diff --git a/campaigns.yaml b/campaigns.yaml
index 8ce81d2..f70fe4d 100644
--- a/campaigns.yaml
+++ b/campaigns.yaml
@@ -10,7 +10,7 @@ campaigns:
     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.'
+    new measurement. Post-/prm 2026-10-17: autolens_profiling#418 (pilot qualification design) merged after an independent review (FINDINGS, none blocking); issue #417 closed; Mind record complete/2026/10/hst-mge-pilot-qualification.md. Carried to the next bounded step: background-load method (none proposed), the 1e-8 relative image L2 budget (likely unattainable; synthetic-harness calibration must be allowed to revise tolerances), and the correctness-process feasibility needed before the 32-process budget is final.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/pull/416
 - id: point-source-image-cpu
   title: Point-source image plane · CPU
@@ -27,22 +27,25 @@ campaigns:
   title: Point-source image plane · A100
   status: parked
   updated: '2026-10-17'
-  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.'
+  next: 'Parked as research, human decision 2026-10-17 (chat) ("just research to file"). Reference measurement
+    autolens_profiling#434 (issue #427 closed) merged 2026-10-17 (human /prm): 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. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/point-source-image-gpu-admission.md.'
   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: complete
   updated: '2026-10-17'
   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: autolens_inference#29 (issue #25) + autolens_profiling#432 (issue #428), both merged 2026-10-17 (human
+    /prm), issues closed: 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. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ps-source-gradient-admission.md.'
   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
@@ -54,16 +57,18 @@ campaigns:
   title: Interferometer likelihood and streaming
   status: parked
   updated: '2026-10-17'
-  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.'
+  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. All merged 2026-10-17
+    (human /prm), pending release for the library PRs: L2 symmetric direct_conv PyAutoArray#636 (issue #635 closed,
+    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 (issue #429 closed) (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 (issues #368, #426 closed). Route-constant refit (RAL
+    FFT/full direct_conv 0.68 vs 0.89 predicted) deferred. Merged in order #636, #425, #432, #431, #433, #434, #435.
+    Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/direct-conv-symmetric.md,
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ral-route-constants-fft-levers.md,
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09f/wiki/campaigns/interferometer_likelihood.md
 - id: linear-solver
   title: Linear-solver accuracy and cost
@@ -80,22 +85,24 @@ campaigns:
   title: Production imaging and likelihood breakdown
   status: active
   updated: '2026-10-17'
-  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.'
+  next: 'Production over-sampling phase 1 merged 2026-10-17 (human /prm): autolens_profiling#431 (issue #430 closed,
+    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. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/imaging-production-over-sampling.md. Human decision 2026-10-17 (chat): hold off on phase 2 (A100 production over-sampling re-measure); not authorized, revisit when production imaging needs a cost picture.'
   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.'
+  next: 'Created parked, human decision 2026-10-17 (chat): "file in research to come back to in the future".
+    PyAutoArray#637 (issue #629 closed) merged 2026-10-17 (human /prm), pending-release: 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. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/delaunaynn-wide-row-overhead.md.'
   evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/637
 - id: measurement-tools
   title: Measurement reliability and profiling tools
@@ -161,7 +168,7 @@ tasks:
   updated: '2026-10-13'
   next: '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.'
+    settings, witness/tolerances and background-load method. No freeze, compute or acceptance authorized. Post-/prm 2026-10-17: #418 merged (QUALIFICATION.md), #417 closed; record https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/hst-mge-pilot-qualification.md. Next bounded step: the synthetic witness harness that also fixes the background-load method and calibrates tolerances; still no collection, freeze or acceptance authorized.'
   issue: https://github.com/PyAutoLabs/autolens_profiling/issues/415
   evidence: https://github.com/PyAutoLabs/autolens_profiling/pull/416
 - id: runtime_cell_single_jit_gpu_warmup
@@ -172,8 +179,10 @@ tasks:
   priority: low
   path: tasks/runtime_cell_single_jit_gpu_warmup.md
   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.'
+  next: 'Fresh medians in autolens_profiling#432, merged 2026-10-17 (human /prm), 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. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ps-source-gradient-admission.md.'
   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
@@ -289,14 +298,15 @@ tasks:
 - id: imaging_production_over_sampling
   campaign: imaging
   title: Profile imaging pixelizations at production over-sampling
-  status: needs-decision
+  status: parked
   priority: normal
   path: tasks/imaging_production_over_sampling.md
   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.'
+  next: 'Phase 1 merged 2026-10-17 (human /prm): autolens_profiling#431 (issue #430 closed, CPU smoke 8/8). Phase 2
+    A100 production re-measure needs-decision (human decision 2026-10-17 (chat): undecided); requires compute
+    authorization (#431 now merged). Findings: HST flat-1 adapt_split pin fails on main; numba HST threshold never
+    reaches sub 4; rectangular JAX cells uncovered. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/imaging-production-over-sampling.md. Human decision 2026-10-17 (chat): phase 2 A100 re-measure held; task stays parked at phase 1 shipped (#431).'
   evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/606
 - id: interferometer_decision_matrix_last_cell
   campaign: interferometer
@@ -328,11 +338,12 @@ tasks:
   priority: medium
   path: tasks/interferometer_nnls_memo_scattered_stream_guard.md
   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).'
+  next: 'After-measurement autolens_profiling#435 merged 2026-10-17 (human /prm) (issue #426 closed; 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). Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md.'
   issue: https://github.com/PyAutoLabs/PyAutoArray/issues/613
   evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/615
 - id: interferometer_streaming_scaling
@@ -342,10 +353,11 @@ tasks:
   priority: high
   path: tasks/interferometer_streaming_scaling.md
   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.'
+  next: 'Phase 2 autolens_profiling#435 merged 2026-10-17 (human /prm) (issue #368 closed): 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. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md.'
   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
@@ -356,10 +368,12 @@ tasks:
   path: tasks/interferometer_large_mask_aris.md
   updated: '2026-10-17'
   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.'
+    both merged 2026-10-17 (human /prm); issue #635 closed. 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: release of #636; shadow row owed in Mind
+    autonomy_log (glance tier; human to confirm merged-unchanged). Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/direct-conv-symmetric.md. Shadow row appended 2026-10-17 (merged-unchanged, stage 1). Human decision 2026-10-17: remaining Aris levers stay research; not enough return to implement for now.'
   issue: https://github.com/PyAutoLabs/PyAutoPulse/issues/62
   evidence: https://github.com/PyAutoLabs/PyAutoArray/issues/626
 - id: interferometer_w_tilde_fft_size_levers
@@ -370,9 +384,10 @@ tasks:
   priority: normal
   path: tasks/interferometer_w_tilde_fft_size_levers.md
   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.'
+  next: 'A100 research autolens_profiling#433 merged 2026-10-17 (human /prm) (issue #429 closed; 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.
+    Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ral-route-constants-fft-levers.md.'
 - id: point_solver_profiling_cells
   campaign: cluster-pointsolver
   title: 'PointSolver profiling cells: lensed quasar → cluster runtime tier → single/multi-source → multiplane'
@@ -388,8 +403,10 @@ tasks:
   priority: high
   path: tasks/point_source_image_plane_gpu_breakdown.md
   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.'
+  next: 'Parked, human decision 2026-10-17 (chat). Reference A100 measurement autolens_profiling#434 (merged
+    2026-10-17 (human /prm), issue #427 closed, job 412519): 0.934 ms single call, 0.0125 ms at vmap-256, forward
+    gradient fixed; phase-2 inference fit measurement dropped. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/point-source-image-gpu-admission.md.'
   issue: https://github.com/PyAutoLabs/PyAutoLens/issues/767
   evidence: https://github.com/PyAutoLabs/PyAutoLens/pull/768
 - id: point_source_source_plane_chi_squared_speed
@@ -399,9 +416,10 @@ tasks:
   priority: low
   path: tasks/point_source_source_plane_chi_squared_speed.md
   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.'
+  next: 'Complete as research, human decision 2026-10-17 (chat). Admission evidence autolens_inference#29 +
+    autolens_profiling#432 (merged 2026-10-17 (human /prm); issues #25, #428 closed): likelihood+gradient ≤3.7% of
+    MultiStartAdam fit wall, so a likelihood speed-up caps at a few percent. Blackjax stays parked. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ps-source-gradient-admission.md.'
   evidence: https://github.com/PyAutoLabs/autolens_inference/pull/17
 - id: pointsolver_cpu_speed_campaign_remainder
   campaign: point-source-image-cpu
@@ -470,8 +488,9 @@ tasks:
   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.'
+  next: 'Parked, human decision 2026-10-17 (chat). PyAutoArray#637 merged 2026-10-17 (human /prm) (issue #629
+    closed, pending-release): 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. Records:
+    https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/delaunaynn-wide-row-overhead.md.'
   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
index e373f58..39692d1 100644
--- a/checkins/2026-10-17.md
+++ b/checkins/2026-10-17.md
@@ -88,3 +88,13 @@ and independent; merge #425 after #636.
 - `/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.
+
+## Post-/prm
+
+The human ran `/prm` on 2026-10-17 after this review; every PR above merged.
+
+- **Merged:** PyAutoArray [#636](https://github.com/PyAutoLabs/PyAutoArray/pull/636) and [#637](https://github.com/PyAutoLabs/PyAutoArray/pull/637) (both pending-release); autolens_profiling [#425](https://github.com/PyAutoLabs/autolens_profiling/pull/425), [#432](https://github.com/PyAutoLabs/autolens_profiling/pull/432), [#431](https://github.com/PyAutoLabs/autolens_profiling/pull/431), [#433](https://github.com/PyAutoLabs/autolens_profiling/pull/433), [#434](https://github.com/PyAutoLabs/autolens_profiling/pull/434), [#435](https://github.com/PyAutoLabs/autolens_profiling/pull/435) (main now c40e33c8, the merge of #435); autolens_inference [#29](https://github.com/PyAutoLabs/autolens_inference/pull/29); PyAutoPulse [#66](https://github.com/PyAutoLabs/PyAutoPulse/pull/66) and [#67](https://github.com/PyAutoLabs/PyAutoPulse/pull/67).
+- **Issues closed:** PyAutoArray #635, #629; autolens_profiling #427, #428, #429, #430, #368, #426; autolens_inference #25.
+- **Mind completion records:** [`direct-conv-symmetric`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/direct-conv-symmetric.md), [`delaunaynn-wide-row-overhead`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/delaunaynn-wide-row-overhead.md), [`ps-source-gradient-admission`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ps-source-gradient-admission.md), [`imaging-production-over-sampling`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/imaging-production-over-sampling.md), [`point-source-image-gpu-admission`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/point-source-image-gpu-admission.md), [`ral-route-constants-fft-levers`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ral-route-constants-fft-levers.md), [`interferometer-streaming-memo-after`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md).
+- **Owed:** shadow row in Mind `autonomy_log` for the Aris L2 work (glance tier; human to confirm merged-unchanged).
+- Library speed-ups (#636, #637) are still unreleased; the figures above remain PR-time measurements until a release carries them. This section links close-outs and does not count as a check-in.
diff --git a/dashboard.html b/dashboard.html
index 22bc19e..b67c321 100644
--- a/dashboard.html
+++ b/dashboard.html
@@ -671,8 +671,8 @@
 - PyAutoPulse: https://github.com/PyAutoLabs/PyAutoPulse
 - autolens_profiling: https://github.com/PyAutoLabs/autolens_profiling
 - autofit_profiling: https://github.com/PyAutoLabs/autofit_profiling
-- autolens_inference: https://github.com/PyAutoLabs/autolens_inference</textarea></details><div class="orchestration-freshness" data-freshness="grey"><details data-freshness-stamp data-refreshed-at="2026-10-23T17:38:21Z"><summary><span data-freshness-label>Last updated 2026-10-23 17:38:21 UTC</span></summary><time datetime="2026-10-23T17:38:21Z">2026-10-23 17:38:21 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:b2279cce088381119a25ae69e4a1a493c347db477e562fe28b38596ca10f6197 --><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>Human decision 2026-10-23 (chat): keep the 1.0″ starting grid as the production default (decision record cluster-pointsolver-starting-grid; closes the phase-1 deferral). Inputs: phase-1 simple sweep (#438), A2744 pilot job 412573 (1.0″ misses fewest, 2/69), starting-grid follow-up autolens_profiling#439 → #440 merged 03d36c81 (no fixed scale finds every image; no separation rule). Mitigation: the workspace validation check. Next bounded task: spurious-core candidates library task (start_dev in progress), then the validation check. No default changed.</p><span class="review-meta">Reviewed 2026-10-23</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">active · normal</p><p>Decision taken 2026-10-23 (human): keep 1.0″ + validation check; #439/#440 merged (03d36c81). Next: spurious-core candidates library task (start_dev in progress), then the workspace validation check.</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). Reconcile onto the phase-1 harness (scripts/cluster/image_plane/robust_settings_sweep.py) when the next cell is issued; no duplicate cell.</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/03d36c810b2a4f7d31342d9a75f46cd7fe10f43a/results/notes/cluster_pointsolver_campaign.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":21002,"comparison_policy":{"id":"no-temporal-comparisons"},"comparisons":[],"coverage":{"excluded":[],"expected":{"baseline_cells":29,"cells":105,"planned_slots":435},"observed":{"records":112,"selected":98,"setups":587}},"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":"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":"da54dc30ff83c7682527984fa0e8a710a70a9927","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autolens_profiling/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/results/breakdown/cluster/robust_settings_sweep_simple_draws53_73_81_laptop_cpu_fp64.json">cluster / other / unspecified — results/breakdown/cluster/robust_settings_sweep_simple_draws53_73_81_laptop_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/breakdown/cluster/robust_settings_sweep_simple_hpc_ral_cpu_fp64.json">cluster / other / unspecified — results/breakdown/cluster/robust_settings_sweep_simple_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/breakdown/cluster/robust_settings_sweep_viz_cluster_laptop_cpu_fp64.json">cluster / other / unspecified — results/breakdown/cluster/robust_settings_sweep_viz_cluster_laptop_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging 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href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64_fft_prune.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64_fft_prune.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/results/breakdown/point_source_image/image_plane_hpc_ral_a100_fp64_r2026_10_17_1.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_a100_fp64_r2026_10_17_1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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/da54dc30ff83c7682527984fa0e8a710a70a9927/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" class="setup-payload">{"catalogue":{"schema":"profiling-summary","version":2,"project":"autofit_profiling","scope":"pyautofit-profiling","generated_at":"2026-10-14T14:45:45Z","evidence_updated_at":"2026-10-14T14:45:45Z","valid_until":null,"producer_revision":null,"comparison_policy":{"id":"no-temporal-comparisons"},"coverage":{"expected":{"cells":32},"observed":{"setups":9,"records":31,"selected":31},"excluded":[]},"setups":[{"id":"analytic_gaussian/parity/seed0_n5","label":"analytic Gaussian parity: closed 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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":"0481145eee4a01c62b4cbfd87a57de01ce40655a","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autofit_profiling/0481145eee4a01c62b4cbfd87a57de01ce40655a/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><section class="walltime-validation" data-walltime="lens-walltime" aria-label="autolens_inference wall-time validation"><h3>autolens_inference: wall-time validation</h3><p>Full-fit measurements: one sampler run per row. Every derived number is the producer&#x27;s; Pulse computes no ratio and never reads a full-fit time as a per-likelihood measurement.</p><div class='tablewrap'><table><thead><tr><th>Likelihood</th><th>Instrument</th><th>Setup</th><th>Hardware class</th><th>Model variant</th><th>Sampler</th><th>Seed</th><th>Cores</th><th>Wall s</th><th>Likelihood evals</th><th>Implied ms/eval</th><th>Predicted ms/eval</th><th>Measured/predicted</th><th>Parallel efficiency</th><th>Parity</th><th>Reference runtime record</th><th>Release</th></tr></thead><tbody><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>8648.197281122208</td><td>38660</td><td>223.69884327786363</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>8</td><td>453.9056279659271</td><td>2120</td><td>214.1064282858147</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>8</td><td>12609.190230846405</td><td>53920</td><td>233.84996718928795</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>8</td><td>2269.41446018219</td><td>10180</td><td>222.928728898054</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>4563.577402114868</td><td>34060</td><td>133.98641814782349</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>32440.87102484703</td><td>30400</td><td>1067.1339152910207</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>8</td><td>5783.566254377365</td><td>5560</td><td>1040.2097579815406</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>8</td><td>38212.712732076645</td><td>34540</td><td>1106.3321578481946</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>8</td><td>5446.4585750103</td><td>11080</td><td>491.5576331236732</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>0</td><td>8</td><td>3367.1460740566254</td><td>92550</td><td>36.38191327992032</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>24129.204991340637</td><td>22020</td><td>1095.7858760826812</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>8</td><td>4042.287693500519</td><td>3720</td><td>1086.6364767474513</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>8</td><td>38641.76520562172</td><td>34180</td><td>1130.5373085319404</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>8</td><td>5703.012356042862</td><td>10620</td><td>537.0068131867101</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>1</td><td>8</td><td>3410.500566005707</td><td>91250</td><td>37.375348668555695</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>36050.4700050354</td><td>21720</td><td>1659.78222859279</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>8</td><td>6332.766473293304</td><td>3820</td><td>1657.7922704956293</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>8</td><td>83132.6692700386</td><td>34540</td><td>2406.852034453926</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>8</td><td>3972.188988685608</td><td>10880</td><td>365.0908996953684</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>0</td><td>8</td><td>3471.9577021598816</td><td>92550</td><td>37.51439980723805</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>4</td><td>558.6179111003876</td><td>23680</td><td>23.590283407955553</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>4</td><td>181.06282258033752</td><td>7800</td><td>23.21318238209455</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>4</td><td>700.8667027950287</td><td>33020</td><td>21.225520981072947</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>4</td><td>234.4628677368164</td><td>11600</td><td>20.21231618420831</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>0</td><td>4</td><td>714.9393587112427</td><td>92550</td><td>7.724898527404027</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>4</td><td>524.995144367218</td><td>21640</td><td>24.26040408351285</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>4</td><td>130.3421642780304</td><td>5000</td><td>26.06843285560608</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>4</td><td>732.5671072006226</td><td>34520</td><td>21.22152685980946</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>4</td><td>219.77784371376038</td><td>10100</td><td>21.76018254591687</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>1</td><td>4</td><td>712.3624505996704</td><td>87700</td><td>8.122718935001943</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>4</td><td>826.8618957996368</td><td>23060</td><td>35.85697726798078</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>4</td><td>298.14068365097046</td><td>8140</td><td>36.62661961314134</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>4</td><td>1786.1555726528168</td><td>32820</td><td>54.42277796017114</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>4</td><td>221.9318356513977</td><td>11100</td><td>19.993859067693485</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>0</td><td>4</td><td>714.965235710144</td><td>92550</td><td>7.725178127608256</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>4</td><td>811.5136122703552</td><td>22980</td><td>35.31390827982399</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>4</td><td>226.34285354614258</td><td>6000</td><td>37.723808924357094</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>4</td><td>1648.602859735489</td><td>30360</td><td>54.30180697415971</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>4</td><td>196.74390411376953</td><td>9200</td><td>21.385206968887992</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>1</td><td>4</td><td>719.88591837883</td><td>87700</td><td>8.208505340693614</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>2605.161903142929</td><td>22920</td><td>113.66325929942973</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>8</td><td>579.7866718769073</td><td>5660</td><td>102.43580775210377</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>8</td><td>4961.520958900452</td><td>33660</td><td>147.40109800654935</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>8</td><td>1275.32878780365</td><td>10520</td><td>121.22897222468156</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>2562.1093657016754</td><td>22240</td><td>115.20275924917605</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>8</td><td>939.1733293533325</td><td>9460</td><td>99.278364625088</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>8</td><td>4867.771735191345</td><td>33200</td><td>146.61963057805258</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>8</td><td>1478.9101498126984</td><td>12000</td><td>123.24251248439153</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>21738.99609732628</td><td>23780</td><td>914.1714086344103</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>20092.072603940964</td><td>20940</td><td>959.506810121345</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>0</td><td>4</td><td>490.45253920555115</td><td>22820</td><td>21.492223453354562</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>4</td><td>449.70745611190796</td><td>20780</td><td>21.64135977439403</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>0</td><td>4</td><td>1624.8536005020142</td><td>48700</td><td>33.36455031831651</td><td>—</td><td>—</td><td>—</td><td>diverged</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>4</td><td>1396.915834903717</td><td>41880</td><td>33.35520140648799</td><td>—</td><td>—</td><td>—</td><td>diverged</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>2329.3615324497223</td><td>24340</td><td>95.70096682209213</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>2076.24631023407</td><td>21960</td><td>94.54673543870992</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr></tbody></table></div></section><!-- decision-history:4d88bb773a36f774562f9e06d52a024b839cc44d2aa91282311891f6064c43c3 --></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/cluster-pointsolver-starting-grid.md" target="_blank" rel="noopener" title="Opens in a new tab">Keep the 1.0″ PointSolver starting grid for cluster fits, with a validation check</a><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;
+- autolens_inference: https://github.com/PyAutoLabs/autolens_inference</textarea></details><div class="orchestration-freshness" data-freshness="grey"><details data-freshness-stamp data-refreshed-at="2026-10-23T21:50:09Z"><summary><span data-freshness-label>Last updated 2026-10-23 21:50:09 UTC</span></summary><time datetime="2026-10-23T21:50:09Z">2026-10-23 21:50:09 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:534100f0568a525813aff5c2ac922f02789eb6251f61b94cb960a3b0b19be138 --><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. Post-/prm 2026-10-17: autolens_profiling#418 (pilot qualification design) merged after an independent review (FINDINGS, none blocking); issue #417 closed; Mind record complete/2026/10/hst-mge-pilot-qualification.md. Carried to the next bounded step: background-load method (none proposed), the 1e-8 relative image L2 budget (likely unattainable; synthetic-harness calibration must be allowed to revise tolerances), and the correctness-process feasibility needed before the 32-process budget is final.</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. Post-/prm 2026-10-17: #418 merged (QUALIFICATION.md), #417 closed; record https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/hst-mge-pilot-qualification.md. Next bounded step: the synthetic witness harness that also fixes the background-load method and calibrates tolerances; still no collection, freeze 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 closed) merged 2026-10-17 (human /prm): 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/point-source-image-gpu-admission.md.</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 (merged 2026-10-17 (human /prm), issue #427 closed, job 412519): 0.934 ms single call, 0.0125 ms at vmap-256, forward gradient fixed; phase-2 inference fit measurement dropped. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/point-source-image-gpu-admission.md.</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>Human decision 2026-10-23 (chat): keep the 1.0″ starting grid as the production default (decision record cluster-pointsolver-starting-grid; closes the phase-1 deferral). Inputs: phase-1 simple sweep (#438), A2744 pilot job 412573 (1.0″ misses fewest, 2/69), starting-grid follow-up autolens_profiling#439 → #440 merged 03d36c81 (no fixed scale finds every image; no separation rule). Mitigation: the workspace validation check. Next bounded task: spurious-core candidates library task (start_dev in progress), then the validation check. No default changed.</p><span class="review-meta">Reviewed 2026-10-23</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">active · normal</p><p>Decision taken 2026-10-23 (human): keep 1.0″ + validation check; #439/#440 merged (03d36c81). Next: spurious-core candidates library task (start_dev in progress), then the workspace validation check.</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). Reconcile onto the phase-1 harness (scripts/cluster/image_plane/robust_settings_sweep.py) when the next cell is issued; no duplicate cell.</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/03d36c810b2a4f7d31342d9a75f46cd7fe10f43a/results/notes/cluster_pointsolver_campaign.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. All merged 2026-10-17 (human /prm), pending release for the library PRs: L2 symmetric direct_conv PyAutoArray#636 (issue #635 closed, 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 (issue #429 closed) (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 (issues #368, #426 closed). Route-constant refit (RAL FFT/full direct_conv 0.68 vs 0.89 predicted) deferred. Merged in order #636, #425, #432, #431, #433, #434, #435. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/direct-conv-symmetric.md, https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ral-route-constants-fft-levers.md, https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md.</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 autolens_profiling#435 merged 2026-10-17 (human /prm) (issue #426 closed; 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). Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md.</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 autolens_profiling#435 merged 2026-10-17 (human /prm) (issue #368 closed): 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md.</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 merged 2026-10-17 (human /prm); issue #635 closed. 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: release of #636; shadow row owed in Mind autonomy_log (glance tier; human to confirm merged-unchanged). Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/direct-conv-symmetric.md. Shadow row appended 2026-10-17 (merged-unchanged, stage 1). Human decision 2026-10-17: remaining Aris levers stay research; not enough return to implement for now.</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 autolens_profiling#433 merged 2026-10-17 (human /prm) (issue #429 closed; 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ral-route-constants-fft-levers.md.</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 merged 2026-10-17 (human /prm): autolens_profiling#431 (issue #430 closed, 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/imaging-production-over-sampling.md. Human decision 2026-10-17 (chat): hold off on phase 2 (A100 production over-sampling re-measure); not authorized, revisit when production imaging needs a cost picture.</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">parked · normal</p><p>Phase 1 merged 2026-10-17 (human /prm): autolens_profiling#431 (issue #430 closed, CPU smoke 8/8). Phase 2 A100 production re-measure needs-decision (human decision 2026-10-17 (chat): undecided); requires compute authorization (#431 now merged). Findings: HST flat-1 adapt_split pin fails on main; numba HST threshold never reaches sub 4; rectangular JAX cells uncovered. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/imaging-production-over-sampling.md. Human decision 2026-10-17 (chat): phase 2 A100 re-measure held; task stays parked at phase 1 shipped (#431).</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 closed) merged 2026-10-17 (human /prm), pending-release: 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/delaunaynn-wide-row-overhead.md.</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 merged 2026-10-17 (human /prm) (issue #629 closed, pending-release): 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/delaunaynn-wide-row-overhead.md.</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":21002,"comparison_policy":{"id":"no-temporal-comparisons"},"comparisons":[],"coverage":{"excluded":[],"expected":{"baseline_cells":29,"cells":105,"planned_slots":435},"observed":{"records":112,"selected":98,"setups":587}},"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":"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":"aeef27565b64520ae01faf6200286aee842ace6b","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autolens_profiling/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/results/breakdown/cluster/robust_settings_sweep_simple_draws53_73_81_laptop_cpu_fp64.json">cluster / other / unspecified — results/breakdown/cluster/robust_settings_sweep_simple_draws53_73_81_laptop_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/breakdown/cluster/robust_settings_sweep_simple_hpc_ral_cpu_fp64.json">cluster / other / unspecified — results/breakdown/cluster/robust_settings_sweep_simple_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/breakdown/cluster/robust_settings_sweep_viz_cluster_laptop_cpu_fp64.json">cluster / other / unspecified — results/breakdown/cluster/robust_settings_sweep_viz_cluster_laptop_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64_fft_prune.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64_fft_prune.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/results/breakdown/point_source_image/image_plane_hpc_ral_a100_fp64_r2026_10_17_1.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_a100_fp64_r2026_10_17_1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_a100_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_nocap_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_hpc_ral_cpu_fp64_v2026.10.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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/aeef27565b64520ae01faf6200286aee842ace6b/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 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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":"0481145eee4a01c62b4cbfd87a57de01ce40655a","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autofit_profiling/0481145eee4a01c62b4cbfd87a57de01ce40655a/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><section class="walltime-validation" data-walltime="lens-walltime" aria-label="autolens_inference wall-time validation"><h3>autolens_inference: wall-time validation</h3><p>Full-fit measurements: one sampler run per row. Every derived number is the producer&#x27;s; Pulse computes no ratio and never reads a full-fit time as a per-likelihood measurement.</p><div class='tablewrap'><table><thead><tr><th>Likelihood</th><th>Instrument</th><th>Setup</th><th>Hardware class</th><th>Model variant</th><th>Sampler</th><th>Seed</th><th>Cores</th><th>Wall s</th><th>Likelihood evals</th><th>Implied ms/eval</th><th>Predicted ms/eval</th><th>Measured/predicted</th><th>Parallel efficiency</th><th>Parity</th><th>Reference runtime record</th><th>Release</th></tr></thead><tbody><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>8648.197281122208</td><td>38660</td><td>223.69884327786363</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>8</td><td>453.9056279659271</td><td>2120</td><td>214.1064282858147</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>8</td><td>12609.190230846405</td><td>53920</td><td>233.84996718928795</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>8</td><td>2269.41446018219</td><td>10180</td><td>222.928728898054</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/delaunay</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>4563.577402114868</td><td>34060</td><td>133.98641814782349</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>32440.87102484703</td><td>30400</td><td>1067.1339152910207</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>8</td><td>5783.566254377365</td><td>5560</td><td>1040.2097579815406</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>8</td><td>38212.712732076645</td><td>34540</td><td>1106.3321578481946</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>8</td><td>5446.4585750103</td><td>11080</td><td>491.5576331236732</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>0</td><td>8</td><td>3367.1460740566254</td><td>92550</td><td>36.38191327992032</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>24129.204991340637</td><td>22020</td><td>1095.7858760826812</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>8</td><td>4042.287693500519</td><td>3720</td><td>1086.6364767474513</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>8</td><td>38641.76520562172</td><td>34180</td><td>1130.5373085319404</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>8</td><td>5703.012356042862</td><td>10620</td><td>537.0068131867101</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>1</td><td>8</td><td>3410.500566005707</td><td>91250</td><td>37.375348668555695</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>36050.4700050354</td><td>21720</td><td>1659.78222859279</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>8</td><td>6332.766473293304</td><td>3820</td><td>1657.7922704956293</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>8</td><td>83132.6692700386</td><td>34540</td><td>2406.852034453926</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>8</td><td>3972.188988685608</td><td>10880</td><td>365.0908996953684</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>0</td><td>8</td><td>3471.9577021598816</td><td>92550</td><td>37.51439980723805</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>4</td><td>558.6179111003876</td><td>23680</td><td>23.590283407955553</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>4</td><td>181.06282258033752</td><td>7800</td><td>23.21318238209455</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>4</td><td>700.8667027950287</td><td>33020</td><td>21.225520981072947</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>4</td><td>234.4628677368164</td><td>11600</td><td>20.21231618420831</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>0</td><td>4</td><td>714.9393587112427</td><td>92550</td><td>7.724898527404027</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>4</td><td>524.995144367218</td><td>21640</td><td>24.26040408351285</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>4</td><td>130.3421642780304</td><td>5000</td><td>26.06843285560608</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>4</td><td>732.5671072006226</td><td>34520</td><td>21.22152685980946</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>4</td><td>219.77784371376038</td><td>10100</td><td>21.76018254591687</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>1</td><td>4</td><td>712.3624505996704</td><td>87700</td><td>8.122718935001943</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>4</td><td>826.8618957996368</td><td>23060</td><td>35.85697726798078</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>4</td><td>298.14068365097046</td><td>8140</td><td>36.62661961314134</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>4</td><td>1786.1555726528168</td><td>32820</td><td>54.42277796017114</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>4</td><td>221.9318356513977</td><td>11100</td><td>19.993859067693485</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>0</td><td>4</td><td>714.965235710144</td><td>92550</td><td>7.725178127608256</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>4</td><td>811.5136122703552</td><td>22980</td><td>35.31390827982399</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>4</td><td>226.34285354614258</td><td>6000</td><td>37.723808924357094</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>4</td><td>1648.602859735489</td><td>30360</td><td>54.30180697415971</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>4</td><td>196.74390411376953</td><td>9200</td><td>21.385206968887992</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>default</td><td>nautilus:source_lp[1]</td><td>1</td><td>4</td><td>719.88591837883</td><td>87700</td><td>8.208505340693614</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>2605.161903142929</td><td>22920</td><td>113.66325929942973</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[2]</td><td>0</td><td>8</td><td>579.7866718769073</td><td>5660</td><td>102.43580775210377</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[1]</td><td>0</td><td>8</td><td>4961.520958900452</td><td>33660</td><td>147.40109800654935</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:light[1]</td><td>0</td><td>8</td><td>1275.32878780365</td><td>10520</td><td>121.22897222468156</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>2562.1093657016754</td><td>22240</td><td>115.20275924917605</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[2]</td><td>1</td><td>8</td><td>939.1733293533325</td><td>9460</td><td>99.278364625088</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:source_pix[1]</td><td>1</td><td>8</td><td>4867.771735191345</td><td>33200</td><td>146.61963057805258</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>default</td><td>nautilus:light[1]</td><td>1</td><td>8</td><td>1478.9101498126984</td><td>12000</td><td>123.24251248439153</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>21738.99609732628</td><td>23780</td><td>914.1714086344103</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_cpu_dense_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>20092.072603940964</td><td>20940</td><td>959.506810121345</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>0</td><td>4</td><td>490.45253920555115</td><td>22820</td><td>21.492223453354562</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_dense_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>4</td><td>449.70745611190796</td><td>20780</td><td>21.64135977439403</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>0</td><td>4</td><td>1624.8536005020142</td><td>48700</td><td>33.36455031831651</td><td>—</td><td>—</td><td>—</td><td>diverged</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>jax_gpu_sparse_fp64</td><td>hpc_a100</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>4</td><td>1396.915834903717</td><td>41880</td><td>33.35520140648799</td><td>—</td><td>—</td><td>—</td><td>diverged</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>0</td><td>8</td><td>2329.3615324497223</td><td>24340</td><td>95.70096682209213</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr><tr><td>imaging/rectangular</td><td>hst</td><td>numba_cpu_sparse_fp64</td><td>hpc_ral_cpu</td><td>light_fixed</td><td>nautilus:mass_total[1]</td><td>1</td><td>8</td><td>2076.24631023407</td><td>21960</td><td>94.54673543870992</td><td>—</td><td>—</td><td>—</td><td>not_assessed</td><td>—</td><td>2026.10.17.1</td></tr></tbody></table></div></section><!-- decision-history:4d88bb773a36f774562f9e06d52a024b839cc44d2aa91282311891f6064c43c3 --></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/cluster-pointsolver-starting-grid.md" target="_blank" rel="noopener" title="Opens in a new tab">Keep the 1.0″ PointSolver starting grid for cluster fits, with a validation check</a><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;
 function promptFits(text){
   let count=0;for(const character of text){if(++count>MAX_PROMPT_CHARS)return false;}
   return true;
diff --git a/dashboard.md b/dashboard.md
index 3e524be..76953f1 100644
--- a/dashboard.md
+++ b/dashboard.md
@@ -1,6 +1,6 @@
 # PyAutoPulse — profiling dashboard
 
-<!-- pulse-campaigns:b2279cce088381119a25ae69e4a1a493c347db477e562fe28b38596ca10f6197 -->
+<!-- pulse-campaigns:534100f0568a525813aff5c2ac922f02789eb6251f61b94cb960a3b0b19be138 -->
 ## Profiling Check In
 
 <details><summary>Full check-in prompt</summary>
@@ -31,14 +31,14 @@ Last check-in: 2026-10-23T15:00:32Z (review date, not measurement freshness).
 
 <details><summary>Setup baseline collection and scientific acceptance — needs-decision</summary>
 
-Reviewed 2026-10-17. 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.
+Reviewed 2026-10-17. 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. Post-/prm 2026-10-17: autolens_profiling#418 (pilot qualification design) merged after an independent review (FINDINGS, none blocking); issue #417 closed; Mind record complete/2026/10/hst-mge-pilot-qualification.md. Carried to the next bounded step: background-load method (none proposed), the 1e-8 relative image L2 budget (likely unattainable; synthetic-harness calibration must be allowed to revise tolerances), and the correctness-process feasibility needed before the 32-process budget is final.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/pull/416)
 
 ### Active tasks
 
 - [Freeze the setup baseline specification, then collect and review evidence](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/setup_baseline_campaign.md) — needs-decision: 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.
-- [Prepare and review the HST/MGE fp64 CPU pilot](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/hst_mge_cpu_pilot.md) — needs-decision: 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.
+- [Prepare and review the HST/MGE fp64 CPU pilot](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/hst_mge_cpu_pilot.md) — needs-decision: 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. Post-/prm 2026-10-17: #418 merged (QUALIFICATION.md), #417 closed; record https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/hst-mge-pilot-qualification.md. Next bounded step: the synthetic witness harness that also fixes the background-load method and calibrates tolerances; still no collection, freeze or acceptance authorized.
 
 </details>
 
@@ -56,13 +56,13 @@ Reviewed 2026-10-17. No new committed campaign measurements. Phases 1–4 remain
 
 <details><summary>Point-source image plane · A100 — parked</summary>
 
-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.
+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 closed) merged 2026-10-17 (human /prm): 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/point-source-image-gpu-admission.md.
 
 [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) — 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.
+- [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 (merged 2026-10-17 (human /prm), issue #427 closed, job 412519): 0.934 ms single call, 0.0125 ms at vmap-256, forward gradient fixed; phase-2 inference fit measurement dropped. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/point-source-image-gpu-admission.md.
 
 </details>
 
@@ -81,17 +81,17 @@ Reviewed 2026-10-23. Human decision 2026-10-23 (chat): keep the 1.0″ starting
 
 <details><summary>Interferometer likelihood and streaming — parked</summary>
 
-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.
+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. All merged 2026-10-17 (human /prm), pending release for the library PRs: L2 symmetric direct_conv PyAutoArray#636 (issue #635 closed, 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 (issue #429 closed) (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 (issues #368, #426 closed). Route-constant refit (RAL FFT/full direct_conv 0.68 vs 0.89 predicted) deferred. Merged in order #636, #425, #432, #431, #433, #434, #435. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/direct-conv-symmetric.md, https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ral-route-constants-fft-levers.md, https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md.
 
 [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) — 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.
+- [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 autolens_profiling#435 merged 2026-10-17 (human /prm) (issue #426 closed; 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). Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md.
+- [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 autolens_profiling#435 merged 2026-10-17 (human /prm) (issue #368 closed): 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md.
+- [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 merged 2026-10-17 (human /prm); issue #635 closed. 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: release of #636; shadow row owed in Mind autonomy_log (glance tier; human to confirm merged-unchanged). Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/direct-conv-symmetric.md. Shadow row appended 2026-10-17 (merged-unchanged, stage 1). Human decision 2026-10-17: remaining Aris levers stay research; not enough return to implement for now.
+- [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 autolens_profiling#433 merged 2026-10-17 (human /prm) (issue #429 closed; 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ral-route-constants-fft-levers.md.
 
 </details>
 
@@ -108,13 +108,13 @@ 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. 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.
+Reviewed 2026-10-17. Production over-sampling phase 1 merged 2026-10-17 (human /prm): autolens_profiling#431 (issue #430 closed, 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/imaging-production-over-sampling.md. Human decision 2026-10-17 (chat): hold off on phase 2 (A100 production over-sampling re-measure); not authorized, revisit when production imaging needs a cost picture.
 
 [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) — 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.
+- [Profile imaging pixelizations at production over-sampling](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/imaging_production_over_sampling.md) — parked: Phase 1 merged 2026-10-17 (human /prm): autolens_profiling#431 (issue #430 closed, CPU smoke 8/8). Phase 2 A100 production re-measure needs-decision (human decision 2026-10-17 (chat): undecided); requires compute authorization (#431 now merged). Findings: HST flat-1 adapt_split pin fails on main; numba HST threshold never reaches sub 4; rectangular JAX cells uncovered. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/imaging-production-over-sampling.md. Human decision 2026-10-17 (chat): phase 2 A100 re-measure held; task stays parked at phase 1 shipped (#431).
 - [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.
 
@@ -122,13 +122,13 @@ Reviewed 2026-10-17. Production over-sampling phase 1 at PR-open: autolens_profi
 
 <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.
+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 closed) merged 2026-10-17 (human /prm), pending-release: 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/delaunaynn-wide-row-overhead.md.
 
 [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.
+- [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 merged 2026-10-17 (human /prm) (issue #629 closed, pending-release): 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. Records: https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/delaunaynn-wide-row-overhead.md.
 
 </details>
 
@@ -265,9 +265,9 @@ Choose a project, dataset and model on the interactive board.
 
 ## lens
 
-<!-- pulse:instance name=lens receipt=da54dc30ff83c7682527984fa0e8a710a70a9927 outcome=ok shown=da54dc30ff83c7682527984fa0e8a710a70a9927 -->
+<!-- pulse:instance name=lens receipt=aeef27565b64520ae01faf6200286aee842ace6b outcome=ok shown=aeef27565b64520ae01faf6200286aee842ace6b -->
 
-Project `autolens_profiling`, scope `setup-catalogue`, read from [PyAutoLabs/autolens_profiling](https://github.com/PyAutoLabs/autolens_profiling) `dashboard/catalogue.json` at [`da54dc30`](https://github.com/PyAutoLabs/autolens_profiling/tree/da54dc30ff83c7682527984fa0e8a710a70a9927); producer revision `692f7e2c`; generated 2026-10-23T14:42:25Z; comparison policy `no-temporal-comparisons`. [Project dashboard](https://pyautolabs.github.io/autolens_profiling/) · [receipt](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/receipts/lens.json).
+Project `autolens_profiling`, scope `setup-catalogue`, read from [PyAutoLabs/autolens_profiling](https://github.com/PyAutoLabs/autolens_profiling) `dashboard/catalogue.json` at [`aeef2756`](https://github.com/PyAutoLabs/autolens_profiling/tree/aeef27565b64520ae01faf6200286aee842ace6b); producer revision `692f7e2c`; generated 2026-10-23T14:42:25Z; comparison policy `no-temporal-comparisons`. [Project dashboard](https://pyautolabs.github.io/autolens_profiling/) · [receipt](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/receipts/lens.json).
 
 - **Integrity:** ok
 - **Freshness:** freshness policy unspecified · evidence time unknown (evidence unknown (No common measurement wall-clock across legacy evidence; inspect each record.))
diff --git a/receipts/fit.json b/receipts/fit.json
index a30898b..b04d9fb 100644
--- a/receipts/fit.json
+++ b/receipts/fit.json
@@ -13,5 +13,5 @@
   "record_count": 31,
   "errors": [],
   "cached_from": null,
-  "refreshed_at": "2026-10-23T17:38:22Z"
+  "refreshed_at": "2026-10-23T21:50:10Z"
 }
diff --git a/receipts/lens-walltime.json b/receipts/lens-walltime.json
index add0b42..2f63446 100644
--- a/receipts/lens-walltime.json
+++ b/receipts/lens-walltime.json
@@ -13,5 +13,5 @@
   "record_count": 56,
   "errors": [],
   "cached_from": null,
-  "refreshed_at": "2026-10-23T17:38:22Z"
+  "refreshed_at": "2026-10-23T21:50:10Z"
 }
diff --git a/receipts/lens.json b/receipts/lens.json
index c9692b2..34c4255 100644
--- a/receipts/lens.json
+++ b/receipts/lens.json
@@ -3,15 +3,15 @@
   "repo": "autolens_profiling",
   "github": "PyAutoLabs/autolens_profiling",
   "branch": "main",
-  "resolved_commit": "da54dc30ff83c7682527984fa0e8a710a70a9927",
+  "resolved_commit": "aeef27565b64520ae01faf6200286aee842ace6b",
   "summary_path": "dashboard/catalogue.json",
   "source": "remote",
-  "fetched_at": "2026-10-23T17:38:00Z",
+  "fetched_at": "2026-10-23T21:50:09Z",
   "outcome": "ok",
   "schema": "profiling-summary",
   "version": 2,
   "record_count": 112,
   "errors": [],
   "cached_from": null,
-  "refreshed_at": "2026-10-23T17:38:21Z"
+  "refreshed_at": "2026-10-23T21:50:09Z"
 }
diff --git a/snapshots/lens.json b/snapshots/lens.json
index d5d62ad..324e94a 100644
--- a/snapshots/lens.json
+++ b/snapshots/lens.json
@@ -3,8 +3,8 @@
   "repo": "autolens_profiling",
   "github": "PyAutoLabs/autolens_profiling",
   "summary_path": "dashboard/catalogue.json",
-  "commit": "da54dc30ff83c7682527984fa0e8a710a70a9927",
-  "fetched_at": "2026-10-23T17:38:00Z",
+  "commit": "aeef27565b64520ae01faf6200286aee842ace6b",
+  "fetched_at": "2026-10-23T21:50:09Z",
   "summary": {
     "archive_record_count": 21002,
     "comparison_policy": {
diff --git a/tasks/delaunay_nn_cpu_imaging.md b/tasks/delaunay_nn_cpu_imaging.md
index 5d35ec5..1c312ce 100644
--- a/tasks/delaunay_nn_cpu_imaging.md
+++ b/tasks/delaunay_nn_cpu_imaging.md
@@ -58,3 +58,9 @@ Per the human decision of 2026-10-17 these stay as research:
 - 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).
+
+### Post-/prm 2026-10-17
+
+- **Merged 2026-10-17 (human /prm):** [PyAutoArray#637](https://github.com/PyAutoLabs/PyAutoArray/pull/637) (pending-release).
+- **Issues closed:** PyAutoArray#629.
+- **Mind record:** [`delaunaynn-wide-row-overhead`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/delaunaynn-wide-row-overhead.md).
diff --git a/tasks/hst_mge_cpu_pilot.md b/tasks/hst_mge_cpu_pilot.md
index f595eab..bb7d7db 100644
--- a/tasks/hst_mge_cpu_pilot.md
+++ b/tasks/hst_mge_cpu_pilot.md
@@ -211,3 +211,10 @@ Pulse-side preparation under `--auto`, leaving the independent correctness audit
 current session. No witness or profiling execution, campaign freeze or acceptance
 was authorized. This focused preparation does not advance the all-campaign
 `last_checkin` or measurement dates.
+
+### Post-/prm 2026-10-17 — qualification design merged
+
+- autolens_profiling#418 merged 2026-10-17 under human `/prm` after an independent review (verdict FINDINGS, none blocking a doc-only merge; review posted on the PR). Issue #417 closed. Mind record: [complete/2026/10/hst-mge-pilot-qualification.md](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/hst-mge-pilot-qualification.md).
+- The design (`scripts/misc/jax_compile/QUALIFICATION.md`) derives what the pilot computes, proposes an independent operator + scipy NNLS witness, lists the identities to capture and gives candidate tolerances marked unaccepted. No contradiction with the #416 runner.
+- Carried, not fixed: no background-load method proposed; the 1e-8 relative image L2 budget is likely unattainable against the packaged solver's inactive-column flux allowance, so synthetic-harness calibration must be allowed to revise tolerances; correctness-process feasibility is deferred, so the 32-process / 16 CPU-hour budget stays unfinalized. Witness placement in autolens_workspace_test with a project adapter needs the human's confirmation.
+- No collection, campaign freeze or acceptance authorized.
diff --git a/tasks/imaging_production_over_sampling.md b/tasks/imaging_production_over_sampling.md
index f118b67..2d5168f 100644
--- a/tasks/imaging_production_over_sampling.md
+++ b/tasks/imaging_production_over_sampling.md
@@ -38,6 +38,13 @@ not refresh measurements or authorize compute. Historical task text is retained.
 - **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.
 
+### Post-/prm 2026-10-17
+
+- **Merged 2026-10-17 (human /prm):** [autolens_profiling#431](https://github.com/PyAutoLabs/autolens_profiling/pull/431).
+- **Issues closed:** autolens_profiling#430.
+- **Mind record:** [`imaging-production-over-sampling`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/imaging-production-over-sampling.md).
+- Phase 2 (A100 production re-measure) still needs a human decision and compute authorization.
+
 ---
 
 # Profile imaging pixelizations at production over-sampling
diff --git a/tasks/interferometer_large_mask_aris.md b/tasks/interferometer_large_mask_aris.md
index 87a8c6f..5d6355f 100644
--- a/tasks/interferometer_large_mask_aris.md
+++ b/tasks/interferometer_large_mask_aris.md
@@ -347,3 +347,16 @@ Not worth it (measured on the 125×125 setup unless stated):
 - **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.
+
+### Post-/prm 2026-10-17
+
+- **Merged 2026-10-17 (human /prm):** [PyAutoArray#636](https://github.com/PyAutoLabs/PyAutoArray/pull/636) (pending-release), [autolens_profiling#425](https://github.com/PyAutoLabs/autolens_profiling/pull/425).
+- **Issues closed:** PyAutoArray#635.
+- **Mind record:** [`direct-conv-symmetric`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/direct-conv-symmetric.md).
+- Shadow row owed in Mind `autonomy_log` (glance tier; human to confirm merged-unchanged).
+- Remaining: release of #636.
+
+### Post-/prm addendum 2026-10-17
+
+- Glance shadow row appended to Mind `autonomy_log.md` for `direct-conv-symmetric` (PyAutoArray#636 / autolens_profiling#425): `merged-unchanged`, stage 1 (human answer 2026-10-17).
+- Human decision 2026-10-17: the remaining Aris levers (route-constant refit, FFT pruning, curvature preload) stay as research; not enough return to implement for now.
diff --git a/tasks/interferometer_nnls_memo_scattered_stream_guard.md b/tasks/interferometer_nnls_memo_scattered_stream_guard.md
index 3deed3f..9a22a72 100644
--- a/tasks/interferometer_nnls_memo_scattered_stream_guard.md
+++ b/tasks/interferometer_nnls_memo_scattered_stream_guard.md
@@ -44,6 +44,12 @@ not refresh measurements or authorize compute. Historical task text is retained.
 - **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.
 
+### Post-/prm 2026-10-17
+
+- **Merged 2026-10-17 (human /prm):** [autolens_profiling#435](https://github.com/PyAutoLabs/autolens_profiling/pull/435).
+- **Issues closed:** autolens_profiling#426.
+- **Mind record:** [`interferometer-streaming-memo-after`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md).
+
 ---
 
 # 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 3d74ed9..8f01288 100644
--- a/tasks/interferometer_streaming_scaling.md
+++ b/tasks/interferometer_streaming_scaling.md
@@ -54,6 +54,12 @@ not refresh measurements or authorize compute. Historical task text is retained.
 - **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.
 
+### Post-/prm 2026-10-17
+
+- **Merged 2026-10-17 (human /prm):** [autolens_profiling#435](https://github.com/PyAutoLabs/autolens_profiling/pull/435).
+- **Issues closed:** autolens_profiling#368.
+- **Mind record:** [`interferometer-streaming-memo-after`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/interferometer-streaming-memo-after.md).
+
 ---
 
 # 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 e34cab9..19c0a01 100644
--- a/tasks/interferometer_w_tilde_fft_size_levers.md
+++ b/tasks/interferometer_w_tilde_fft_size_levers.md
@@ -35,6 +35,12 @@ not refresh measurements or authorize compute. Historical task text is retained.
 - **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.
 
+### Post-/prm 2026-10-17
+
+- **Merged 2026-10-17 (human /prm):** [autolens_profiling#433](https://github.com/PyAutoLabs/autolens_profiling/pull/433).
+- **Issues closed:** autolens_profiling#429.
+- **Mind record:** [`ral-route-constants-fft-levers`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ral-route-constants-fft-levers.md).
+
 ---
 
 # 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 226b48c..bd2a02f 100644
--- a/tasks/point_source_image_plane_gpu_breakdown.md
+++ b/tasks/point_source_image_plane_gpu_breakdown.md
@@ -51,6 +51,12 @@ not refresh measurements or authorize compute. Historical task text is retained.
 - **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.
 
+### Post-/prm 2026-10-17
+
+- **Merged 2026-10-17 (human /prm):** [autolens_profiling#434](https://github.com/PyAutoLabs/autolens_profiling/pull/434).
+- **Issues closed:** autolens_profiling#427.
+- **Mind record:** [`point-source-image-gpu-admission`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/point-source-image-gpu-admission.md).
+
 ---
 
 # 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 ae4d1dd..f00a22a 100644
--- a/tasks/point_source_source_plane_chi_squared_speed.md
+++ b/tasks/point_source_source_plane_chi_squared_speed.md
@@ -38,6 +38,12 @@ not refresh measurements or authorize compute. Historical task text is retained.
 - **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.
 
+### Post-/prm 2026-10-17
+
+- **Merged 2026-10-17 (human /prm):** [autolens_inference#29](https://github.com/PyAutoLabs/autolens_inference/pull/29), [autolens_profiling#432](https://github.com/PyAutoLabs/autolens_profiling/pull/432).
+- **Issues closed:** autolens_inference#25, autolens_profiling#428.
+- **Mind record:** [`ps-source-gradient-admission`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ps-source-gradient-admission.md).
+
 ---
 
 # 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 2a4d758..6419851 100644
--- a/tasks/runtime_cell_single_jit_gpu_warmup.md
+++ b/tasks/runtime_cell_single_jit_gpu_warmup.md
@@ -46,6 +46,12 @@ not refresh measurements or authorize compute. Historical task text is retained.
 - 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.
 
+### Post-/prm 2026-10-17
+
+- **Merged 2026-10-17 (human /prm):** [autolens_profiling#432](https://github.com/PyAutoLabs/autolens_profiling/pull/432).
+- **Issues closed:** autolens_profiling#428.
+- **Mind record:** [`ps-source-gradient-admission`](https://github.com/PyAutoLabs/PyAutoMind/blob/main/complete/2026/10/ps-source-gradient-admission.md).
+
 ---
 
 # 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