaling rows merged (autolens_profiling#375, 2026-10-04); wiki/campaigns/interferometer_streaming.md. Optional: 1e8 CPU row and A100 rows (compute authorization). Library candidate unfiled: chunk transformer.image_from in apply_sparse_operator (memory).
-- [Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_large_mask_aris.md) — active: Released: #630/#631/#632/#634 in 2026.10.16.1 (2026-10-16). Phase-2 record #420 merged 2026-10-15 (table re-pinned at fd29902); #422/#424 merged 2026-10-16. L2 (PyAutoArray#635) paused 2026-10-16 with v3 (ymin order, gate 0.7) uncommitted in worktree feature/direct-conv-symmetric; synthetic witness PASS 1.81–1.90× (RAL 411835), real-data laptop 1.46× at ymin order; owed on resume: verify v3 + full suite, RAL real-data witness ≥1.3× (data and private library clones staged under scratch/aris_l2_20261016), Heart check, ship_library PR, write-up (results note, Knowledge section, decision record direct-conv-symmetric-gate). κ=1.32 means the #632 cost-model constant for direct_conv is effectively refit when L2 ships.
-- [Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space pixel scale on the A100](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_w_tilde_fft_size_levers.md) — ready: Unstarted (no PR/issue/branch/results, 2026-10-04). Select one bounded step when prioritised.
+- [Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_fixed_mapper_curvature_preload.md) — parked: Parked, human decision 2026-10-17 (chat): interferometer CPU work closed; &quot;we will come back to it soon&quot;. Phase-1 Mind draft unstarted.
+- [fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_nnls_memo_scattered_stream_guard.md) — parked: After-measurement at PR-open autolens_profiling#435 (issue #426, OPEN; job 412526, ral compute-10-2, load ~10, 1 thread, 6 rounds, paired bootstrap 90% CI): Delaunay iid #332 flags 1.918 [1.887,1.925] (2.17 in #332); 15 timed solves 1.018 [1.013,1.023]; Delaunay walk 0.46; rect iid 0.887/0.987; rect walk 0.15; FoM identical. Not evidence #613 is fixed. Research owed: Nautilus-replay witness, varied-seed rounds. Parked, human decision 2026-10-17 (chat).
+- [Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_streaming_scaling.md) — parked: Phase 2 at PR-open autolens_profiling#435 (issue #368 reopened, OPEN): from_stream chunk 65536 fp64, 1e8 vis RAL CPU 707.5 s / 1.78 GB RSS, A100 157.3 s / 1.48 GB device; compile ~3 s constant. In-memory fails at 2.5e5 (RAL 10 GB cap) and 4e6 (A100); uncapped CPU ~7.5 kB/vis. Optional 2e8 rows are research; library candidate (chunk transformer.image_from) unfiled per human decision 2026-10-17 (chat). Parked.
+- [Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_large_mask_aris.md) — active: L2 shipped to PR: PyAutoArray#636 (head 62a1e3ac, pending-release) + autolens_profiling#425 (results note), both OPEN, CI green. RAL real-data witness 1.49×/1.53×/1.58× at 1,459/2,577/5,746 vertices (pred 1.48×/1.50×/1.53×), logL to round-off; peak RSS 43 GB without NUFFT chunking. Decision record direct-conv-symmetric-gate added. Route-constant refit (0.68 measured vs 0.89 predicted) deferred: CPU lane closed, human decision 2026-10-17 (chat). Remaining: human merge of #636 then #425, and release.
+- [Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space pixel scale on the A100](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_w_tilde_fft_size_levers.md) — parked: A100 research filed at PR-open autolens_profiling#433 (issue #429, OPEN; job 412515): pruned padded FFT 1.08×/1.18×/1.41× whole likelihood at alma/alma_high/jvla, F exact to 1.7e-15; pixel-scale coarsening shifts log evidence by +1,310 to +2,757 nats (fails). No library prompt, human decision 2026-10-17 (chat). Parked.
 
 </details>
 
@@ -121,21 +108,33 @@ Reviewed 2026-10-17. Retain the 2026-10-08 human decision: Jacobi for Mapper, ra
 
 <details><summary>Production imaging and likelihood breakdown — active</summary>
 
-Reviewed 2026-10-17. No new production-over-sampling or post-certified measurements. #408 now adds median headline fields to runtime cells; #412 merged 2026-10-13 and extends remaining headline surfaces. Production presets, memo-blind harness, cumulative MGE steps and quick-update costs remain separate pending work. Scope production-preset phase 1 (CPU smoke only); A100 measurement separately authorized. New related library evidence: PyAutoArray#624/#625 (numba Sibson kernel; DelaunayNN within 2× Delaunay on Aris 1,405) released 2026.10.16.1; follow-up PyAutoArray#629 (imaging DelaunayNN still ~2.7× Delaunay at sub_size 4: unique_mappings 0.30 vs 0.10 s, adaptive_pixel_signals 0.16 vs 0.01 s; laptop, threads=1, load 3–5, not a controlled measurement) is open and its Mind draft expects a Pulse campaign delaunay-nn-cpu that does not yet exist — human decision whether to open one.
+Reviewed 2026-10-17. Production over-sampling phase 1 at PR-open: autolens_profiling#431 (issue #430, OPEN, CPU smoke 8/8): --over-sampling {flat1,production} on the 4 JAX Delaunay/DelaunayNN cells, euclid/hst presets. Findings: HST flat-1 adapt_split pin fails on unmodified main (29155.000 vs 29140.296); numba cells&#x27; production threshold never reaches sub 4 for HST; rectangular JAX cells uncovered. Phase 2 (A100 production re-measure) needs-decision (human decision 2026-10-17 (chat): undecided). DelaunayNN imaging moved to the parked delaunay-nn-cpu campaign. Post-certified, quick-update plotting and other tasks unchanged.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/post_certified_breakdown.md)
 
 ### Active tasks
 
-- [Profile imaging pixelizations at production over-sampling](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/imaging_production_over_sampling.md) — ready: Phase 1 (workspace-only): --instrument euclid\|hst presets for the JAX Delaunay runtime/breakdown cells, keeping flat-1 selectable; CPU smoke only. A100 re-measure is a separate compute-authorized phase.
+- [Profile imaging pixelizations at production over-sampling](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/imaging_production_over_sampling.md) — needs-decision: Phase 1 at PR-open: autolens_profiling#431 (issue #430, OPEN, CPU smoke 8/8). Phase 2 A100 production re-measure needs-decision (human decision 2026-10-17 (chat): undecided); requires #431 merged and compute authorization. Findings: HST flat-1 adapt_split pin fails on main; numba HST threshold never reaches sub 4; rectangular JAX cells uncovered.
 - [Assess remaining likelihood bottlenecks after certified solver integration on CPU and GPUs](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/post_certified_solver_likelihood_breakdown.md) — needs-slicing: Blocked-by is stale: resolved 2026-09-27 (PyAutoArray#567 in 2026.9.26.1). Unissued; scope one bounded phase from the full task contract.
 - [Quick-update plotting cost — minutes per update, and it is not JAX compile](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/quick_update_plotting_cost.md) — ready: Unchanged; targets PyAutoLens/PyAutoGalaxy (PyAutoLens#680 closed 2026-07-31, no follow-up issue). Select one bounded step when prioritised.
 
 </details>
 
+<details><summary>DelaunayNN (Sibson) mesh at Delaunay cost on the numba/NumPy CPU path — parked</summary>
+
+Reviewed 2026-10-17. Created parked, human decision 2026-10-17 (chat): &quot;file in research to come back to in the future&quot;. PyAutoArray#637 (issue #629, OPEN, pending-release, CI 3 legs green): RAL compute-10-4 exclusive 1 thread A-B-B-A ×2, 1,030 vertices / 15,380 px: NN sub4 0.569→0.392 s (2.20× Delaunay, was 3.00×), sub2 0.235→0.189 s (1.75×); logL bit-identical; fixes the size-0 Sibson NaN case. ~1.5× target not met; remaining cost is Sibson (~0.16 s) + wider curvature rows. Target and two hazards recorded as future work, not filed.
+
+[Campaign evidence](https://github.com/PyAutoLabs/PyAutoArray/pull/637)
+
+### Active tasks
+
+- [DelaunayNN (Sibson) imaging likelihood at close to Delaunay cost on the numba/NumPy CPU path](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/delaunay_nn_cpu_imaging.md) — parked: Parked, human decision 2026-10-17 (chat). PyAutoArray#637 (OPEN, pending-release) at PR-open: sub4 2.20× Delaunay (was 3.00×), sub2 1.75×; logL bit-identical. Future (unfiled): ~1.5× target; hazards sub_slim_indexes_for_pix_index padding and Sibson rows with mid-row gaps.
+
+</details>
+
 <details><summary>Measurement reliability and profiling tools — active</summary>
 
-Reviewed 2026-10-17. Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415 closed. No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo interpretation, fresh runtime repeats and other tools remain pending. Check-in 2026-10-17: autolens_profiling#418 (pilot qualification design) open since 2026-10-13 with CI green and no review; #417 open; no new measurements.
+Reviewed 2026-10-17. Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415 closed. No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo interpretation, fresh runtime repeats and other tools remain pending. #418 and #417 open. Evening 2026-10-17 merge-order note: Six open autolens_profiling PRs (#425, #431, #432, #433, #434, #435) all regenerate the shared catalogue/dashboard: each merge after the first needs a rebase + re-render; suggested order #425, #432, #431, #433, #434, #435. #425 after PyAutoArray#636.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/pull/416)
 
@@ -330,9 +329,10 @@ Project `autofit_profiling`, scope `pyautofit-profiling`, read from [PyAutoLabs/
 </details>
 
 
-<!-- decision-history:20f0256fc1d56eda82a910f81ddb71d393e76eeaf26e486d9c836b00282b99f9 -->
+<!-- decision-history:731ca0edc0c01337b75841f88d79bd1838b31ba05910928a6353dc43150ca0ea -->
 ## Decision History
 
+- <a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/direct-conv-symmetric-gate.md">Gate the symmetric direct_conv kernel at work fraction 0.7, with ymin column ordering</a>
 - <a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/interferometer-route-gate-cost-model.md">Route interferometer curvature by a calibrated two-constant cost model</a>
 - <a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/adaptsplit-zero-weight-delaunay-only.md">Exclude zero-weight mappings from AdaptSplit pixel signals on Delaunay meshes only</a>
 - <a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md">Keep Jacobi PDIP as the Mapper default and raw+polish as the MGE-only default</a>
diff --git a/decisions.yaml b/decisions.yaml
index e34b2b3..155a7f7 100644
--- a/decisions.yaml
+++ b/decisions.yaml
@@ -12,3 +12,7 @@ decisions:
   title: Route interferometer curvature by a calibrated two-constant cost model
   date: '2026-10-15'
   url: https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/interferometer-route-gate-cost-model.md
+- id: direct-conv-symmetric-gate
+  title: Gate the symmetric direct_conv kernel at work fraction 0.7, with ymin column ordering
+  date: '2026-10-16'
+  url: https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/direct-conv-symmetric-gate.md
diff --git a/decisions/direct-conv-symmetric-gate.md b/decisions/direct-conv-symmetric-gate.md
new file mode 100644
index 0000000..15004d2
--- /dev/null
+++ b/decisions/direct-conv-symmetric-gate.md
@@ -0,0 +1,85 @@
+# Gate the symmetric direct_conv kernel at work fraction 0.7, with ymin column ordering
+
+Date: 2026-10-16
+Decision maker: James Nightingale
+Decision source: The human paused session 13efb443 on 2026-10-16 with the decision "rank columns by ascending ymin and raise the default gate from 0.5 to 0.7", recorded in the checkpoint comment on [PyAutoArray#635](https://github.com/PyAutoLabs/PyAutoArray/issues/635) ("Human decision (2026-10-16)").
+
+## Campaign
+
+This decision belongs to the interferometer campaign, task `interferometer_large_mask_aris`
+([PyAutoPulse#62](https://github.com/PyAutoLabs/PyAutoPulse/issues/62)), lever L2.
+
+The numba `direct_conv` curvature kernel convolves every source column over the whole extent
+rectangle. A symmetric, row-restricted variant computes only one triangle of F, restricted to the
+extent rows later-ranked columns span and each row's mask x-range, then mirrors it. Its computed
+entries are bit-identical.
+
+Two questions remained: when the variant should run, and how the columns should be ordered. The
+ordering decides how much work it skips.
+
+## Evidence
+
+All timings are serial numba, compile excluded. Library: PyAutoArray `e59d5fa9` plus the #635 patch
+(PR [#636](https://github.com/PyAutoLabs/PyAutoArray/pull/636), head `62a1e3ac`). Record:
+autolens_profiling [#425](https://github.com/PyAutoLabs/autolens_profiling/pull/425),
+`results/notes/interferometer_direct_conv_symmetric_2026_10.md`.
+
+- **Cost model.** The symmetric kernel takes κf of the full kernel's time, with κ = 1.32. κ was
+  fitted on synthetic Aris-512 geometry on RAL (job 411835, `euclid-ral-compute-1`, load < 1;
+  measured/predicted 0.99–1.01).
+- **Real Aris data, before the decision** (laptop, shared, 1,459 vertices):
+  - With centroid ordering, f = 0.587, measured 1.32× (predicted 1.29×).
+  - With `ymin` ordering, f = 0.511, measured 1.46× (predicted 1.48×).
+  - The original 0.5 gate kept Aris on the full kernel either way.
+  - Real columns are tall because of multiple imaging: the median spans 90 of 341 rows, against 15
+    synthetic.
+- **After the decision, on RAL** (`euclid-ral-compute-1`, `ral`, exclusive, load ≤ 1, jobs
+  412490–412493), `ymin` order, default gate 0.7:
+  - Real Aris: 1.49× / 1.53× / 1.58× at 1,459 / 2,577 / 5,746 vertices. log L matches to round-off;
+    a log-det triangle control shows the 5,746-vertex 0.0071-nat difference is round-off.
+  - Synthetic: 1.83× / 1.86× / 1.90×.
+- **Uncertainty.** κ has been measured only at f 0.40–0.51, so the predicted 1.08× at f = 0.7 is
+  an extrapolation. There is one real dataset. At 1,459 vertices the FFT route measures 15.32 s on
+  the same node, so the gain over the previous default route there is about 1.02×.
+
+## Options considered
+
+- **Keep the gate at 0.5 with centroid ordering.** This is safe but excludes real multiply imaged
+  data (f ≈ 0.51–0.59), so it gives no gain where the campaign needs one.
+- **Gate at 0.5 with `ymin` ordering.** Real Aris sits at f ≈ 0.50–0.51, right on the boundary, so
+  it would flip between kernels across meshes.
+- **Gate at 0.7 with `ymin` ordering (chosen).** It sits below the model's break-even of f = 0.76,
+  keeps a predicted gain of at least 1.08×, and covers multiply imaged data with margin.
+- **No gate (always symmetric).** Above f ≈ 0.76 the model predicts a slowdown.
+
+## Decision
+
+- Rank source columns by ascending `ymin` (stable, empty columns last).
+- Set the packaged default of `interferometer_direct_conv_symmetric_max_work_fraction` to **0.7**.
+  `0` disables the kernel, and `null` means the default.
+- The scope is the serial numba `direct_conv` route only. The parallel kernel and the JAX routes
+  are unchanged.
+
+## Implications
+
+- With serial numba, NumPy interferometer fits with f ≤ 0.7 use the symmetric kernel by default
+  once PyAutoArray#636 is merged and released. Results are identical to round-off, so this is a
+  speed change only.
+- The #632 route model now scales `t_dc` by `min(1, κf)`. On the Aris 512 mask at 1,459 vertices
+  this flips the route from FFT to `direct_conv`. That is correct on RAL, but only by about 1.02×.
+- **Shipped:** PR-open, pending release (PyAutoArray#636; record autolens_profiling#425). Merge is
+  the human's.
+- **Follow-up (deferred):** on RAL, FFT/full `direct_conv` measures 0.68 against 0.89 predicted by the
+  laptop-calibrated #632 constants. A cluster-node refit is deferred: the human closed the
+  interferometer CPU lane on 2026-10-17 (chat).
+
+## Revisit conditions
+
+- A dataset where the kernel runs at f between 0.6 and 0.7 and measures slower than the full
+  kernel. That would mean κ is not constant up to the gate.
+- A refit of the route constants that changes which route is chosen near the gate.
+- A parallel symmetric kernel, which would need its own gate.
+
+## History
+
+This is the first record for this gate.
diff --git a/tasks/delaunay_nn_cpu_imaging.md b/tasks/delaunay_nn_cpu_imaging.md
new file mode 100644
index 0000000..5d35ec5
--- /dev/null
+++ b/tasks/delaunay_nn_cpu_imaging.md
@@ -0,0 +1,60 @@
+# DelaunayNN (Sibson) imaging likelihood at close to Delaunay cost on the numba/NumPy CPU path
+
+- Campaign: `delaunay-nn-cpu` (parked, human decision 2026-10-17, chat: "file in research to come back to in the future")
+- Issue: [PyAutoArray#629](https://github.com/PyAutoLabs/PyAutoArray/issues/629)
+- PR: [PyAutoArray#637](https://github.com/PyAutoLabs/PyAutoArray/pull/637) (OPEN, CI 3 legs green, pending-release, unmerged; merge is the human's)
+- Prior work: [PyAutoArray#624](https://github.com/PyAutoLabs/PyAutoArray/pull/624)/[#625](https://github.com/PyAutoLabs/PyAutoArray/pull/625) (issue #623, numba Sibson kernel; released 2026.10.16.1): Aris SMA 1,405 vertices DelaunayNN 18.1 s → 0.57 s (Delaunay 0.41 s), threads=1, logL identical.
+
+## Question
+
+Can natural-neighbour (Sibson) interpolation run in the CPU imaging likelihood at close to Delaunay
+cost? The draft target was ~1.5× Delaunay at sub-size 4.
+
+## Measurement (PyAutoArray#637)
+
+RAL `euclid-ral-compute-10-4`, `--partition=ral --exclusive`, 1 thread, `use_jax=False`; A-B-B-A × 2
+blocks, 10 timed calls per process after 2 warm-ups. A = PyAutoArray `e59d5fa9` (2026.10.17.1),
+B = the #637 head; other libraries 2026.10.17.1. HST imaging, 1,030 vertices, 15,380 pixels.
+Heart read STALE ("release validation incomplete: no rehearsal for current source"); no override used.
+
+| Case | A | B | Ratio to Delaunay (B) | adaptive_pixel_signals A → B | unique_mappings A → B |
+|---|---|---|---|---|---|
+| DelaunayNN sub 4 | 0.569 s | 0.392 s | 2.20× (was 3.00×) | 158.0 → 6.5 ms | 22.5 → 17.0 ms |
+| Delaunay sub 4 | 0.189 s | 0.178 s | 1 | 10.0 → 1.2 ms | 4.4 → 3.5 ms |
+| DelaunayNN sub 2 | 0.235 s | 0.189 s | 1.75× (was 2.10×) | 37.3 → 1.1 ms | 4.3 → 2.7 ms |
+| Delaunay sub 2 | 0.112 s | 0.108 s | 1 | 2.5 → 0.4 ms | 1.9 → 1.1 ms |
+
+- logL bit-identical in all four cases.
+- Sibson interpolation itself is ~0.16 s at sub 4 (~41 % of the DelaunayNN call).
+- **Target ~1.5× not met** (2.20× at sub 4). The remaining cost is Sibson work and wider curvature
+  rows (up to 43 unique pixels per data pixel against 27 for Delaunay), not padding overhead.
+- #637 also fixes the size-0 Sibson NaN edge case: failed rows now keep `size >= 1` with a NaN weight
+  on the nearest seed vertex (reference, numba and JAX paths), so a failed row no longer yields a
+  finite, wrong evidence.
+
+## Knowledge
+
+- **Padded rows.** DelaunayNN tables are 32 columns wide (`SIBSON_MAX_NEIGHBORS`), but HST-scale
+  imaging uses at most 13 slots per row (mean 7.2). `adaptive_pixel_signals_from` used `np.add.at`
+  over all 32 slots of 246k sub-pixels; visiting only live entries cuts it from 158 to 6.5 ms with an
+  unchanged summation order.
+- **Misattribution trap.** cProfile charged ~0.30 s to `mapper.unique_mappings`; most of it was the
+  Sibson interpolation running lazily on first access to the mapper's tables. Time kernels directly
+  before acting on cumulative profiles of cached properties.
+- **Imaging vs interferometer.** On the Aris interferometer the visibility-side curvature dwarfs the
+  mapping tables, so DelaunayNN sits near ~1.4× Delaunay. Imaging at sub 4 has 16× as many query
+  points and cheap curvature, so per-sub-pixel table work and Sibson set the ratio.
+
+## Future work (recorded, not filed)
+
+Per the human decision of 2026-10-17 these stay as research:
+
+- The ~1.5× target: Sibson kernel cost at threads=1 and curvature over wider unique rows.
+- Hazard: `sub_slim_indexes_for_pix_index` padding.
+- Hazard: Sibson rows with mid-row gaps.
+
+## Check-in 2026-10-17 (evening)
+
+- Campaign created parked (human decision 2026-10-17, chat). #637 recorded at PR-open; it is file-
+  disjoint from PyAutoArray#636 and independent of it.
+- [Full dated review](../checkins/2026-10-17.md).
diff --git a/tasks/imaging_production_over_sampling.md b/tasks/imaging_production_over_sampling.md
index d868910..f118b67 100644
--- a/tasks/imaging_production_over_sampling.md
+++ b/tasks/imaging_production_over_sampling.md
@@ -28,6 +28,16 @@ GitHub sources and limits. Current RAL queue was empty; the transfer preview tim
 out, so historical jobs and unpulled results remain unverified. Review time does
 not refresh measurements or authorize compute. Historical task text is retained.
 
+
+## Check-in 2026-10-17 (evening)
+
+- **Phase 1 at PR-open:** autolens_profiling PR [#431](https://github.com/PyAutoLabs/autolens_profiling/pull/431) (issue #430; OPEN, CI green, unmerged). Workspace-only; CPU functional smoke 8/8 pass; no rows recorded, no compute. Heart read STALE ("release validation incomplete: no rehearsal for current source"); no override used.
+  - Adds `--over-sampling {flat1,production}` (default flat1) to the four JAX Delaunay/DelaunayNN imaging cells (`scripts/imaging/delaunay{,_nn}/likelihood_{runtime,breakdown}.py`).
+  - Presets: euclid lp [4,4,2] at [0.1,0.3], pix sub 4 where source S/N > 3 else 2 (euclid_strong_lens_modeling_pipeline@9b1bc4c); hst lp [4,2,2] at [0.1,0.3], same rule (subhalo_validation@ab4bf32). HST 2,022 / 15,361 px at sub 4; Euclid 1,057 / 3,841.
+- **Findings:** the HST flat-1 `adapt_split` pin fails on unmodified main (29155.000 vs 29140.296, 5e-4); the numba cells' production threshold (capped adapt image) never reaches sub 4 for HST; rectangular JAX cells are not covered.
+- **Phase 2** (A100 production re-measure, `--over-sampling production --instrument euclid|hst`): **needs-decision** — human undecided 2026-10-17 (chat). Requires #431 merged and separate compute authorization.
+- [Full dated review](../checkins/2026-10-17.md). Review time does not refresh measurements or authorize compute; merge is the human's.
+
 ---
 
 # Profile imaging pixelizations at production over-sampling
diff --git a/tasks/interferometer_fixed_mapper_curvature_preload.md b/tasks/interferometer_fixed_mapper_curvature_preload.md
index 15375ef..de5c12d 100644
--- a/tasks/interferometer_fixed_mapper_curvature_preload.md
+++ b/tasks/interferometer_fixed_mapper_curvature_preload.md
@@ -40,6 +40,12 @@ GitHub sources and limits. Current RAL queue was empty; the transfer preview tim
 out, so historical jobs and unpulled results remain unverified. Review time does
 not refresh measurements or authorize compute. Historical task text is retained.
 
+
+## Check-in 2026-10-17 (evening)
+
+- **Human decision 2026-10-17 (chat):** interferometer CPU work is exhaustive and closed; curvature preload is parked — "we will come back to it soon". The phase-1 Mind draft is not started. Status → parked.
+- [Full dated review](../checkins/2026-10-17.md). Review time does not refresh measurements or authorize compute; merge is the human's.
+
 ---
 
 # Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`
diff --git a/tasks/interferometer_large_mask_aris.md b/tasks/interferometer_large_mask_aris.md
index 5b366e3..87a8c6f 100644
--- a/tasks/interferometer_large_mask_aris.md
+++ b/tasks/interferometer_large_mask_aris.md
@@ -252,7 +252,7 @@ these cells.
 | Rank | Lever | Size | State |
 |---|---|---|---|
 | L1 | One `cho_factor` of F+H for both the reconstruction solve and the log det (NumPy path, `use_positive_only_solver=False`). The positive-only path already reuses its factor. | 65 ms vs 113+81 ms at 1,405; 612 ms vs 948+654 ms at 2,947 → **−20% / −40–45% per call**. Solution change 7e-14 / 2e-11 relative, log det identical, non-PD still raises. | Filed and **in development**: Mind task `reuse-cholesky-factor`, PyAutoArray [#633](https://github.com/PyAutoLabs/PyAutoArray/issues/633). |
-| L2 | `direct_conv` symmetry: convolve only the extent rows that later-ranked (y-sorted) columns span, clipped to the mask x-span; gather the upper triangle; mirror. Exact to 2e-16. | Work 0.60× at 125, 0.42× at 512. Measured: 125 1.10× / 0.82× (overhead cancels, so not worth it there); 512 synthetic (341², 91k px, 1,428 vertices) 2.5× uncontended (63 → 25 s), but 1.03–1.10× under load. **2–2.5× at 512, unconfirmed.** | Filed (Mind draft), unstarted. If it holds, direct_conv may beat FFT at 1,459 too, and the #632 constants must be refitted. |
+| L2 | `direct_conv` symmetry: rank the source columns by top extent row (`ymin`). Each column convolves only the extent rows that later-ranked columns span, clipped to each row's mask x-range. Gather the ranked upper triangle, then mirror. Computed entries are bit-identical. Serial numba only. | Real Aris 512, RAL quiet core: **1.49× / 1.53× / 1.58×** at 1,459 / 2,577 / 5,746 vertices (f 0.511 / 0.504 / 0.495). Synthetic Aris-512: 1.83–1.90× (f ≈ 0.41). Gain over the *previous default route* at 1,459 is only ~1.02×, because FFT measures 15.32 s on the same node. | **PR-open** [PyAutoArray#636](https://github.com/PyAutoLabs/PyAutoArray/pull/636) (issue #635; pending release; merge is the human's). Default gate 0.7. Record: autolens_profiling [#425](https://github.com/PyAutoLabs/autolens_profiling/pull/425) `results/notes/interferometer_direct_conv_symmetric_2026_10.md` (PR-open). Decision: [`direct-conv-symmetric-gate`](../decisions/direct-conv-symmetric-gate.md). |
 | L3 | Fixed-mapper curvature preload: F, D, the mapper and the split mappings do not change while the lens is fixed, which is exactly a source_pix[1]-style phase with only AdaptSplit parameters free. | With L1: ~0.1 s at 1,405 (~6×), ~0.8 s at 2,947, ~1 s at 512 / 2,577 against 34–44 s (~30×). Applies to **fixed-lens phases only**; a moving lens (mass[1]) has nothing to cache. | Existing Pulse task `interferometer_fixed_mapper_curvature_preload` (ready; needs the fixed-mapper science decision). |
 
 Not worth it (measured on the 125×125 setup unless stated):
@@ -280,6 +280,55 @@ Not worth it (measured on the 125×125 setup unless stated):
   now routes it automatically.
 - **Leave `interferometer_numba_nnz_per_source_max` unset** (the cost model) unless running an A/B.
   Scripts that set it explicitly keep the old hard-gate meaning.
+- **Leave `interferometer_direct_conv_symmetric_max_work_fraction` unset** (default 0.7) once
+  PyAutoArray#636 is released. With serial numba it speeds up F by about 1.5× on multiply imaged
+  sources at no accuracy cost. Set it to `0` only for an A/B test against the full kernel.
+
+### (h) The symmetric `direct_conv` kernel and the tall-column insight (PyAutoArray#635 / #636, 2026-10-17)
+
+- **Rule.**
+  - The serial numba `direct_conv` route uses `curvature_direct_conv_symmetric` when the geometry
+    work fraction f ≤ 0.7. f is computed from the mapper triplets and the mask extent in about 1 ms,
+    before any curvature work.
+  - Its time is κf of the full kernel, with κ = 1.32. κ was fitted on the synthetic geometry
+    (log-rms 0.008) and checked out of sample on real data: predicted 1.48–1.53×, measured
+    1.49–1.58×. So the kernel wins while f < 0.76.
+  - The #632 route model scales `t_dc` by `min(1, κf)`.
+- **Tall columns from multiple imaging.**
+  - On real Aris data, each source vertex is fed by two to four lensed images, so its image-plane
+    column spans a median of **90 of 341** extent rows (p95 158), against 15 on the synthetic
+    3-NN geometry.
+  - The tall columns are not the mask-edge vertices: 0 of the 30 tallest are edge vertices.
+  - Tall columns overlap in rows, so less work can be skipped. f is about 0.50 on real data against
+    about 0.41 synthetic, which means about 1.5× rather than 1.9×.
+  - Ranking columns by **ascending `ymin`** instead of centroid lowers real-data f from 0.587 to
+    0.511. On compact columns the ordering barely matters (0.416 → 0.414).
+- **Witness.**
+  - **Setup:** RAL `euclid-ral-compute-1`, `ral`, `--exclusive`, 1 thread, load ≤ 1.0, compile
+    excluded, min of 2. Software: PyAutoArray `e59d5fa9` + v3 (PR head `62a1e3ac`), Nerves `7f3055b`,
+    Fit `9b394e73`, Galaxy `9f9ac947`, Lens `4723668a`; numba 0.65.1, numpy 2.2.6.
+  - **Curvature step:** 22.43 → 15.08 s (1,459 vertices), 22.83 → 14.95 s (2,577), 24.22 → 15.35 s
+    (5,746).
+  - **log L:** bit-identical at 1,459, relative 3e-16 at 2,577 and 2e-12 at 5,746.
+  - **5,746 log det:** Δ 0.0071 nats, which is round-off. Control job 412493: the full kernel's own
+    F, factorised from its lower versus its upper triangle, moves the log det by 0.0077 nats at
+    cond(F+H) = 7.2e13.
+  - **Memory:** peak RSS 43 GB without NUFFT chunking.
+  - **Jobs:** 412490 (step 8 + synthetic), 412491 (FFT arm), 412492 (steps 6 and 4), 412493
+    (control).
+- **Route-constant calibration gap (deferred).**
+  - At 1,459 vertices on RAL, FFT/full `direct_conv` measures **0.68**. The laptop-calibrated #632
+    constants (`c_fft` 7.62e-10, `c_dc` 1.77e-09) predict 0.89.
+  - The route the factory now picks at 1,459 (symmetric `direct_conv`, 15.08 s) is right, but only
+    by about 1.02× over FFT (15.32 s). No measured route is wrong; the margin is thin on RAL.
+  - A cluster-node refit of `c_fft / c_dc` is deferred: the human closed the interferometer CPU lane
+    (human decision 2026-10-17, chat).
+- **Not covered.**
+  - The parallel numba kernel (unchanged).
+  - The 125×125 mask, not re-timed with v3. The earlier research found the overhead cancels the
+    gain there.
+  - Other lenses: one real dataset only.
+  - κ near the 0.7 gate: the predicted 1.08× there is an extrapolation.
 
 ### Check-in 2026-10-17
 
@@ -290,3 +339,11 @@ Not worth it (measured on the 125×125 setup unless stated):
 - **Human decision 2026-10-16 (recorded on #635, not yet a Pulse decision record):** rank columns by ascending ymin; raise the default `interferometer_direct_conv_symmetric_max_work_fraction` from 0.5 to 0.7 (kernel predicted to win while κf < 1, f < 0.76). v3 implementing this is in the worktree diff (gate 0.7, ymin ordering); its test run is unconfirmed.
 - **Owed on resume (from the #635 checkpoint):** verify v3 and the full suites; RAL real-data witness on `ral`, serial (`l2_realdata.py --n-pixels 512 --mesh-step 8 --repeats 2`), gate ≥ 1.3× on the curvature step with matching logL; re-confirm the synthetic witness with ymin order; Heart check; `/ship_library` PR; write-up (autolens_profiling results note with the decision rule, this Knowledge section, decision record `direct-conv-symmetric-gate`). When L2 ships, the #632 `c_dc` constant is effectively scaled by min(1, κf) and the Aris-512 1,459-vertex cell is predicted to flip from FFT to direct_conv.
 - **RAL staging verified 2026-10-17.** `/mnt/ral/jnightin/scratch/aris_l2_20261016/` holds `aris_data/`, `PyAutoArray` and `PyAutoArray_l2` (the v3 patch still to be applied), PyAutoNerves/PyAutoFit/PyAutoGalaxy/PyAutoLens mains, `numba_cache/`, the witness scripts, patches and the 411832–411835 job outputs.
+
+## Check-in 2026-10-17 (evening)
+
+- **L2 shipped to PR.** [PyAutoArray#636](https://github.com/PyAutoLabs/PyAutoArray/pull/636) (issue #635, head `62a1e3ac`, pending-release) and autolens_profiling [#425](https://github.com/PyAutoLabs/autolens_profiling/pull/425) (results note `results/notes/interferometer_direct_conv_symmetric_2026_10.md`): both OPEN, CI green, unmerged. Heart read STALE ("release validation incomplete: no rehearsal for current source"); no override used. Merge #425 after #636.
+- **RAL real-data witness met** (≥ 1.3× on the curvature step): 1.49× / 1.53× / 1.58× at 1,459 / 2,577 / 5,746 vertices, predicted 1.48× / 1.50× / 1.53×; see Knowledge (h). Synthetic ymin order 1.83× / 1.86× / 1.90×.
+- **Decision record** [`direct-conv-symmetric-gate`](../decisions/direct-conv-symmetric-gate.md) added for the human decision of 2026-10-16 on PyAutoArray#635 (ymin ordering, gate 0.5 → 0.7).
+- **Human decision 2026-10-17 (chat):** interferometer CPU work is exhaustive and closed. The route-constant refit (RAL FFT/full `direct_conv` 0.68 measured vs 0.89 predicted) is deferred, not filed. Curvature preload (L3) parked: "we will come back to it soon".
+- **State:** all L-levers are shipped or parked. This task stays active only until #636 and #425 are merged and released; merge is the human's.
diff --git a/tasks/interferometer_nnls_memo_scattered_stream_guard.md b/tasks/interferometer_nnls_memo_scattered_stream_guard.md
index 06f0395..3deed3f 100644
--- a/tasks/interferometer_nnls_memo_scattered_stream_guard.md
+++ b/tasks/interferometer_nnls_memo_scattered_stream_guard.md
@@ -32,6 +32,18 @@ GitHub sources and limits. Current RAL queue was empty; the transfer preview tim
 out, so historical jobs and unpulled results remain unverified. Review time does
 not refresh measurements or authorize compute. Historical task text is retained.
 
+
+## Check-in 2026-10-17 (evening)
+
+- **After-measurement on 2026.10.17.1:** autolens_profiling PR [#435](https://github.com/PyAutoLabs/autolens_profiling/pull/435) (issue #426; OPEN, CI green, unmerged). Heart read STALE ("release validation incomplete: no rehearsal for current source"); no override used. RAL job 412526, `ral` compute-10-2 (load ~10), 1 thread, 6 rounds, median memo on/off with paired-bootstrap 90 % CI:
+  - Delaunay iid at the #332 flags: 1.918 [1.887, 1.925] (2.17 in #332). 15 timed solves: 1.018 [1.013, 1.023].
+  - Delaunay walk 0.46; rect iid 0.887 / 0.987; rect walk 0.15. Figure of merit identical.
+  - Limits: the rounds re-time one fixed iid stream, so the CI covers timing noise, not stream-to-stream variation; host class differs from #332. Reported as an after-measurement, not as evidence that #613 is fixed.
+  - Harness note: clear with `nnls_memo.memo_clear()` (memo plus back-off state) on PyAutoArray ≥ 2026.10.7.1.
+- **Owed, recorded as research (not tasks):** a Nautilus evaluation-sequence replay witness; varied-seed rounds.
+- **Human decision 2026-10-17 (chat):** interferometer CPU work is exhaustive and closed. Status → parked.
+- [Full dated review](../checkins/2026-10-17.md). Review time does not refresh measurements or authorize compute; merge is the human's.
+
 ---
 
 # fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)
diff --git a/tasks/interferometer_streaming_scaling.md b/tasks/interferometer_streaming_scaling.md
index 4212a53..3d74ed9 100644
--- a/tasks/interferometer_streaming_scaling.md
+++ b/tasks/interferometer_streaming_scaling.md
@@ -42,6 +42,18 @@ GitHub sources and limits. Current RAL queue was empty; the transfer preview tim
 out, so historical jobs and unpulled results remain unverified. Review time does
 not refresh measurements or authorize compute. Historical task text is retained.
 
+
+## Check-in 2026-10-17 (evening)
+
+- **Phase 2 measured:** autolens_profiling PR [#435](https://github.com/PyAutoLabs/autolens_profiling/pull/435) (issue #368 reopened; OPEN, CI green, unmerged). Heart read STALE ("release validation incomplete: no rehearsal for current source"); no override used. Streaming (`from_stream`, chunk 65536, fp64, synthetic 400×400 mask NUFFT), compile split from execution:
+  - 1e8 visibilities, RAL CPU (`ral`, Xeon 8490H, 8 threads): 707.5 s wall, 3.1 s compile, 1.78 GB peak RSS; 7.04 s per 1e6 vis.
+  - 1e8 visibilities, A100: 157.3 s wall, 3.6 s compile, 1.48 GB device; 1.53 s per 1e6 vis. Compile is constant at ~3 s across N_vis.
+  - In-memory: the RAL CPU 10 GB cap fails at 2.5e5; uncapped, 1.6e7 in 68.3 s at 119.3 GB (~7.5 kB/vis). The A100 fails at 4e6 (46.2 GB vs the 63.8 GB limit). Streaming is the only route to ALMA-cube visibility counts on either device.
+  - Streamed vs in-memory parity: 1.2e-9 nats at 5e5, 6.1e-8 at 4e6.
+  - Cap-failure points are host-dependent; quote uncapped peak RSS as the portable memory figure.
+- **Human decision 2026-10-17 (chat):** interferometer CPU work closed; GPU levers filed as research, no source changes. The library candidate (chunk `transformer.image_from` in `apply_sparse_operator`) stays unfiled. Optional measured 2e8 rows recorded as research, not a task. Status → parked.
+- [Full dated review](../checkins/2026-10-17.md). Review time does not refresh measurements or authorize compute; merge is the human's.
+
 ---
 
 # Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities
diff --git a/tasks/interferometer_w_tilde_fft_size_levers.md b/tasks/interferometer_w_tilde_fft_size_levers.md
index d4e6215..e34cab9 100644
--- a/tasks/interferometer_w_tilde_fft_size_levers.md
+++ b/tasks/interferometer_w_tilde_fft_size_levers.md
@@ -26,6 +26,15 @@ GitHub sources and limits. Current RAL queue was empty; the transfer preview tim
 out, so historical jobs and unpulled results remain unverified. Review time does
 not refresh measurements or authorize compute. Historical task text is retained.
 
+
+## Check-in 2026-10-17 (evening)
+
+- **Measured on the A100** under autolens_profiling issue #429, PR [#433](https://github.com/PyAutoLabs/autolens_profiling/pull/433) (OPEN, CI green, unmerged; titles still name the CPU route constants, which were dropped). RAL job 412515, `euclid-ral-gpu-1`, A100 80GB, JAX 0.10.2, fp64, private 2026.10.17.1 stack, compile excluded. Note `results/notes/interferometer_w_tilde_fft_size_levers_2026_10.md`; witness `results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64_fft_prune.json`. Heart read STALE ("release validation incomplete: no rehearsal for current source"); no override used.
+- **(1) Pruned padded FFT vs library F** (Delaunay-1500, batch 128): alma 140² 17.97 → 14.49 ms, alma_high 280² 65.45 → 50.96 ms, jvla 700² 499.6 → 351.2 ms (F saving 19.4 / 22.1 / 29.7 %; whole likelihood 1.08× / 1.18× / 1.41×). F max rel diff ≤ 1.7e-15 (bar 1e-12), figure-of-merit shift 0 (bar 1e-6 nats). The saving grows with extent; four 1-D cuFFT calls beat one 2-D call at every extent measured.
+- **(2) Real-space pixel scale at jvla:** 0.015 / 0.02 / 0.025″ instead of 0.01″ cuts F 1.53× / 4.09× / 7.61× but shifts log evidence +1,310 / +1,807 / +2,757 nats (mesh rebuilt per scale), failing the 0.5-nat bar: a modelling choice, not a speed setting. No workspace docs prompt. At 0.015″ the padded length 932 = 2²·233 limits the gain (awkward FFT lengths matter on the GPU too).
+- **Human decision 2026-10-17 (chat):** interferometer GPU levers — "file wiki research but not make source code changes for now". The PyAutoArray `apply_operator` JAX pruning prompt is NOT filed; the prototype (`apply_pruned` / `curvature_pruned` in `scripts/interferometer/delaunay/fft_size_levers.py`) stays in the project. Route-constant refit on CPU deferred (CPU lane closed). Status → parked (research filed).
+- [Full dated review](../checkins/2026-10-17.md). Review time does not refresh measurements or authorize compute; merge is the human's.
+
 ---
 
 # Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space pixel scale on the A100
diff --git a/tasks/point_source_image_plane_gpu_breakdown.md b/tasks/point_source_image_plane_gpu_breakdown.md
index ecbaeaa..226b48c 100644
--- a/tasks/point_source_image_plane_gpu_breakdown.md
+++ b/tasks/point_source_image_plane_gpu_breakdown.md
@@ -39,6 +39,18 @@ GitHub sources and limits. Current RAL queue was empty; the transfer preview tim
 out, so historical jobs and unpulled results remain unverified. Review time does
 not refresh measurements or authorize compute. Historical task text is retained.
 
+
+## Check-in 2026-10-17 (evening)
+
+- **A100 reference measurement:** autolens_profiling PR [#434](https://github.com/PyAutoLabs/autolens_profiling/pull/434) (issue #427; OPEN, CI green, unmerged). Heart read STALE ("release validation incomplete: no rehearsal for current source"); no override used. Job 412519, `euclid-ral-gpu-2` exclusive, A100 80GB, jax 0.10.2, fp64, 2026.10.17.1 private clone:
+  - Single call 0.934 ms (pre-#768 0.910 ms); vmap-256 0.0125 ms per likelihood (0.0120); compile 6.54 s, separate.
+  - Forward gradient finite 16/16 and equal to reverse to 1.6e-11 (was NaN before #768); reverse/likelihood 1.889. Strict FD check 4/6, failing at the same points as before. logL bit-exact.
+  - Within-run MDI 1.50 %. The +2.7 % against job 366916 is not attributed (different node and date).
+  - The A100 per-step breakdown rows are unusable (11/43 differences negative); A100 step attribution needs a device trace.
+  - jax 0.10.2 on RAL is outside 2026.10.17.1's declared jax range; SHAs are the provenance.
+- **Human decision 2026-10-17 (chat):** image plane is "just research to file". Campaign and task → parked with this as the reference measurement; the phase-2 autolens_inference image-plane fit measurement is dropped. No baseline admission is recorded.
+- [Full dated review](../checkins/2026-10-17.md). Review time does not refresh measurements or authorize compute; merge is the human's.
+
 ---
 
 # Point-source A100 speed-up campaign: profile and optimize with the shared breakdown
diff --git a/tasks/point_source_source_plane_chi_squared_speed.md b/tasks/point_source_source_plane_chi_squared_speed.md
index b3e0c77..ae4d1dd 100644
--- a/tasks/point_source_source_plane_chi_squared_speed.md
+++ b/tasks/point_source_source_plane_chi_squared_speed.md
@@ -27,6 +27,17 @@ GitHub sources and limits. Current RAL queue was empty; the transfer preview tim
 out, so historical jobs and unpulled results remain unverified. Review time does
 not refresh measurements or authorize compute. Historical task text is retained.
 
+
+## Check-in 2026-10-17 (evening)
+
+- **Gradient-search admission evidence landed:** autolens_inference PR [#29](https://github.com/PyAutoLabs/autolens_inference/pull/29) (issue #25) and autolens_profiling PR [#432](https://github.com/PyAutoLabs/autolens_profiling/pull/432) (issue #428); both OPEN, CI green, unmerged. Heart read STALE ("release validation incomplete: no rehearsal for current source"); no override used.
+  - `af.MultiStartAdam`, forward gradients, 48 starts, 5 seeds, released 2026.10.17.1 wheels (jax 0.11.2), fp64, RAL CPU (`ral`, 8490H) and A100 (RAL 412495/412498).
+  - Default 300 steps: 4/5 seeds stop on the step limit, two 1.4σ off. 3000 steps: all seeds converge into the Nautilus log-L range, parameters ≤ 0.8σ from truth.
+  - Likelihood + gradient is ≤ 3.7 % of fit wall. Per step 1.7 ms CPU / 4.4 ms A100, of which 0.24 / 0.35 ms is likelihood + gradient. The A100 is slower end to end than 8 CPU cores.
+  - So a likelihood/gradient speed-up caps at a few percent of such fits.
+- **Human decision 2026-10-17 (chat):** source-plane speed-ups "not worth pursuing again, file as research". Task → complete; campaign closed as research. Blackjax stays parked. The Adam step-limit observation is recorded as a PyAutoFit search-settings note, not a task.
+- [Full dated review](../checkins/2026-10-17.md). Review time does not refresh measurements or authorize compute; merge is the human's.
+
 ---
 
 # Point-source source-plane chi-squared speed-up campaign — remaining candidates (phases 1–2e shipped)
diff --git a/tasks/runtime_cell_single_jit_gpu_warmup.md b/tasks/runtime_cell_single_jit_gpu_warmup.md
index 7844080..2a4d758 100644
--- a/tasks/runtime_cell_single_jit_gpu_warmup.md
+++ b/tasks/runtime_cell_single_jit_gpu_warmup.md
@@ -39,6 +39,13 @@ GitHub sources and limits. Current RAL queue was empty; the transfer preview tim
 out, so historical jobs and unpulled results remain unverified. Review time does
 not refresh measurements or authorize compute. Historical task text is retained.
 
+
+## Check-in 2026-10-17 (evening)
+
+- **Fresh median evidence:** autolens_profiling PR [#432](https://github.com/PyAutoLabs/autolens_profiling/pull/432) (OPEN, CI green, unmerged), 2026.10.17.1 wheels. Source-plane solved runtime cell: A100 median 0.233 ms (`single_jit` 0.347 ms); RAL CPU median 0.147 ms (`single_jit` 0.177 ms). Pins pass. `single_jit` sits above the median on both, so the "first block after compile" label and the quote-the-median rule hold. Heart read STALE ("release validation incomplete: no rehearsal for current source"); no override used.
+- Task → complete (witness met at PR-open; merge is the human's). Remaining release-sweep cells gain the field at the next full sweep.
+- [Full dated review](../checkins/2026-10-17.md). Review time does not refresh measurements or authorize compute; merge is the human's.
+
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 # Runtime cells' A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node