diff --git a/campaigns.yaml b/campaigns.yaml
index 882cc6e..99c8e24 100644
--- a/campaigns.yaml
+++ b/campaigns.yaml
@@ -1,101 +1,140 @@
 version: 1
-last_checkin: '2026-10-13T17:51:40Z'
+last_checkin: '2026-10-17T11:09:26Z'
 campaigns:
 - id: setup-baseline
   title: Setup baseline collection and scientific acceptance
   status: needs-decision
-  updated: '2026-10-13'
+  updated: '2026-10-17'
   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.
+    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.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/pull/416
 - id: point-source-image-cpu
   title: Point-source image plane · CPU
   status: active
-  updated: '2026-10-13'
+  updated: '2026-10-17'
   next: '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.'
+    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.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04
 - id: point-source-image-gpu
   title: Point-source image plane · A100
   status: needs-decision
-  updated: '2026-10-13'
-  next: 'No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries
-    now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still
-    needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.'
+  updated: '2026-10-17'
+  next: 'No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have
+    release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs
+    authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.
+    Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond
+    autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no
+    new measurement.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_gpu_breakdown.md
 - id: point-source-source
   title: Point-source source plane
   status: parked
-  updated: '2026-10-13'
+  updated: '2026-10-17'
   next: 'Core remains parked; autolens_inference#21 reorganized setup evidence but added no point-source gradient
     admission measurement. #408 merged median fields into 11 runtime cells; #412 merged 2026-10-13, completing
-    headline/repeat support. No new runtime sweep; keep Blackjax parked.'
+    headline/repeat support. No new runtime sweep; keep Blackjax parked. Check-in 2026-10-17: no project
+    wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only);
+    releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.'
   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
   status: active
-  updated: '2026-10-13'
-  next: No campaign wiki/results change or new matching implementation claim found. Representative cluster
-    baseline and observable-overflow policy remain unstarted. Choose one bounded phase; inherited single-source
-    results do not qualify a representative cluster baseline.
+  updated: '2026-10-17'
+  next: 'No campaign wiki/results change or new matching implementation claim found. Representative cluster baseline
+    and observable-overflow policy remain unstarted. Choose one bounded phase; inherited single-source results
+    do not qualify a representative cluster baseline. Check-in 2026-10-17: no project wiki/results/issue change
+    since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and
+    2026.10.17.1 supply no new measurement.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/cluster_pointsolver.md
 - id: interferometer
   title: Interferometer likelihood and streaming
   status: active
-  updated: '2026-10-15'
-  next: 'Aris large-mask work (PyAutoPulse#62): #630/#631/#632 merged 2026-10-15, pending release; knowledge section and two human decisions recorded; phase-2 record at PR-open (autolens_profiling#420); L1 Cholesky reuse (PyAutoArray#633) in development, L2 direct_conv symmetry filed and unstarted. Streaming 1e8 CPU/A100 rows and the #332 memo after-measurement still need authorization; curvature preload needs the fixed-mapper science decision; FFT levers remain unstarted.'
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/interferometer_likelihood.md
+  updated: '2026-10-17'
+  next: 'Aris large-mask (PyAutoPulse#62): library PRs PyAutoArray#630/#631/#632/#634 RELEASED in 2026.10.16.1
+    (2026-10-16; 2026.10.16.2 and 2026.10.17.1 followed with no further PyAutoArray change). Phase-2 route-gate
+    record autolens_profiling#420 merged 2026-10-15; #422 (gate-60 wording historical) and #424 (lint re-pin
+    after #634) merged 2026-10-16; project main fd29902. L1 Cholesky reuse (#634) measured on the NumPy path:
+    solve+log-det 144.5→50.9 ms at 1,405 vertices, 1,268→373 ms at 2,947 (full call −22–24% / −51%). L2
+    direct_conv symmetry (PyAutoArray#635) PAUSED by the human 2026-10-16, uncommitted on worktree
+    feature/direct-conv-symmetric (base e59d5fa9), no PR: synthetic Aris-512 serial RAL witness (job 411835)
+    1.81×/1.85×/1.90× at S 1,428/2,540/5,712, computed entries bit-identical; real Aris data on the laptop 1.32×
+    (centroid order) / 1.46× (ymin order), work fraction 0.59/0.51; human decision 2026-10-16: rank by ascending
+    ymin, default gate 0.5→0.7; v3 (ymin + 0.7) present in the worktree diff, its test run, the RAL real-data
+    witness (≥1.3× curvature step), ship and write-up are owed. Streaming 1e8 CPU/A100 rows, the #332 memo
+    after-measurement, curvature preload (fixed-mapper science decision) and FFT levers unchanged.'
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09f/wiki/campaigns/interferometer_likelihood.md
 - id: linear-solver
   title: Linear-solver accuracy and cost
   status: active
-  updated: '2026-10-13'
+  updated: '2026-10-17'
   next: '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.'
+    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.'
   evidence: https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md
 - id: imaging
   title: Production imaging and likelihood breakdown
   status: active
-  updated: '2026-10-13'
-  next: 'No new production-over-sampling or post-certified measurements. #408 now adds median headline fields
-    to runtime cells; #412 merged 2026-10-13 and extends remaining headline surfaces. Production presets, memo-blind
-    harness, cumulative MGE steps and quick-update costs remain separate pending work. Scope production-preset
-    phase 1 (CPU smoke only); A100 measurement separately authorized.'
+  updated: '2026-10-17'
+  next: 'No new production-over-sampling or post-certified measurements. #408 now adds median headline fields to
+    runtime cells; #412 merged 2026-10-13 and extends remaining headline surfaces. Production presets,
+    memo-blind harness, cumulative MGE steps and quick-update costs remain separate pending work. Scope
+    production-preset phase 1 (CPU smoke only); A100 measurement separately authorized. New related library
+    evidence: PyAutoArray#624/#625 (numba Sibson kernel; DelaunayNN within 2× Delaunay on Aris 1,405) released
+    2026.10.16.1; follow-up PyAutoArray#629 (imaging DelaunayNN still ~2.7× Delaunay at sub_size 4:
+    unique_mappings 0.30 vs 0.10 s, adaptive_pixel_signals 0.16 vs 0.01 s; laptop, threads=1, load 3–5, not a
+    controlled measurement) is open and its Mind draft expects a Pulse campaign delaunay-nn-cpu that does not
+    yet exist — human decision whether to open one.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/post_certified_breakdown.md
 - id: measurement-tools
   title: Measurement reliability and profiling tools
   status: active
-  updated: '2026-10-13'
-  next: 'Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415
-    closed. No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy,
-    memo interpretation, fresh runtime repeats and other tools remain pending.'
+  updated: '2026-10-17'
+  next: 'Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415 closed.
+    No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo
+    interpretation, fresh runtime repeats and other tools remain pending. Check-in 2026-10-17:
+    autolens_profiling#418 (pilot qualification design) open since 2026-10-13 with CI green and no review; #417
+    open; no new measurements.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/pull/416
 - id: autofit
   title: PyAutoFit profiling bootstrap
   status: active
-  updated: '2026-10-14'
-  next: 'B4a merged (autofit_profiling#3, Pulse#60); B4b at PR (issue autofit_profiling#4) ports the EP /
-    graphical N3/N10/N30 baselines and the analytic parity cell (runtime + memory records, same laptop host as
-    B4a). Truth logL sums and both N30 sanity FAILs reproduce exactly; every wall is faster than the committed
-    developer baseline beyond 2x (improved). Receipt/snapshot refresh after its Pages deploy. Epic 2 not opened.'
+  updated: '2026-10-17'
+  next: '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.'
   evidence: https://github.com/PyAutoLabs/autofit_profiling/issues/4
 - id: critical-curves
   title: Critical curves and evaluation grids
   status: needs-decision
-  updated: '2026-10-13'
-  next: '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.'
+  updated: '2026-10-17'
+  next: '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.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/critical_curves.md
 - id: certified-solver
   title: Certified positive solver
   status: parked
-  updated: '2026-10-13'
-  next: 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.
+  updated: '2026-10-17'
+  next: '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.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/certified_positive_solver.md
 tasks:
 - id: setup_baseline_campaign
@@ -195,10 +234,12 @@ tasks:
   status: active
   priority: high
   path: tasks/jax_compile_probe_needs_own_cell_builder.md
-  updated: '2026-10-13'
-  next: '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.'
+  updated: '2026-10-17'
+  next: '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.'
   evidence: https://github.com/PyAutoLabs/autolens_profiling/pull/416
 - id: mass_field_flat_adoption_science_repos
   campaign: measurement-tools
@@ -228,11 +269,13 @@ tasks:
   status: active
   priority: normal
   path: tasks/autofit_profiling_bootstrap.md
-  updated: '2026-10-14'
-  next: 'B4a merged (autofit_profiling#3, Pulse#60); B4b at PR (issue autofit_profiling#4) ports the EP /
-    graphical N3/N10/N30 baselines and the analytic parity cell (runtime + memory records, same laptop host as
-    B4a). Truth logL sums and both N30 sanity FAILs reproduce exactly; every wall is faster than the committed
-    developer baseline beyond 2x (improved). Receipt/snapshot refresh after its Pages deploy. Epic 2 not opened.'
+  updated: '2026-10-17'
+  next: '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.'
   evidence: https://github.com/PyAutoLabs/autofit_profiling/issues/4
 - id: cluster_pointsolver_speed
   campaign: cluster-pointsolver
@@ -306,8 +349,14 @@ tasks:
   status: active
   priority: high
   path: tasks/interferometer_large_mask_aris.md
-  updated: '2026-10-15'
-  next: 'Knowledge written 2026-10-15 (cost structure, routing model, levers, collaborator settings) and the two human decisions recorded. #630/#631/#632 merged, pending release. Phase-2 record at PR-open (autolens_profiling#420): re-pin its table on merge. L1 Cholesky reuse (PyAutoArray#633) in development; L2 direct_conv symmetry filed, unstarted.'
+  updated: '2026-10-17'
+  next: 'Released: #630/#631/#632/#634 in 2026.10.16.1 (2026-10-16). Phase-2 record #420 merged 2026-10-15 (table
+    re-pinned at fd29902); #422/#424 merged 2026-10-16. L2 (PyAutoArray#635) paused 2026-10-16 with v3 (ymin
+    order, gate 0.7) uncommitted in worktree feature/direct-conv-symmetric; synthetic witness PASS 1.81–1.90×
+    (RAL 411835), real-data laptop 1.46× at ymin order; owed on resume: verify v3 + full suite, RAL real-data
+    witness ≥1.3× (data and private library clones staged under scratch/aris_l2_20261016), Heart check,
+    ship_library PR, write-up (results note, Knowledge section, decision record direct-conv-symmetric-gate).
+    κ=1.32 means the #632 cost-model constant for direct_conv is effectively refit when L2 ships.'
   issue: https://github.com/PyAutoLabs/PyAutoPulse/issues/62
   evidence: https://github.com/PyAutoLabs/PyAutoArray/issues/626
 - id: interferometer_w_tilde_fft_size_levers
diff --git a/dashboard.html b/dashboard.html
index ea11a48..56a21d9 100644
--- a/dashboard.html
+++ b/dashboard.html
@@ -646,7 +646,7 @@
 - PyAutoPulse: https://github.com/PyAutoLabs/PyAutoPulse
 - autolens_profiling: https://github.com/PyAutoLabs/autolens_profiling
 - autofit_profiling: https://github.com/PyAutoLabs/autofit_profiling</textarea></details><div class="orchestration-freshness" data-freshness="grey"><details data-freshness-stamp data-refreshed-at="2026-10-17T08:07:26Z"><summary><span data-freshness-label>Last updated 2026-10-17 08:07:26 UTC</span></summary><time datetime="2026-10-17T08:07:26Z">2026-10-17 08:07:26 UTC</time></details><a data-refresh-link href="https://github.com/PyAutoLabs/PyAutoPulse/actions/workflows/dashboard_refresh.yml" title="Open the dashboard refresh controls">↻ Update</a></div></div><p class="orchestration-status" role="status" aria-live="polite"></p></section>
-<nav class="board-nav" aria-label="Board sections"><a class="board-nav-card" href="#campaigns"><span class="board-nav-label">Active campaigns</span></a><a class="board-nav-card" href="#evidence"><span class="board-nav-label">Profiling Results</span></a><a class="board-nav-card" href="#decision-history"><span class="board-nav-label">Decision History</span></a></nav><main><!-- pulse-campaigns:4e132b2d2378f34b9712646cb9a497b82be62a3d7d484f8136ab2bc19cc70303 --><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.</p><span class="review-meta">Reviewed 2026-10-13</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.</p><span class="review-meta">Reviewed 2026-10-13</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pointsolver_cpu_speed_campaign_remainder.md">Point-source (single-source) CPU campaign — carried leftovers and completion evidence</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/370" aria-label="Issue for Point-source (single-source) CPU campaign — carried leftovers and completion evidence" title="Issue">↗</a></h4><p class="review-meta">ready · low</p><p>No new committed campaign measurements. Phases 1–4 remain shipped; epic closure is a human decision. Carried leftovers remain pending. RAL queue empty at review, but transfer preview timed out: cleanup folders and unpulled results unverified. Select one owed cleanup/measurement phase only after review.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04" aria-label="Evidence for Point-source image plane · CPU" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-image-cpu" data-open="campaign-point-source-image-cpu" aria-label="Tasks for Point-source image plane · CPU" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source image plane · A100</strong><details id="campaign-point-source-image-gpu" class="campaign-detail"><summary>1 task · next step</summary><p>No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.</p><span class="review-meta">Reviewed 2026-10-13</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_image_plane_gpu_breakdown.md">Point-source A100 speed-up campaign: profile and optimize with the shared breakdown</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoLens/issues/767" aria-label="Issue for Point-source A100 speed-up campaign: profile and optimize with the shared breakdown" title="Issue">↗</a></h4><p class="review-meta">needs-decision · high</p><p>No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.</p></article></details></td><td><span class="pill">needs-decision</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_gpu_breakdown.md" aria-label="Evidence for Point-source image plane · A100" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-image-gpu" data-open="campaign-point-source-image-gpu" aria-label="Tasks for Point-source image plane · A100" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source source plane</strong><details id="campaign-point-source-source" class="campaign-detail"><summary>2 tasks · next step</summary><p>Core remains parked; autolens_inference#21 reorganized setup evidence but added no point-source gradient admission measurement. #408 merged median fields into 11 runtime cells; #412 merged 2026-10-13, completing headline/repeat support. No new runtime sweep; keep Blackjax parked.</p><span class="review-meta">Reviewed 2026-10-13</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/runtime_cell_single_jit_gpu_warmup.md">Runtime cells&#x27; A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/371" aria-label="Issue for Runtime cells&#x27; A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node" title="Issue">↗</a></h4><p class="review-meta">active · low</p><p>#408 merged 2026-10-09: median headline fields now wired into 11 runtime cells, superseding the old imaging-not-wired note. #412 merged 2026-10-13 with remaining headline/repeat support. No new runtime sweep; fresh median evidence still missing.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_source_plane_chi_squared_speed.md">Point-source source-plane chi-squared speed-up campaign — remaining candidates (phases 1–2e shipped)</a></h4><p class="review-meta">blocked · low</p><p>Still blocked on a point-source gradient-sampler admission measurement. Inference #21 is setup organization, not that measurement. Keep parked.</p></article></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_source_plane.md" aria-label="Evidence for Point-source source plane" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-source" data-open="campaign-point-source-source" aria-label="Tasks for Point-source source plane" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Cluster PointSolver robustness and performance</strong><details id="campaign-cluster-pointsolver" class="campaign-detail"><summary>2 tasks · next step</summary><p>No campaign wiki/results change or new matching implementation claim found. Representative cluster baseline and observable-overflow policy remain unstarted. Choose one bounded phase; inherited single-source results do not qualify a representative cluster baseline.</p><span class="review-meta">Reviewed 2026-10-13</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/cluster_pointsolver_speed.md">Cluster PointSolver — robustness, analysis settings and performance</a></h4><p class="review-meta">ready · normal</p><p>Unissued, no claim. Pick one bounded phase per Mind epics.md: observable-overflow invalid-result policy + CI witness, or a representative-data baseline cell.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_solver_profiling_cells.md">PointSolver profiling cells: lensed quasar → cluster runtime tier → single/multi-source → multiplane</a></h4><p class="review-meta">ready · normal</p><p>Unissued, no claim. Candidate first cell: a representative-data cluster likelihood_runtime baseline (single-source preset).</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/cluster_pointsolver.md" aria-label="Evidence for Cluster PointSolver robustness and performance" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-cluster-pointsolver" data-open="campaign-cluster-pointsolver" aria-label="Tasks for Cluster PointSolver robustness and performance" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Interferometer likelihood and streaming</strong><details id="campaign-interferometer" class="campaign-detail"><summary>5 tasks · next step</summary><p>Aris large-mask work (PyAutoPulse#62): #630/#631/#632 merged 2026-10-15, pending release; knowledge section and two human decisions recorded; phase-2 record at PR-open (autolens_profiling#420); L1 Cholesky reuse (PyAutoArray#633) in development, L2 direct_conv symmetry filed and unstarted. Streaming 1e8 CPU/A100 rows and the #332 memo after-measurement still need authorization; curvature preload needs the fixed-mapper science decision; FFT levers remain unstarted.</p><span class="review-meta">Reviewed 2026-10-15</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_fixed_mapper_curvature_preload.md">Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`</a></h4><p class="review-meta">ready · normal</p><p>Phase 1 Mind draft (2026-10-04) still unstarted; run it through start_dev. Human science call first - no production SLaM stage holds the mapper fixed today.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_nnls_memo_scattered_stream_guard.md">fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoArray/issues/613" aria-label="Issue for fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)" title="Issue">↗</a></h4><p class="review-meta">active · medium</p><p>Library half shipped: PyAutoArray#615 (closes #613) merged 2026-10-06, in 2026.10.7.1. Local solver-only witness (n=576): iid memo on/off 1.49x → 1.17x; walk 0.18x unchanged. Owed: re-run the autolens_profiling#332 ALMA Delaunay 2.17x after-measurement (needs compute authorization).</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_streaming_scaling.md">Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/368" aria-label="Issue for Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities" title="Issue">↗</a></h4><p class="review-meta">active · high</p><p>Phase 1 CPU cells + results/streaming_scaling rows merged (autolens_profiling#375, 2026-10-04); wiki/campaigns/interferometer_streaming.md. Optional: 1e8 CPU row and A100 rows (compute authorization). Library candidate unfiled: chunk transformer.image_from in apply_sparse_operator (memory).</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_large_mask_aris.md">Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoPulse/issues/62" aria-label="Issue for Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model" title="Issue">↗</a></h4><p class="review-meta">active · high</p><p>Knowledge written 2026-10-15 (cost structure, routing model, levers, collaborator settings) and the two human decisions recorded. #630/#631/#632 merged, pending release. Phase-2 record at PR-open (autolens_profiling#420): re-pin its table on merge. L1 Cholesky reuse (PyAutoArray#633) in development; L2 direct_conv symmetry filed, unstarted.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_w_tilde_fft_size_levers.md">Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space pixel scale on the A100</a></h4><p class="review-meta">ready · normal</p><p>Unstarted (no PR/issue/branch/results, 2026-10-04). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/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.</p><span class="review-meta">Reviewed 2026-10-13</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md" aria-label="Evidence for Linear-solver accuracy and cost" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-linear-solver" data-open="campaign-linear-solver" aria-label="Tasks for Linear-solver accuracy and cost" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Production imaging and likelihood breakdown</strong><details id="campaign-imaging" class="campaign-detail"><summary>3 tasks · next step</summary><p>No new production-over-sampling or post-certified measurements. #408 now adds median headline fields to runtime cells; #412 merged 2026-10-13 and extends remaining headline surfaces. Production presets, memo-blind harness, cumulative MGE steps and quick-update costs remain separate pending work. Scope production-preset phase 1 (CPU smoke only); A100 measurement separately authorized.</p><span class="review-meta">Reviewed 2026-10-13</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/imaging_production_over_sampling.md">Profile imaging pixelizations at production over-sampling</a></h4><p class="review-meta">ready · normal</p><p>Phase 1 (workspace-only): --instrument euclid|hst presets for the JAX Delaunay runtime/breakdown cells, keeping flat-1 selectable; CPU smoke only. A100 re-measure is a separate compute-authorized phase.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/post_certified_solver_likelihood_breakdown.md">Assess remaining likelihood bottlenecks after certified solver integration on CPU and GPUs</a></h4><p class="review-meta">needs-slicing · normal</p><p>Blocked-by is stale: resolved 2026-09-27 (PyAutoArray#567 in 2026.9.26.1). Unissued; scope one bounded phase from the full task contract.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/quick_update_plotting_cost.md">Quick-update plotting cost — minutes per update, and it is not JAX compile</a></h4><p class="review-meta">ready · medium</p><p>Unchanged; targets PyAutoLens/PyAutoGalaxy (PyAutoLens#680 closed 2026-07-31, no follow-up issue). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/post_certified_breakdown.md" aria-label="Evidence for Production imaging and likelihood breakdown" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-imaging" data-open="campaign-imaging" aria-label="Tasks for Production imaging and likelihood breakdown" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Measurement reliability and profiling tools</strong><details id="campaign-measurement-tools" class="campaign-detail"><summary>8 tasks · next step</summary><p>Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415 closed. No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo interpretation, fresh runtime repeats and other tools remain pending.</p><span class="review-meta">Reviewed 2026-10-13</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.</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 merged (autofit_profiling#3, Pulse#60); B4b at PR (issue autofit_profiling#4) ports the EP / graphical N3/N10/N30 baselines and the analytic parity cell (runtime + memory records, same laptop host as B4a). Truth logL sums and both N30 sanity FAILs reproduce exactly; every wall is faster than the committed developer baseline beyond 2x (improved). Receipt/snapshot refresh after its Pages deploy. Epic 2 not opened.</p><span class="review-meta">Reviewed 2026-10-14</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 merged (autofit_profiling#3, Pulse#60); B4b at PR (issue autofit_profiling#4) ports the EP / graphical N3/N10/N30 baselines and the analytic parity cell (runtime + memory records, same laptop host as B4a). Truth logL sums and both N30 sanity FAILs reproduce exactly; every wall is faster than the committed developer baseline beyond 2x (improved). Receipt/snapshot refresh after its Pages deploy. Epic 2 not opened.</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.</p><span class="review-meta">Reviewed 2026-10-13</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.</p><span class="review-meta">Reviewed 2026-10-13</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/certified_positive_solver.md" aria-label="Evidence for Certified positive solver" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-certified-solver" data-open="campaign-certified-solver" aria-label="Tasks for Certified positive solver" title="Open tasks">☷</a></div></td></tr></tbody></table></div><p class="review-meta">Open means tracked, not necessarily running. <a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/campaigns.yaml">Full ledger ↗</a></p></div></details><details class="board-section"><summary><h2 id="evidence">Profiling Results</h2></summary><div class="board-section-body"><section class="setup-browser" data-setup-browser="lens" aria-label="autolens_profiling setups"><script type="application/json" class="setup-payload">{"catalogue":{"archive_record_count":20709,"comparison_policy":{"id":"no-temporal-comparisons"},"comparisons":[],"coverage":{"excluded":[],"expected":{"baseline_cells":29,"cells":101,"planned_slots":435},"observed":{"records":112,"selected":98,"setups":540}},"evidence_shards":[{"axes":["breakdown"],"axis_devices":{"breakdown":["cpu"]},"devices":["cpu"],"path":"catalogue/shards/eb112c2ba02f1d8956d3.json","precisions":["precision not recorded"],"records":8,"setup_id":"cluster/image_plane/unspecified/07bd0ee5a96fb26cb66f","sha256":"22303965ec4263b38044fcd99edfba7522035594c13f9ef0e01b13e8e80c612a"},{"axes":["breakdown"],"axis_devices":{"breakdown":["cpu"]},"devices":["cpu"],"path":"catalogue/shards/7b1b0860730056355cb6.json","precisions":["precision not 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no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"point_source_source","id":"baseline/point_source_source/source_plane/unspecified","instrument":null,"label":"point_source_source/source_plane/unspecified · baseline pending","model":"source_plane","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}},{"configuration":{"image_pixels_masked":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"image_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"n_vis":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"oversampling":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"dimensionless","value":null},"preloads":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"cluster","id":"baseline/cluster/image_plane/unspecified","instrument":null,"label":"cluster/image_plane/unspecified · baseline pending","model":"image_plane","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}},{"configuration":{"image_pixels_masked":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"image_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"n_vis":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"oversampling":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"dimensionless","value":null},"preloads":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"multi_dataset","id":"baseline/multi_dataset/delaunay/unspecified","instrument":null,"label":"multi_dataset/delaunay/unspecified · baseline pending","model":"delaunay","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}}],"static_memory_estimates":[{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_delaunay.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay","per_replica":{"unit":"MiB","value":921.913},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":62},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_delaunay_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay","per_replica":{"unit":"MiB","value":131.505},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_mge.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"mge","per_replica":{"unit":"MiB","value":16.411},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_mge_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"mge","per_replica":{"unit":"MiB","value":16.411},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_pixelization.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"rectangular","per_replica":{"unit":"MiB","value":931.004},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":62},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_pixelization_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"rectangular","per_replica":{"unit":"MiB","value":94.906},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/delaunay_nn/vmap_probe_delaunay_nn_batch64_cufft_failed.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay_nn","per_replica":{"unit":"MiB","value":770.97},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"point_source_source","evidence":{"fragment":null,"path":"results/runtime/point_source_source/source_plane_solved/vmap_probe_source_plane_solved.json"},"instrument":"simple","kind":"xla_static_buffer_estimate","model":"source_plane_solved","per_replica":{"unit":"MiB","value":0.0},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64}],"unbound_findings":[{"description":"The conversion maps every magnitude at or above 0.999 to q=0.000500250125062532. NumPy reaches the thin valid annulus below one but its constructor guard blocks the beyond-unit region; JAX arrays and tracers bypass that concrete-scalar guard.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.ell_comps.magnitude-saturation","path":"results/hazards/hazards_index.json"},"id":"component.ell_comps.magnitude-saturation","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"ell_comps","title":"ell_comps magnitude saturates at 0.999"},{"description":"A second, load-bearing saturation prevents the q=1 divisions in the elliptical Isothermal deflection from becoming 0/0. Its affected ell_comps disk is too small for prior-sampling discovery.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.isothermal.near-spherical-saturation","path":"results/hazards/hazards_index.json"},"id":"component.isothermal.near-spherical-saturation","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"isothermal","title":"Isothermal caps near-spherical axis ratios at 0.99999"},{"description":"`_wofz_rational` selects one of three rational branches with `xp.where` on |z|^2 and Im(z)^2. The value is continuous to ~1e-6 across a seam but the derivative is not, so `jax.grad` of any MGE deflection carries a kink wherever a grid point crosses a boundary. Autodiff derivative error just above the r2=2.5 seam reaches 7.71e-06 relative, against 1.3e-14 for the SciPy path the numpy backend uses.","discovery":{"models":[],"reason":"Shared MGE mass-profile deflection component study, not specifically the imaging MGE light-model likelihood. Consult the recorded component and revision before applying this finding.","shared":true},"evidence":{"fragment":"/findings/component.mge.faddeeva-seam-gradient","path":"results/hazards/hazards_index.json"},"id":"component.mge.faddeeva-seam-gradient","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"mge","title":"The JAX Faddeeva routine's region seams make its derivative discontinuous"},{"description":"The elliptical Faddeeva form is singular at q = 1, so the MGE machinery clamps the axis ratio to 0.9999. The numpy path escapes through an exact radial branch; the JAX path cannot, and every *Sph MGE-routed profile is biased by up to 6.18e-05 relative with a spurious cross-axis deflection of 0.000139 arcsec.","discovery":{"models":[],"reason":"Shared MGE mass-profile deflection component study, not specifically the imaging MGE light-model likelihood. Consult the recorded component and revision before applying this finding.","shared":true},"evidence":{"fragment":"/findings/component.mge.spherical-clamp-bias","path":"results/hazards/hazards_index.json"},"id":"component.mge.spherical-clamp-bias","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"mge","title":"Spherical MGE profiles are evaluated as ellipses at the q = 0.9999 clamp on JAX"},{"description":"The JAX backend replaces the exact SciPy hyp2f1 angular factor with a fixed 20-term omega series. Relative deflection error grows continuously to 0.297 over the measured factor curve.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.power-law.series-vs-hyp2f1-divergence","path":"results/hazards/hazards_index.json"},"id":"component.power-law.series-vs-hyp2f1-divergence","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"power_law","title":"PowerLaw's 20-term JAX series diverges from hyp2f1 at high ellipticity"},{"description":"The spherical radial coordinate returns the correct finite value zero at the profile centre, but JAX differentiation of sqrt(y^2+x^2) returns a non-finite gradient there.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.spherical-geometry.radial-sqrt-gradient-at-zero","path":"results/hazards/hazards_index.json"},"id":"component.spherical-geometry.radial-sqrt-gradient-at-zero","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"spherical_geometry","title":"Radial sqrt has a non-finite gradient at r=0"},{"description":"Using only the diagonal entries the policy actually touches, the absolute floor reaches 4.457e-05 of their scale. A reference-calibrated scale-aware counterfactual holds its fraction fixed with maximum relative output error 3.395e-05.","discovery":{"models":[{"dataset":"imaging","model":"rectangular"}],"reason":"The reproducer is the imaging rectangular-source likelihood fixture. This is related model evidence; it does not establish applicability to other configurations or measured versions.","shared":false},"evidence":{"fragment":"/findings/likelihood.imaging-pixelization.absolute-conditioning-floors","path":"results/hazards/hazards_index.json"},"id":"likelihood.imaging-pixelization.absolute-conditioning-floors","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"imaging_pixelization","title":"Absolute inversion floors move with dataset scale"},{"description":"The NumPy full-likelihood scan changes active support at 5 bounded Einstein-radius location(s). The report measures explicit epsilon-neighbourhood mass, not exact-kink prior mass.","discovery":{"models":[{"dataset":"imaging","model":"rectangular"}],"reason":"The reproducer is the imaging rectangular-source likelihood fixture. This is related model evidence; it does not establish applicability to other configurations or measured versions.","shared":false},"evidence":{"fragment":"/findings/likelihood.imaging-pixelization.nnls-active-set-kinks","path":"results/hazards/hazards_index.json"},"id":"likelihood.imaging-pixelization.nnls-active-set-kinks","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"imaging_pixelization","title":"NNLS support changes create likelihood kinks"},{"description":"2 argmax-switch location(s) found in [0.0, 3.0] (4 truth positions, 0 of them where max_sep is already over threshold — i.e. a second, threshold-independent gradient kink in the ACTIVE penalty region, from max_sep itself being a max of several smooth per-position functions).","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.argmax-switch","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.argmax-switch","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"Which position-pair sets max_sep switches discontinuously across the sweep"},{"description":"Across 40/401 grid points with max_sep \u003c threshold, penalty value and gradient are exactly zero (max |value|=0.000e+00, max |grad|=0.000e+00) — a gradient-search lane deep inside the fence gets NO signal at all from the positions term, by construction.","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.interior-plateau","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.interior-plateau","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"PositionsLH penalty is an exact-zero, zero-gradient plateau inside threshold"},{"description":"Sweeping einstein_radius in [0.0, 3.0] (401 pts, threshold=0.3, factor=1e+08), a tight (+/-1e-6) probe at each of 2 max_sep==threshold crossing(s) shows the penalty VALUE is continuous there (max gap 2.006e+02) while its jax-autodiff GRADIENT jumps by up to 2.006e+08 — zero on the interior side, nonzero on the exterior side. A gradient optimizer sees a step-function force switching on at the fence, not a smooth potential.","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.threshold-hinge","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.threshold-hinge","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"PositionsLH penalty has a C0 hinge at the threshold crossing"},{"description":"The configured 1.0e-03 diagonal add ranges from dominant to negligible as synthetic curvature scale changes. Detection needs no dataset; judging scientific relevance does.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/matrix.curvature.absolute-diagonal-floor","path":"results/hazards/hazards_index.json"},"id":"matrix.curvature.absolute-diagonal-floor","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"matrix","subject_name":"curvature_matrix","title":"Absolute curvature-diagonal floor is scale dependent"}],"valid_until":null,"version":2},"instance":"lens","label":"PyAutoLens","repo":"https://github.com/PyAutoLabs/autolens_profiling","commit":"fd29902bd09ff5defcab5e9007a547423048e8aa","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autolens_profiling/fd29902bd09ff5defcab5e9007a547423048e8aa/dashboard/","local":false,"remote_shards":true}</script><p data-id="load-status" role="status" aria-live="polite"></p><div data-id="navigation"></div><section data-id="results" aria-label="Selected setup" hidden></section><noscript><details><summary>Original setup evidence (JavaScript disabled)</summary><ul><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_breakdown_v2026.7.23.1.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_breakdown_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_breakdown_v2026.7.6.649.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_breakdown_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_hpc_ral_cpu_fp64.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_local_cpu_fp64.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/source_plane_breakdown_v2026.7.23.1.json">cluster / source_plane / unspecified — results/breakdown/cluster/source_plane_breakdown_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/source_plane_breakdown_v2026.7.6.649.json">cluster / source_plane / unspecified — results/breakdown/cluster/source_plane_breakdown_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/datacube/delaunay_hpc_a100_fp64.json">datacube / delaunay / alma_high — results/breakdown/datacube/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/datacube/delaunay_hpc_a100_mp.json">datacube / delaunay / alma_high — results/breakdown/datacube/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_autotune4.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_autotune4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1_control.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_recon_split.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_mp.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_mp_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_breakdown_hst_v2026.8.17.1.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly_control.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk1024.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk2048.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk2048.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk4096.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk512.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk512.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_control.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_recon_split.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_vmap64.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_vmap64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_numba_breakdown_euclid_v2026.8.17.1.json">imaging / delaunay / euclid — results/breakdown/imaging/delaunay_numba_breakdown_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_numba_breakdown_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_numba_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2_control.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3_control.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n3000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n3000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_nn_hpc_a100_fp64_fixed_light.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_delaunay_nn_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_hpc_a100_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_t1.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_tall.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_hpc_a100_fp64_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_t1.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_tall.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n1225_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n1225_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n484_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n484_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_hpc_a100_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_t1.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_tall.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library_vmap2.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library_vmap2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_mp_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n1225_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n1225_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n484_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n484_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3025_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3025_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n12000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n3000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n5000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_nn_hpc_a100_fp64_matrix_free.json">imaging / delaunay_nn / hst — results/breakdown/imaging/matrix_free_delaunay_nn_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n12000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n3000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n5000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.5.29.4.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.7.6.649.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.8.17.1.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_hpc_a100_fp64.json">imaging / mge / hst — results/breakdown/imaging/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_recon_split.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_mp.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_mp_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_euclid_v2026.8.17.1.json">imaging / rectangular / euclid — results/breakdown/imaging/pixelization_numba_breakdown_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_mp.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64_sparse.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_mp.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_mp.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay / sma — results/breakdown/interferometer/delaunay_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_constant_split.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_mp.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay / sma — results/breakdown/interferometer/delaunay_numba_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_levers.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_levers.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r4.25.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r4.25.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r6.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r6.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_jax_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_jax_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_jax_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_jax_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_reference_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_reference_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_reference_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_reference_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_source_loop_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_source_loop_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_symmetric_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_symmetric_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_two_stage_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_two_stage_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/delaunay_hpc_a100_mp.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_fp64.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_fp64_sparse.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_mp.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/pixelization_hpc_a100_fp64.json">interferometer / rectangular / jvla — results/breakdown/interferometer/jvla/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/pixelization_hpc_a100_mp.json">interferometer / rectangular / jvla — results/breakdown/interferometer/jvla/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_high_v2026.8.17.1.json">interferometer / mge / alma_high — results/breakdown/interferometer/mge_breakdown_alma_high_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1.json">interferometer / mge / alma — results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1_sparse.json">interferometer / mge / alma — results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1.json">interferometer / mge / sma — results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1_sparse.json">interferometer / mge / sma — results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_dft_breakdown_sma_v2026.8.17.1.json">interferometer / mge / sma — results/breakdown/interferometer/mge_dft_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_hpc_a100_fp64.json">interferometer / mge / alma — results/breakdown/interferometer/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_hpc_a100_fp64_sparse.json">interferometer / mge / alma — results/breakdown/interferometer/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n1000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n1000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n2500.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n4000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n4000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n1000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n1000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n2500.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n4000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n4000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n1024.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n2500.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n4096.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n1024.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n2500.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n4096.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular / sma — results/breakdown/interferometer/pixelization_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_mp.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular / sma — results/breakdown/interferometer/pixelization_numba_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_levers.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_levers.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r4.25.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r4.25.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r6.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r6.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_jax_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_jax_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_jax_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_jax_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_reference_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_reference_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_reference_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_reference_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/delaunay_hpc_a100_fp64.json">interferometer / delaunay / sdp81 — results/breakdown/interferometer/sdp81/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / sdp81 — results/breakdown/interferometer/sdp81/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64.json">interferometer / mge / sdp81 — results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64_sparse.json">interferometer / mge / sdp81 — results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/pixelization_hpc_a100_fp64.json">interferometer / rectangular / sdp81 — results/breakdown/interferometer/sdp81/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / sdp81 — results/breakdown/interferometer/sdp81/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_mp.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter_main_control.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter_main_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_mp.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64_p4.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64_p4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_local_cpu_fp64.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_hpc_a100_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_hpc_ral_cpu_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_local_cpu_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_constant_split.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64_sparse.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp_sparse.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly_control.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_autotune4.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_autotune4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_control.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_sparse.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_euclid_v2026.8.17.1.json">imaging / delaunay / euclid — results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/comparison.json">imaging / other / ao — results/runtime/imaging/mge/ao/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/comparison.json">imaging / other / ao — results/runtime/imaging/mge/ao/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/mge_local_cpu_fp64.json">imaging / mge / ao — results/runtime/imaging/mge/ao/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/mge_local_cpu_mp.json">imaging / mge / ao — results/runtime/imaging/mge/ao/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_fp64.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_fp64_sparse.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_mp.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_mp_sparse.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_fp64.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_fp64_sparse.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_mp.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_mp_sparse.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/mge_likelihood_summary_hst_v2026.8.17.1.json">imaging / mge / hst — results/runtime/imaging/mge/mge_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge_mass_jax/mge_mass_jax_likelihood_summary_hst_v2026.8.17.1.json">imaging / mge / hst — results/runtime/imaging/mge_mass_jax/mge_mass_jax_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64_sparse.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp_sparse.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_recon_split.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_euclid_v2026.8.17.1.json">imaging / rectangular / euclid — results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba_mge_mass/pixelization_numba_mge_mass_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization_numba_mge_mass/pixelization_numba_mge_mass_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/alma/comparison.json">interferometer / delaunay / alma — results/runtime/interferometer/delaunay/alma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/alma/delaunay_local_cpu_mp.json">interferometer / delaunay / alma — results/runtime/interferometer/delaunay/alma/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/comparison.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/comparison.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_fp64.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_mp.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/comparison.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/comparison.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/mge_local_cpu_fp64.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/mge_local_cpu_mp.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/comparison.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/comparison.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_fp64.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_mp.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_image/image_plane_solved/image_plane_solved_summary_v2026.7.23.1.json">point_source_image / image_plane / unspecified — results/runtime/point_source_image/image_plane_solved/image_plane_solved_summary_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/comparison.json">point_source_source / other / unspecified — results/runtime/point_source_source/source_plane_solved/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/comparison.json">point_source_source / other / unspecified — results/runtime/point_source_source/source_plane_solved/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_a100_fp64.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_ral_cpu_fp64.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_summary_v2026.7.23.1.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_summary_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/export_probe.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/export_probe.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/export_probe.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/export_probe.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">experiments / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">experiments / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/trace_profile.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/trace_profile.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json">imaging / 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scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json</a></li></ul></details></noscript></section><section class="setup-browser" data-setup-browser="fit" aria-label="autofit_profiling setups"><script type="application/json" 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none"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/sampler_bookkeeping","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"sampler_bookkeeping","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/bare_sampler/breakdown/sampler_bookkeeping","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/other","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"other","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/bare_sampler/breakdown/other","reason":"the cell's committed median row"}],"hazards":[],"recommendations":[],"comparisons":[],"limitations":["Laptop CPU only, pinned to one host (DESKTOP-H143S82, WSL2) so Pulse's same-host rule pairs every record; the epic's release-sweep pin (euclid-ral-gpu-2, an A100 node) does not fit CPU Dynesty runs. Cross-machine reproduction on RAL is a later campaign.","Wall times vary up to ±40 % between sessions on this host, so breakdown shares are the comparable quantity, not seconds; the EP / graphical / parity rows are single fresh-process fits (repetitions 1).","Component times are exclusive monkey-patched wall-clock intervals; 'other' is wall minus the instrumented total, not a measured phase.","The EP / graphical rows carry a producer-side reproduction note against the committed autofit_workspace_developer baselines (runtime-drift-2x-1ms, in each row's `reference`); it is not a Pulse comparison, because those baselines recorded no host and no record pair exists.","The N30 EP and graphical rows reproduce the committed sanity FAIL; their wall and memory are real measurements of a fit whose parameter recovery fails, not of a correct fit.","Every record is unreviewed: the producer declares measurements, the Brain's profiling conductor judges them; nothing here is a verdict.","No temporal comparisons: one baseline per setup."]},"instance":"fit","label":"autofit","repo":"https://github.com/PyAutoLabs/autofit_profiling","commit":"58e56b9646da2181061b2e8d7907d6db2542ee07","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autofit_profiling/58e56b9646da2181061b2e8d7907d6db2542ee07/dashboard/","local":false,"remote_shards":true}</script><p data-id="load-status" role="status" aria-live="polite"></p><div data-id="navigation"></div><section data-id="results" aria-label="Selected setup" hidden></section><noscript><details><summary>Original setup evidence (JavaScript disabled)</summary><ul></ul></details></noscript></section><!-- decision-history:20f0256fc1d56eda82a910f81ddb71d393e76eeaf26e486d9c836b00282b99f9 --></div></details><details class="board-section"><summary><h2>Decision History</h2></summary><div class="board-section-body"><section id="decision-history"><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/interferometer-route-gate-cost-model.md" target="_blank" rel="noopener" title="Opens in a new tab">Route interferometer curvature by a calibrated two-constant cost model</a><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/adaptsplit-zero-weight-delaunay-only.md" target="_blank" rel="noopener" title="Opens in a new tab">Exclude zero-weight mappings from AdaptSplit pixel signals on Delaunay meshes only</a><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md" target="_blank" rel="noopener" title="Opens in a new tab">Keep Jacobi PDIP as the Mapper default and raw+polish as the MGE-only default</a></section></div></details><ul class="boards"><li class="muted" style="padding:.3rem 0">Boards:</li><li><a data-organ="organism" href="https://pyautolabs.github.io/PyAutoScientist/">Scientist</a></li><li><a data-organ="brain" href="https://pyautolabs.github.io/PyAutoBrain/">Brain</a></li><li><a data-organ="mind" href="https://pyautolabs.github.io/PyAutoMind/">Mind</a></li><li><a data-organ="cortex" href="https://pyautolabs.github.io/PyAutoCortex/">Cortex</a></li><li><a data-organ="memory" href="https://pyautolabs.github.io/PyAutoMemory/">Memory</a></li><li><a data-organ="eyes" href="https://pyautolabs.github.io/PyAutoEyes/">Eyes</a></li><li><a data-organ="ears" href="https://pyautolabs.github.io/PyAutoEars/">Ears</a></li><li><a data-organ="heart" href="https://pyautolabs.github.io/PyAutoHeart/">Heart</a></li><li><a data-organ="hands" href="https://pyautolabs.github.io/PyAutoHands/">Hands</a></li><li><a data-organ="insight" href="https://pyautolabs.github.io/PyAutoInsight/">Insight</a></li><li><a data-organ="dna" href="https://pyautolabs.github.io/PyAutoDNA/">DNA</a></li><li><a data-organ="nerves" href="https://pyautolabs.github.io/PyAutoNerves/">Nerves</a></li><li><a data-organ="broca" href="https://pyautolabs.github.io/PyAutoBroca/">Broca</a></li><li><a data-organ="gut" href="https://pyautolabs.github.io/PyAutoGut/">Gut</a></li></ul></main><script>const MAX_PROMPT_CHARS = 50000;
+<nav class="board-nav" aria-label="Board sections"><a class="board-nav-card" href="#campaigns"><span class="board-nav-label">Active campaigns</span></a><a class="board-nav-card" href="#evidence"><span class="board-nav-label">Profiling Results</span></a><a class="board-nav-card" href="#decision-history"><span class="board-nav-label">Decision History</span></a></nav><main><!-- pulse-campaigns:19faf009965e3df26e5113e7c9ff26b4dcb4a5df3d156bf43e8838997abca73d --><details class="board-section"><summary><h2 id="campaigns">Active campaigns</h2></summary><div class="board-section-body"><div class="campaign-table"><table><thead><tr><th>Campaign</th><th>Status</th><th>Links</th></tr></thead><tbody><tr><td><strong>Setup baseline collection and scientific acceptance</strong><details id="campaign-setup-baseline" class="campaign-detail"><summary>2 tasks · next step</summary><p>Review the Pulse collection choices (candidate input hashes and 31-worker resource plan verified without execution) alongside project correctness audit #417. Resolve data origin/suitability, named host/stack/full settings, witness/tolerances and background-load method. No freeze, compute or acceptance authorized. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/setup_baseline_campaign.md">Freeze the setup baseline specification, then collect and review evidence</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/384" aria-label="Issue for Freeze the setup baseline specification, then collect and review evidence" title="Issue">↗</a></h4><p class="review-meta">needs-decision · high</p><p>HST/MGE raw fixed-parameter CPU runner merged in #416; Pulse handoff #55 merged and Mind implementation closed. Resolve exact production host/stack/data, full settings, independent correctness witness/tolerances and background-load method. No collection, campaign freeze or acceptance authorized; global compile dispatch remains unavailable.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/hst_mge_cpu_pilot.md">Prepare and review the HST/MGE fp64 CPU pilot</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/415" aria-label="Issue for Prepare and review the HST/MGE fp64 CPU pilot" title="Issue">↗</a></h4><p class="review-meta">needs-decision · high</p><p>Review the Pulse collection choices (candidate input hashes and 31-worker resource plan verified without execution) alongside project correctness audit #417. Resolve data origin/suitability, named host/stack/full settings, witness/tolerances and background-load method. No freeze, compute or acceptance authorized.</p></article></details></td><td><span class="pill">needs-decision</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/pull/416" aria-label="Evidence for Setup baseline collection and scientific acceptance" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-setup-baseline" data-open="campaign-setup-baseline" aria-label="Tasks for Setup baseline collection and scientific acceptance" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source image plane · CPU</strong><details id="campaign-point-source-image-cpu" class="campaign-detail"><summary>1 task · next step</summary><p>No new committed campaign measurements. Phases 1–4 remain shipped; epic closure is a human decision. Carried leftovers remain pending. RAL queue empty at review, but transfer preview timed out: cleanup folders and unpulled results unverified. Select one owed cleanup/measurement phase only after review. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement. RAL queue holds only euclid_dr1 jobs; cleanup folders and unpulled results still unverified.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pointsolver_cpu_speed_campaign_remainder.md">Point-source (single-source) CPU campaign — carried leftovers and completion evidence</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/370" aria-label="Issue for Point-source (single-source) CPU campaign — carried leftovers and completion evidence" title="Issue">↗</a></h4><p class="review-meta">ready · low</p><p>No new committed campaign measurements. Phases 1–4 remain shipped; epic closure is a human decision. Carried leftovers remain pending. RAL queue empty at review, but transfer preview timed out: cleanup folders and unpulled results unverified. Select one owed cleanup/measurement phase only after review.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04" aria-label="Evidence for Point-source image plane · CPU" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-image-cpu" data-open="campaign-point-source-image-cpu" aria-label="Tasks for Point-source image plane · CPU" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source image plane · A100</strong><details id="campaign-point-source-image-gpu" class="campaign-detail"><summary>1 task · next step</summary><p>No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_image_plane_gpu_breakdown.md">Point-source A100 speed-up campaign: profile and optimize with the shared breakdown</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoLens/issues/767" aria-label="Issue for Point-source A100 speed-up campaign: profile and optimize with the shared breakdown" title="Issue">↗</a></h4><p class="review-meta">needs-decision · high</p><p>No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.</p></article></details></td><td><span class="pill">needs-decision</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_gpu_breakdown.md" aria-label="Evidence for Point-source image plane · A100" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-image-gpu" data-open="campaign-point-source-image-gpu" aria-label="Tasks for Point-source image plane · A100" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Point-source source plane</strong><details id="campaign-point-source-source" class="campaign-detail"><summary>2 tasks · next step</summary><p>Core remains parked; autolens_inference#21 reorganized setup evidence but added no point-source gradient admission measurement. #408 merged median fields into 11 runtime cells; #412 merged 2026-10-13, completing headline/repeat support. No new runtime sweep; keep Blackjax parked. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/runtime_cell_single_jit_gpu_warmup.md">Runtime cells&#x27; A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/371" aria-label="Issue for Runtime cells&#x27; A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node" title="Issue">↗</a></h4><p class="review-meta">active · low</p><p>#408 merged 2026-10-09: median headline fields now wired into 11 runtime cells, superseding the old imaging-not-wired note. #412 merged 2026-10-13 with remaining headline/repeat support. No new runtime sweep; fresh median evidence still missing.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_source_plane_chi_squared_speed.md">Point-source source-plane chi-squared speed-up campaign — remaining candidates (phases 1–2e shipped)</a></h4><p class="review-meta">blocked · low</p><p>Still blocked on a point-source gradient-sampler admission measurement. Inference #21 is setup organization, not that measurement. Keep parked.</p></article></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_source_plane.md" aria-label="Evidence for Point-source source plane" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-point-source-source" data-open="campaign-point-source-source" aria-label="Tasks for Point-source source plane" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Cluster PointSolver robustness and performance</strong><details id="campaign-cluster-pointsolver" class="campaign-detail"><summary>2 tasks · next step</summary><p>No campaign wiki/results change or new matching implementation claim found. Representative cluster baseline and observable-overflow policy remain unstarted. Choose one bounded phase; inherited single-source results do not qualify a representative cluster baseline. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/cluster_pointsolver_speed.md">Cluster PointSolver — robustness, analysis settings and performance</a></h4><p class="review-meta">ready · normal</p><p>Unissued, no claim. Pick one bounded phase per Mind epics.md: observable-overflow invalid-result policy + CI witness, or a representative-data baseline cell.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_solver_profiling_cells.md">PointSolver profiling cells: lensed quasar → cluster runtime tier → single/multi-source → multiplane</a></h4><p class="review-meta">ready · normal</p><p>Unissued, no claim. Candidate first cell: a representative-data cluster likelihood_runtime baseline (single-source preset).</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/cluster_pointsolver.md" aria-label="Evidence for Cluster PointSolver robustness and performance" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-cluster-pointsolver" data-open="campaign-cluster-pointsolver" aria-label="Tasks for Cluster PointSolver robustness and performance" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Interferometer likelihood and streaming</strong><details id="campaign-interferometer" class="campaign-detail"><summary>5 tasks · next step</summary><p>Aris large-mask (PyAutoPulse#62): library PRs PyAutoArray#630/#631/#632/#634 RELEASED in 2026.10.16.1 (2026-10-16; 2026.10.16.2 and 2026.10.17.1 followed with no further PyAutoArray change). Phase-2 route-gate record autolens_profiling#420 merged 2026-10-15; #422 (gate-60 wording historical) and #424 (lint re-pin after #634) merged 2026-10-16; project main fd29902. L1 Cholesky reuse (#634) measured on the NumPy path: solve+log-det 144.5→50.9 ms at 1,405 vertices, 1,268→373 ms at 2,947 (full call −22–24% / −51%). L2 direct_conv symmetry (PyAutoArray#635) PAUSED by the human 2026-10-16, uncommitted on worktree feature/direct-conv-symmetric (base e59d5fa9), no PR: synthetic Aris-512 serial RAL witness (job 411835) 1.81×/1.85×/1.90× at S 1,428/2,540/5,712, computed entries bit-identical; real Aris data on the laptop 1.32× (centroid order) / 1.46× (ymin order), work fraction 0.59/0.51; human decision 2026-10-16: rank by ascending ymin, default gate 0.5→0.7; v3 (ymin + 0.7) present in the worktree diff, its test run, the RAL real-data witness (≥1.3× curvature step), ship and write-up are owed. Streaming 1e8 CPU/A100 rows, the #332 memo after-measurement, curvature preload (fixed-mapper science decision) and FFT levers unchanged.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_fixed_mapper_curvature_preload.md">Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`</a></h4><p class="review-meta">ready · normal</p><p>Phase 1 Mind draft (2026-10-04) still unstarted; run it through start_dev. Human science call first - no production SLaM stage holds the mapper fixed today.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_nnls_memo_scattered_stream_guard.md">fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoArray/issues/613" aria-label="Issue for fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)" title="Issue">↗</a></h4><p class="review-meta">active · medium</p><p>Library half shipped: PyAutoArray#615 (closes #613) merged 2026-10-06, in 2026.10.7.1. Local solver-only witness (n=576): iid memo on/off 1.49x → 1.17x; walk 0.18x unchanged. Owed: re-run the autolens_profiling#332 ALMA Delaunay 2.17x after-measurement (needs compute authorization).</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_streaming_scaling.md">Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/368" aria-label="Issue for Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities" title="Issue">↗</a></h4><p class="review-meta">active · high</p><p>Phase 1 CPU cells + results/streaming_scaling rows merged (autolens_profiling#375, 2026-10-04); wiki/campaigns/interferometer_streaming.md. Optional: 1e8 CPU row and A100 rows (compute authorization). Library candidate unfiled: chunk transformer.image_from in apply_sparse_operator (memory).</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_large_mask_aris.md">Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model</a><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoPulse/issues/62" aria-label="Issue for Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model" title="Issue">↗</a></h4><p class="review-meta">active · high</p><p>Released: #630/#631/#632/#634 in 2026.10.16.1 (2026-10-16). Phase-2 record #420 merged 2026-10-15 (table re-pinned at fd29902); #422/#424 merged 2026-10-16. L2 (PyAutoArray#635) paused 2026-10-16 with v3 (ymin order, gate 0.7) uncommitted in worktree feature/direct-conv-symmetric; synthetic witness PASS 1.81–1.90× (RAL 411835), real-data laptop 1.46× at ymin order; owed on resume: verify v3 + full suite, RAL real-data witness ≥1.3× (data and private library clones staged under scratch/aris_l2_20261016), Heart check, ship_library PR, write-up (results note, Knowledge section, decision record direct-conv-symmetric-gate). κ=1.32 means the #632 cost-model constant for direct_conv is effectively refit when L2 ships.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_w_tilde_fft_size_levers.md">Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space pixel scale on the A100</a></h4><p class="review-meta">ready · normal</p><p>Unstarted (no PR/issue/branch/results, 2026-10-04). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09f/wiki/campaigns/interferometer_likelihood.md" aria-label="Evidence for Interferometer likelihood and streaming" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-interferometer" data-open="campaign-interferometer" aria-label="Tasks for Interferometer likelihood and streaming" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Linear-solver accuracy and cost</strong><details id="campaign-linear-solver" class="campaign-detail"><summary>0 tasks · next step</summary><p>Retain the 2026-10-08 human decision: Jacobi for Mapper, raw+polish for MGE-only, no default change. Project wiki still says decision pending (stale). No new solver measurements after the recorded phase-5 results; release 2026.10.12.1 does not supply a rerun. Wide-prior/interferometer gaps remain evidence gaps, not authorized tasks. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md" aria-label="Evidence for Linear-solver accuracy and cost" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-linear-solver" data-open="campaign-linear-solver" aria-label="Tasks for Linear-solver accuracy and cost" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Production imaging and likelihood breakdown</strong><details id="campaign-imaging" class="campaign-detail"><summary>3 tasks · next step</summary><p>No new production-over-sampling or post-certified measurements. #408 now adds median headline fields to runtime cells; #412 merged 2026-10-13 and extends remaining headline surfaces. Production presets, memo-blind harness, cumulative MGE steps and quick-update costs remain separate pending work. Scope production-preset phase 1 (CPU smoke only); A100 measurement separately authorized. New related library evidence: PyAutoArray#624/#625 (numba Sibson kernel; DelaunayNN within 2× Delaunay on Aris 1,405) released 2026.10.16.1; follow-up PyAutoArray#629 (imaging DelaunayNN still ~2.7× Delaunay at sub_size 4: unique_mappings 0.30 vs 0.10 s, adaptive_pixel_signals 0.16 vs 0.01 s; laptop, threads=1, load 3–5, not a controlled measurement) is open and its Mind draft expects a Pulse campaign delaunay-nn-cpu that does not yet exist — human decision whether to open one.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/imaging_production_over_sampling.md">Profile imaging pixelizations at production over-sampling</a></h4><p class="review-meta">ready · normal</p><p>Phase 1 (workspace-only): --instrument euclid|hst presets for the JAX Delaunay runtime/breakdown cells, keeping flat-1 selectable; CPU smoke only. A100 re-measure is a separate compute-authorized phase.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/post_certified_solver_likelihood_breakdown.md">Assess remaining likelihood bottlenecks after certified solver integration on CPU and GPUs</a></h4><p class="review-meta">needs-slicing · normal</p><p>Blocked-by is stale: resolved 2026-09-27 (PyAutoArray#567 in 2026.9.26.1). Unissued; scope one bounded phase from the full task contract.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/quick_update_plotting_cost.md">Quick-update plotting cost — minutes per update, and it is not JAX compile</a></h4><p class="review-meta">ready · medium</p><p>Unchanged; targets PyAutoLens/PyAutoGalaxy (PyAutoLens#680 closed 2026-07-31, no follow-up issue). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/post_certified_breakdown.md" aria-label="Evidence for Production imaging and likelihood breakdown" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-imaging" data-open="campaign-imaging" aria-label="Tasks for Production imaging and likelihood breakdown" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Measurement reliability and profiling tools</strong><details id="campaign-measurement-tools" class="campaign-detail"><summary>8 tasks · next step</summary><p>Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415 closed. No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo interpretation, fresh runtime repeats and other tools remain pending. Check-in 2026-10-17: autolens_profiling#418 (pilot qualification design) open since 2026-10-13 with CI green and no review; #417 open; no new measurements.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/mge_likelihood_breakdown_steps_are_cumulative_an.md">MGE likelihood_breakdown steps are cumulative and `linear_gaussians` is reported as 0</a></h4><p class="review-meta">ready · low</p><p>Unchanged and unissued (no GitHub refs, 2026-10-04). Select one bounded step when measurement-tools is prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/gradient_cost_probe.md">A gradient-cost probe: forward vs `value_and_grad` ms/eval and a strict FD check, on any registry cell</a></h4><p class="review-meta">ready · low</p><p>Unissued. New context 2026-10-07: Heart unit-test timing flagged PyAutoLens forward-gradient tests +150%/+96% after PyAutoLens#768 rewrote that test file; not a controlled comparison, and this probe is the right instrument. Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/numba_breakdown_harness_memo_blind.md">Numba breakdown harness: perturb the instance so the operated-matrix memo cannot hide a step</a></h4><p class="review-meta">ready · medium</p><p>Unchanged and unissued (PyAutoArray#496 closed 2026-08-27). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/search_settings_estimation_infrastructure.md">Search settings-estimation + profiling infrastructure (n_starts / batch_size / n_batch)</a></h4><p class="review-meta">ready · normal</p><p>Unchanged and unissued (autolens_profiling#82 closed 2026-08-18). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/jax_compile_probe_needs_own_cell_builder.md">jax_compile/probe.py lost its cell builder with the searches tier — give profiling its own</a></h4><p class="review-meta">active · high</p><p>Manual HST/MGE fp64 jit builder #414 and separate raw pilot runner #416 merged. Bounded Mind implementations complete; wider builder coverage/global compile dispatch still unavailable. Agree a next capability or separately authorize a qualified pilot; no production compute authorized. Check-in 2026-10-17: autolens_profiling#418 (pilot qualification design) open since 2026-10-13 with CI green and no review; #417 open; no new measurements.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/profile_lens_aggregator_needs_config_dir.md">profile_lens_aggregator.py cannot run from the autolens_workspace_developer root: no config/ directory</a></h4><p class="review-meta">ready · low</p><p>Unchanged: workspace_developer main still has no root config/ (bug presumed live, not re-run). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/gradient_pixelization_pin_residual_drift.md">jax_profiling/gradient/imaging/pixelization.py: 3.2% of its pin move is unattributed</a></h4><p class="review-meta">ready · normal</p><p>Unchanged and unissued (PyAutoArray#490 merged 2026-08-26). Select one bounded step when prioritised.</p></article><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pair_jax_xla_env_vars_with_measured.md">Pair JAX/XLA env vars with measured compile and run times, per backend</a></h4><p class="review-meta">ready · high</p><p>Unchanged and unissued. Context: the jax-version policy is now released (2026.10.7.1 libraries require Nerves with the JAX deadlock exclusions). Select one bounded step when prioritised.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/pull/416" aria-label="Evidence for Measurement reliability and profiling tools" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-measurement-tools" data-open="campaign-measurement-tools" aria-label="Tasks for Measurement reliability and profiling tools" title="Open tasks">☷</a></div></td></tr><tr><td><strong>PyAutoFit profiling bootstrap</strong><details id="campaign-autofit" class="campaign-detail"><summary>1 task · next step</summary><p>B4a (autofit_profiling#3) and B4b (autofit_profiling#5, issue #4 closed, merged 2026-10-15) shipped: search.fit breakdown cell, profiling-summary@2 exporter, EP/graphical N3/N10/N30 baselines and analytic parity cell; #6 repointed RAL paths to the category layout (2026-10-15). Pulse fit fixture/campaign merged (#60, #61); fit receipt ok at autofit_profiling 58e56b9 (31 records, fetched 2026-10-15T22:58Z), so the post-deploy refresh is done. Epic 2 not opened: next bounded step is a human-scoped epic-2 campaign row; B5c inference work belongs to Insight, not Pulse.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/autofit_profiling_bootstrap.md">autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic</a></h4><p class="review-meta">active · normal</p><p>B4a (autofit_profiling#3) and B4b (autofit_profiling#5, issue #4 closed, merged 2026-10-15) shipped: search.fit breakdown cell, profiling-summary@2 exporter, EP/graphical N3/N10/N30 baselines and analytic parity cell; #6 repointed RAL paths to the category layout (2026-10-15). Pulse fit fixture/campaign merged (#60, #61); fit receipt ok at autofit_profiling 58e56b9 (31 records, fetched 2026-10-15T22:58Z), so the post-deploy refresh is done. Epic 2 not opened: next bounded step is a human-scoped epic-2 campaign row; B5c inference work belongs to Insight, not Pulse.</p></article></details></td><td><span class="pill">active</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autofit_profiling/issues/4" aria-label="Evidence for PyAutoFit profiling bootstrap" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-autofit" data-open="campaign-autofit" aria-label="Tasks for PyAutoFit profiling bootstrap" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Critical curves and evaluation grids</strong><details id="campaign-critical-curves" class="campaign-detail"><summary>0 tasks · next step</summary><p>No new campaign measurement. Phase 3b cap fix remains shipped; project wiki still incorrectly says #646 awaits merge/release. Seed coverage and path completion remain unresolved; retain marching-squares decision and ask the human whether to scope the next phase. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">needs-decision</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/critical_curves.md" aria-label="Evidence for Critical curves and evaluation grids" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-critical-curves" data-open="campaign-critical-curves" aria-label="Tasks for Critical curves and evaluation grids" title="Open tasks">☷</a></div></td></tr><tr><td><strong>Certified positive solver</strong><details id="campaign-certified-solver" class="campaign-detail"><summary>0 tasks · next step</summary><p>Parked unchanged. No new campaign evidence; existing C2/library/workspace drafts remain pending. Do not infer admission from solver timing, and do not change default or restart the campaign. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.</p><span class="review-meta">Reviewed 2026-10-17</span><h3>Active tasks</h3><p class="muted">No open tasks.</p></details></td><td><span class="pill">parked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/certified_positive_solver.md" aria-label="Evidence for Certified positive solver" title="Campaign evidence">↗</a><a class="icon-link" href="#campaign-certified-solver" data-open="campaign-certified-solver" aria-label="Tasks for Certified positive solver" title="Open tasks">☷</a></div></td></tr></tbody></table></div><p class="review-meta">Open means tracked, not necessarily running. <a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/campaigns.yaml">Full ledger ↗</a></p></div></details><details class="board-section"><summary><h2 id="evidence">Profiling Results</h2></summary><div class="board-section-body"><section class="setup-browser" data-setup-browser="lens" aria-label="autolens_profiling setups"><script type="application/json" class="setup-payload">{"catalogue":{"archive_record_count":20709,"comparison_policy":{"id":"no-temporal-comparisons"},"comparisons":[],"coverage":{"excluded":[],"expected":{"baseline_cells":29,"cells":101,"planned_slots":435},"observed":{"records":112,"selected":98,"setups":540}},"evidence_shards":[{"axes":["breakdown"],"axis_devices":{"breakdown":["cpu"]},"devices":["cpu"],"path":"catalogue/shards/eb112c2ba02f1d8956d3.json","precisions":["precision not 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no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"point_source_source","id":"baseline/point_source_source/source_plane/unspecified","instrument":null,"label":"point_source_source/source_plane/unspecified · baseline pending","model":"source_plane","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}},{"configuration":{"image_pixels_masked":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"image_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"n_vis":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"oversampling":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"dimensionless","value":null},"preloads":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"cluster","id":"baseline/cluster/image_plane/unspecified","instrument":null,"label":"cluster/image_plane/unspecified · baseline pending","model":"image_plane","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}},{"configuration":{"image_pixels_masked":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"image_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"n_vis":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"oversampling":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"dimensionless","value":null},"preloads":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"psf_shape":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"pixel","value":null},"regularization":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"solver":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null},"source_pixels":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"count","value":null},"transformer":{"reason":"Not recorded in this legacy evidence; no current default substituted.","unit":"name","value":null}},"configuration_id":"baseline-not-defined","dataset":"multi_dataset","id":"baseline/multi_dataset/delaunay/unspecified","instrument":null,"label":"multi_dataset/delaunay/unspecified · baseline pending","model":"delaunay","role":"planned_baseline","unknowns":{"instrument":"Instrument-independent or not yet declared by the baseline campaign."}}],"static_memory_estimates":[{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_delaunay.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay","per_replica":{"unit":"MiB","value":921.913},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":62},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_delaunay_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay","per_replica":{"unit":"MiB","value":131.505},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_mge.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"mge","per_replica":{"unit":"MiB","value":16.411},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_mge_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"mge","per_replica":{"unit":"MiB","value":16.411},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_pixelization.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"rectangular","per_replica":{"unit":"MiB","value":931.004},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":62},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/a100_probes/vmap_probe_pixelization_sparse.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"rectangular","per_replica":{"unit":"MiB","value":94.906},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"imaging","evidence":{"fragment":null,"path":"results/runtime/imaging/delaunay_nn/vmap_probe_delaunay_nn_batch64_cufft_failed.json"},"instrument":"hst","kind":"xla_static_buffer_estimate","model":"delaunay_nn","per_replica":{"unit":"MiB","value":770.97},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64},{"dataset":"point_source_source","evidence":{"fragment":null,"path":"results/runtime/point_source_source/source_plane_solved/vmap_probe_source_plane_solved.json"},"instrument":"simple","kind":"xla_static_buffer_estimate","model":"source_plane_solved","per_replica":{"unit":"MiB","value":0.0},"reason":"Compiler memory_analysis estimate; not observed peak VRAM or a validated batch recommendation.","recommended_batch_size":64}],"unbound_findings":[{"description":"The conversion maps every magnitude at or above 0.999 to q=0.000500250125062532. NumPy reaches the thin valid annulus below one but its constructor guard blocks the beyond-unit region; JAX arrays and tracers bypass that concrete-scalar guard.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.ell_comps.magnitude-saturation","path":"results/hazards/hazards_index.json"},"id":"component.ell_comps.magnitude-saturation","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"ell_comps","title":"ell_comps magnitude saturates at 0.999"},{"description":"A second, load-bearing saturation prevents the q=1 divisions in the elliptical Isothermal deflection from becoming 0/0. Its affected ell_comps disk is too small for prior-sampling discovery.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.isothermal.near-spherical-saturation","path":"results/hazards/hazards_index.json"},"id":"component.isothermal.near-spherical-saturation","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"isothermal","title":"Isothermal caps near-spherical axis ratios at 0.99999"},{"description":"`_wofz_rational` selects one of three rational branches with `xp.where` on |z|^2 and Im(z)^2. The value is continuous to ~1e-6 across a seam but the derivative is not, so `jax.grad` of any MGE deflection carries a kink wherever a grid point crosses a boundary. Autodiff derivative error just above the r2=2.5 seam reaches 7.71e-06 relative, against 1.3e-14 for the SciPy path the numpy backend uses.","discovery":{"models":[],"reason":"Shared MGE mass-profile deflection component study, not specifically the imaging MGE light-model likelihood. Consult the recorded component and revision before applying this finding.","shared":true},"evidence":{"fragment":"/findings/component.mge.faddeeva-seam-gradient","path":"results/hazards/hazards_index.json"},"id":"component.mge.faddeeva-seam-gradient","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"mge","title":"The JAX Faddeeva routine's region seams make its derivative discontinuous"},{"description":"The elliptical Faddeeva form is singular at q = 1, so the MGE machinery clamps the axis ratio to 0.9999. The numpy path escapes through an exact radial branch; the JAX path cannot, and every *Sph MGE-routed profile is biased by up to 6.18e-05 relative with a spurious cross-axis deflection of 0.000139 arcsec.","discovery":{"models":[],"reason":"Shared MGE mass-profile deflection component study, not specifically the imaging MGE light-model likelihood. Consult the recorded component and revision before applying this finding.","shared":true},"evidence":{"fragment":"/findings/component.mge.spherical-clamp-bias","path":"results/hazards/hazards_index.json"},"id":"component.mge.spherical-clamp-bias","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"mge","title":"Spherical MGE profiles are evaluated as ellipses at the q = 0.9999 clamp on JAX"},{"description":"The JAX backend replaces the exact SciPy hyp2f1 angular factor with a fixed 20-term omega series. Relative deflection error grows continuously to 0.297 over the measured factor curve.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.power-law.series-vs-hyp2f1-divergence","path":"results/hazards/hazards_index.json"},"id":"component.power-law.series-vs-hyp2f1-divergence","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"power_law","title":"PowerLaw's 20-term JAX series diverges from hyp2f1 at high ellipticity"},{"description":"The spherical radial coordinate returns the correct finite value zero at the profile centre, but JAX differentiation of sqrt(y^2+x^2) returns a non-finite gradient there.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/component.spherical-geometry.radial-sqrt-gradient-at-zero","path":"results/hazards/hazards_index.json"},"id":"component.spherical-geometry.radial-sqrt-gradient-at-zero","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"component","subject_name":"spherical_geometry","title":"Radial sqrt has a non-finite gradient at r=0"},{"description":"Using only the diagonal entries the policy actually touches, the absolute floor reaches 4.457e-05 of their scale. A reference-calibrated scale-aware counterfactual holds its fraction fixed with maximum relative output error 3.395e-05.","discovery":{"models":[{"dataset":"imaging","model":"rectangular"}],"reason":"The reproducer is the imaging rectangular-source likelihood fixture. This is related model evidence; it does not establish applicability to other configurations or measured versions.","shared":false},"evidence":{"fragment":"/findings/likelihood.imaging-pixelization.absolute-conditioning-floors","path":"results/hazards/hazards_index.json"},"id":"likelihood.imaging-pixelization.absolute-conditioning-floors","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"imaging_pixelization","title":"Absolute inversion floors move with dataset scale"},{"description":"The NumPy full-likelihood scan changes active support at 5 bounded Einstein-radius location(s). The report measures explicit epsilon-neighbourhood mass, not exact-kink prior mass.","discovery":{"models":[{"dataset":"imaging","model":"rectangular"}],"reason":"The reproducer is the imaging rectangular-source likelihood fixture. This is related model evidence; it does not establish applicability to other configurations or measured versions.","shared":false},"evidence":{"fragment":"/findings/likelihood.imaging-pixelization.nnls-active-set-kinks","path":"results/hazards/hazards_index.json"},"id":"likelihood.imaging-pixelization.nnls-active-set-kinks","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"imaging_pixelization","title":"NNLS support changes create likelihood kinks"},{"description":"2 argmax-switch location(s) found in [0.0, 3.0] (4 truth positions, 0 of them where max_sep is already over threshold — i.e. a second, threshold-independent gradient kink in the ACTIVE penalty region, from max_sep itself being a max of several smooth per-position functions).","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.argmax-switch","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.argmax-switch","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"Which position-pair sets max_sep switches discontinuously across the sweep"},{"description":"Across 40/401 grid points with max_sep \u003c threshold, penalty value and gradient are exactly zero (max |value|=0.000e+00, max |grad|=0.000e+00) — a gradient-search lane deep inside the fence gets NO signal at all from the positions term, by construction.","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.interior-plateau","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.interior-plateau","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"PositionsLH penalty is an exact-zero, zero-gradient plateau inside threshold"},{"description":"Sweeping einstein_radius in [0.0, 3.0] (401 pts, threshold=0.3, factor=1e+08), a tight (+/-1e-6) probe at each of 2 max_sep==threshold crossing(s) shows the penalty VALUE is continuous there (max gap 2.006e+02) while its jax-autodiff GRADIENT jumps by up to 2.006e+08 — zero on the interior side, nonzero on the exterior side. A gradient optimizer sees a step-function force switching on at the fence, not a smooth potential.","discovery":{"models":[],"reason":"Shared positions-penalty method study. Whether a particular likelihood uses this penalty and is affected is not established.","shared":true},"evidence":{"fragment":"/findings/likelihood.positions-penalty.threshold-hinge","path":"results/hazards/hazards_index.json"},"id":"likelihood.positions-penalty.threshold-hinge","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"likelihood","subject_name":"positions_penalty","title":"PositionsLH penalty has a C0 hinge at the threshold crossing"},{"description":"The configured 1.0e-03 diagonal add ranges from dominant to negligible as synthetic curvature scale changes. Detection needs no dataset; judging scientific relevance does.","discovery":{"models":[],"reason":"Shared component or matrix study. Model usage and exact setup/version applicability have not been established.","shared":true},"evidence":{"fragment":"/findings/matrix.curvature.absolute-diagonal-floor","path":"results/hazards/hazards_index.json"},"id":"matrix.curvature.absolute-diagonal-floor","reason":"Legacy finding does not identify measured library versions and an exact profiling setup; no global applicability inferred.","status":"applicability_unknown","subject":"matrix","subject_name":"curvature_matrix","title":"Absolute curvature-diagonal floor is scale dependent"}],"valid_until":null,"version":2},"instance":"lens","label":"PyAutoLens","repo":"https://github.com/PyAutoLabs/autolens_profiling","commit":"fd29902bd09ff5defcab5e9007a547423048e8aa","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autolens_profiling/fd29902bd09ff5defcab5e9007a547423048e8aa/dashboard/","local":false,"remote_shards":true}</script><p data-id="load-status" role="status" aria-live="polite"></p><div data-id="navigation"></div><section data-id="results" aria-label="Selected setup" hidden></section><noscript><details><summary>Original setup evidence (JavaScript disabled)</summary><ul><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_breakdown_v2026.7.23.1.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_breakdown_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_breakdown_v2026.7.6.649.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_breakdown_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_hpc_ral_cpu_fp64.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/image_plane_local_cpu_fp64.json">cluster / image_plane / unspecified — results/breakdown/cluster/image_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/source_plane_breakdown_v2026.7.23.1.json">cluster / source_plane / unspecified — results/breakdown/cluster/source_plane_breakdown_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/cluster/source_plane_breakdown_v2026.7.6.649.json">cluster / source_plane / unspecified — results/breakdown/cluster/source_plane_breakdown_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/datacube/delaunay_hpc_a100_fp64.json">datacube / delaunay / alma_high — results/breakdown/datacube/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/datacube/delaunay_hpc_a100_mp.json">datacube / delaunay / alma_high — results/breakdown/datacube/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.5.29.4_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.7.6.649_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_breakdown_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_autotune4.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_autotune4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1_control.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_lever1_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n12000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_n8000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_recon_split.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_mp.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_hpc_a100_mp_sparse.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_hpc_a100_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_breakdown_hst_v2026.8.17.1.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly_control.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_assembly_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk1024.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk2048.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk2048.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk4096.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk512.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_chunk512.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_control.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_launch_latency_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_recon_split.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_sparse.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_vmap64.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_vmap64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay_nn / hst — results/breakdown/imaging/delaunay_nn_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_numba_breakdown_euclid_v2026.8.17.1.json">imaging / delaunay / euclid — results/breakdown/imaging/delaunay_numba_breakdown_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/delaunay_numba_breakdown_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/breakdown/imaging/delaunay_numba_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2_control.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever2_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3_control.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_hpc_a100_fp64_fixed_light_lever3_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1000_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n1500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n2500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n3000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n3000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n4000_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_hpc_a100_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_cpu_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_fp64_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_mp_fixed_light.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_delaunay_n500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_delaunay_nn_hpc_a100_fp64_fixed_light.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_delaunay_nn_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n1250_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n2500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / euclid — results/breakdown/imaging/fixed_light_library_delaunay_euclid_n500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_hpc_a100_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_t1.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_tall.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n1250_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n2500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_cpu_fp64_fixed_light_library_t8.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_cpu_fp64_fixed_light_library_t8.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay / hst — results/breakdown/imaging/fixed_light_library_delaunay_n500_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_hpc_a100_fp64_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_t1.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_tall.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_fp64_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_mp_fixed_light_library.json">imaging / delaunay_nn / hst — results/breakdown/imaging/fixed_light_library_delaunay_nn_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n1225_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n1225_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_euclid_n484_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / euclid — results/breakdown/imaging/fixed_light_library_rectangular_euclid_n484_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_hpc_a100_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_hpc_a100_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_t1.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_t1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_tall.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_cpu_fp64_fixed_light_library_tall.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library_vmap2.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_fp64_fixed_light_library_vmap2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_mp_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_local_rtx2060_mp_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n1225_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n1225_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n2500_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n2500_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_library_rectangular_n484_local_rtx2060_fp64_fixed_light_library.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_library_rectangular_n484_local_rtx2060_fp64_fixed_light_library.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1024_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n1521_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n2500_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3025_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3025_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n3969_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_hpc_a100_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_hpc_a100_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_cpu_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_cpu_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_fp64_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_fp64_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_mp_fixed_light.json">imaging / rectangular / hst — results/breakdown/imaging/fixed_light_rectangular_n484_local_rtx2060_mp_fixed_light.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n12000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n3000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n5000.json">imaging / delaunay / hst — results/breakdown/imaging/matrix_free_delaunay_hpc_a100_fp64_matrix_free_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_delaunay_nn_hpc_a100_fp64_matrix_free.json">imaging / delaunay_nn / hst — results/breakdown/imaging/matrix_free_delaunay_nn_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n12000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n3000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n5000.json">imaging / rectangular / hst — results/breakdown/imaging/matrix_free_rectangular_hpc_a100_fp64_matrix_free_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.5.29.4.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.7.6.649.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_breakdown_hst_v2026.8.17.1.json">imaging / mge / hst — results/breakdown/imaging/mge_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/mge_hpc_a100_fp64.json">imaging / mge / hst — results/breakdown/imaging/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.5.29.4_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.7.6.649_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_breakdown_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n12000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n3000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n5000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_n8000_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_recon_split.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_fp64_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_mp.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_hpc_a100_mp_sparse.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_hpc_a100_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_euclid_v2026.8.17.1.json">imaging / rectangular / euclid — results/breakdown/imaging/pixelization_numba_breakdown_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/breakdown/imaging/pixelization_numba_breakdown_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_a100_mp.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / alma_high — results/breakdown/interferometer/alma_high/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64_sparse.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/mge_hpc_a100_mp.json">interferometer / mge / alma_high — results/breakdown/interferometer/alma_high/mge_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_a100_mp.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / alma_high — results/breakdown/interferometer/alma_high/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay / sma — results/breakdown/interferometer/delaunay_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_constant_split.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_a100_mp.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay / sma — results/breakdown/interferometer/delaunay_numba_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_direct_conv_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_levers.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_levers.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r4.25.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r4.25.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r6.0.json">interferometer / delaunay / alma — results/breakdown/interferometer/delaunay_numba_hpc_ral_cpu_fp64_r6.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_jax_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_jax_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_jax_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_jax_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_reference_breakdown_alma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / alma — results/breakdown/interferometer/delaunay_numba_reference_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_reference_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_reference_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_source_loop_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_source_loop_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_symmetric_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_symmetric_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/delaunay_numba_two_stage_breakdown_sma_v2026.8.17.1.json">interferometer / delaunay_hilbert_1500 / sma — results/breakdown/interferometer/delaunay_numba_two_stage_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/delaunay_hpc_a100_mp.json">interferometer / delaunay / jvla — results/breakdown/interferometer/jvla/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_fp64.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_fp64_sparse.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/mge_hpc_a100_mp.json">interferometer / mge / jvla — results/breakdown/interferometer/jvla/mge_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/pixelization_hpc_a100_fp64.json">interferometer / rectangular / jvla — results/breakdown/interferometer/jvla/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/jvla/pixelization_hpc_a100_mp.json">interferometer / rectangular / jvla — results/breakdown/interferometer/jvla/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_high_v2026.8.17.1.json">interferometer / mge / alma_high — results/breakdown/interferometer/mge_breakdown_alma_high_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1.json">interferometer / mge / alma — results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1_sparse.json">interferometer / mge / alma — results/breakdown/interferometer/mge_breakdown_alma_v2026.8.17.1_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1.json">interferometer / mge / sma — results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1_sparse.json">interferometer / mge / sma — results/breakdown/interferometer/mge_breakdown_sma_v2026.8.17.1_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_dft_breakdown_sma_v2026.8.17.1.json">interferometer / mge / sma — results/breakdown/interferometer/mge_dft_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_hpc_a100_fp64.json">interferometer / mge / alma — results/breakdown/interferometer/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/mge_hpc_a100_fp64_sparse.json">interferometer / mge / alma — results/breakdown/interferometer/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n1000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n1000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n2500.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n4000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_hpc_a100_fp64_n4000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n1000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n1000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n2500.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n4000.json">interferometer / delaunay / alma — results/breakdown/interferometer/n_sweep/delaunay_numba_hpc_ral_cpu_fp64_n4000.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n1024.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n2500.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n4096.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_hpc_a100_fp64_n4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n1024.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n1024.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n2500.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n2500.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n4096.json">interferometer / rectangular / alma — results/breakdown/interferometer/n_sweep/pixelization_numba_hpc_ral_cpu_fp64_n4096.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular / sma — results/breakdown/interferometer/pixelization_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_a100_mp.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular / sma — results/breakdown/interferometer/pixelization_numba_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_direct_conv_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_levers.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_levers.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r4.25.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r4.25.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r6.0.json">interferometer / rectangular / alma — results/breakdown/interferometer/pixelization_numba_hpc_ral_cpu_fp64_r6.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_jax_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_jax_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_jax_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_jax_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_reference_breakdown_alma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / alma — results/breakdown/interferometer/pixelization_numba_reference_breakdown_alma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/pixelization_numba_reference_breakdown_sma_v2026.8.17.1.json">interferometer / rectangular_adapt_image_32x32 / sma — results/breakdown/interferometer/pixelization_numba_reference_breakdown_sma_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/delaunay_hpc_a100_fp64.json">interferometer / delaunay / sdp81 — results/breakdown/interferometer/sdp81/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / sdp81 — results/breakdown/interferometer/sdp81/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64.json">interferometer / mge / sdp81 — results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64_sparse.json">interferometer / mge / sdp81 — results/breakdown/interferometer/sdp81/mge_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/pixelization_hpc_a100_fp64.json">interferometer / rectangular / sdp81 — results/breakdown/interferometer/sdp81/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sdp81/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / sdp81 — results/breakdown/interferometer/sdp81/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r2.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r5.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_a100_mp.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_hpc_ral_cpu_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / delaunay / sma — results/breakdown/interferometer/sma/delaunay_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter_main_control.json">interferometer / mge / sma — results/breakdown/interferometer/sma/mge_hpc_a100_fp64_real_scatter_main_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r2.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r5.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_a100_mp.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_a100_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_hpc_ral_cpu_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r2.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json">interferometer / rectangular / sma — results/breakdown/interferometer/sma/pixelization_numba_hpc_ral_cpu_fp64_r5.0.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64_p4.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_hpc_ral_cpu_fp64_p4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_image/image_plane_local_cpu_fp64.json">point_source_image / image_plane / simple — results/breakdown/point_source_image/image_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_hpc_a100_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_hpc_ral_cpu_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/breakdown/point_source_source/source_plane_local_cpu_fp64.json">point_source_source / source_plane / simple — results/breakdown/point_source_source/source_plane_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_constant_split.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_control_2026_09_07.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_control_2026_09_07.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_hpc_a100_fp64_walk_early_exit.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/delaunay_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/delaunay_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/comparison.json">imaging / other / hst — results/runtime/imaging/delaunay/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp_sparse.json">imaging / delaunay / hst — results/runtime/imaging/delaunay/hst/delaunay_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/delaunay/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64_sparse.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp_sparse.json">imaging / delaunay / jwst — results/runtime/imaging/delaunay/jwst/delaunay_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly_control.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_assembly_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_autotune4.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_autotune4.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_constant_split_2026_09_05.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_chunk256.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_control.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_launch_latency_control.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_sparse.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_nn/delaunay_nn_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay_nn / hst — results/runtime/imaging/delaunay_nn/delaunay_nn_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_euclid_v2026.8.17.1.json">imaging / delaunay / euclid — results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_hst_v2026.8.17.1.json">imaging / delaunay / hst — results/runtime/imaging/delaunay_numba/delaunay_numba_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/comparison.json">imaging / other / ao — results/runtime/imaging/mge/ao/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/comparison.json">imaging / other / ao — results/runtime/imaging/mge/ao/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/mge_local_cpu_fp64.json">imaging / mge / ao — results/runtime/imaging/mge/ao/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/ao/mge_local_cpu_mp.json">imaging / mge / ao — results/runtime/imaging/mge/ao/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/comparison.json">imaging / other / hst — results/runtime/imaging/mge/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_fp64.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_fp64_sparse.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_mp.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/hst/mge_local_cpu_mp_sparse.json">imaging / mge / hst — results/runtime/imaging/mge/hst/mge_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/mge/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_fp64.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_fp64_sparse.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_mp.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/jwst/mge_local_cpu_mp_sparse.json">imaging / mge / jwst — results/runtime/imaging/mge/jwst/mge_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge/mge_likelihood_summary_hst_v2026.8.17.1.json">imaging / mge / hst — results/runtime/imaging/mge/mge_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/mge_mass_jax/mge_mass_jax_likelihood_summary_hst_v2026.8.17.1.json">imaging / mge / hst — results/runtime/imaging/mge_mass_jax/mge_mass_jax_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/comparison.json">imaging / other / hst — results/runtime/imaging/pixelization/hst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/hst/pixelization_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/comparison.json">imaging / other / jwst — results/runtime/imaging/pixelization/jwst/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64_sparse.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp_sparse.json">imaging / rectangular / jwst — results/runtime/imaging/pixelization/jwst/pixelization_local_cpu_mp_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_recon_split.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_recon_split.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_sparse.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_hpc_a100_fp64_sparse.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1_rtu.json">imaging / rectangular / hst — results/runtime/imaging/pixelization/pixelization_likelihood_summary_hst_v2026.8.17.1_rtu.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_euclid_v2026.8.17.1.json">imaging / rectangular / euclid — results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_euclid_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization_numba/pixelization_numba_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/imaging/pixelization_numba_mge_mass/pixelization_numba_mge_mass_likelihood_summary_hst_v2026.8.17.1.json">imaging / rectangular / hst — results/runtime/imaging/pixelization_numba_mge_mass/pixelization_numba_mge_mass_likelihood_summary_hst_v2026.8.17.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/alma/comparison.json">interferometer / delaunay / alma — results/runtime/interferometer/delaunay/alma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/alma/delaunay_local_cpu_mp.json">interferometer / delaunay / alma — results/runtime/interferometer/delaunay/alma/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/comparison.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/comparison.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_fp64.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_mp.json">interferometer / delaunay / sma — results/runtime/interferometer/delaunay/sma/delaunay_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/comparison.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/comparison.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/mge_local_cpu_fp64.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/mge_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/mge/sma/mge_local_cpu_mp.json">interferometer / mge / sma — results/runtime/interferometer/mge/sma/mge_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/comparison.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/comparison.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_fp64.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_mp.json">interferometer / rectangular / sma — results/runtime/interferometer/pixelization/sma/pixelization_local_cpu_mp.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_image/image_plane_solved/image_plane_solved_summary_v2026.7.23.1.json">point_source_image / image_plane / unspecified — results/runtime/point_source_image/image_plane_solved/image_plane_solved_summary_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/comparison.json">point_source_source / other / unspecified — results/runtime/point_source_source/source_plane_solved/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/comparison.json">point_source_source / other / unspecified — results/runtime/point_source_source/source_plane_solved/comparison.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_a100_fp64.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_a100_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_ral_cpu_fp64.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_hpc_ral_cpu_fp64.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/runtime/point_source_source/source_plane_solved/source_plane_solved_summary_v2026.7.23.1.json">point_source_source / source_plane / unspecified — results/runtime/point_source_source/source_plane_solved/source_plane_solved_summary_v2026.7.23.1.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/accumulate_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1%2B2.json">interferometer / streaming / unspecified — results/streaming_scaling/in_memory_local_cpu_v2026.10.4.1+2.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json">imaging / delaunay_matern / hst — scripts/misc/jax_compile/results/local_cpu/delaunay_matern.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/export_probe.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/export_probe.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/export_probe.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/export_probe.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/knn.json">imaging / knn / hst — scripts/misc/jax_compile/results/local_cpu/knn.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">experiments / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">experiments / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_cpu/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_cpu/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_cpu/trace_profile.json">experiments / mge / unspecified — scripts/misc/jax_compile/results/local_cpu/trace_profile.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json">imaging / 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scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json">imaging / rectangular / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/pixelization.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/fd29902bd09ff5defcab5e9007a547423048e8aa/scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json">multi_dataset / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_GeForce_RTX_2060_with_Max-Q_Design/mge.json</a></li></ul></details></noscript></section><section class="setup-browser" data-setup-browser="fit" aria-label="autofit_profiling setups"><script type="application/json" 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row"},{"id":"select/gaussian_x1/nautilus/search_fit/breakdown/proposal_sampling","setup_id":"gaussian_x1/nautilus/search_fit","axis":"breakdown","metric":"proposal_sampling","unit":"s","method_id":"search-fit-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/search_fit/breakdown/proposal_sampling","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/search_fit/breakdown/likelihood","setup_id":"gaussian_x1/nautilus/search_fit","axis":"breakdown","metric":"likelihood","unit":"s","method_id":"search-fit-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/search_fit/breakdown/likelihood","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/search_fit/breakdown/checkpoint_io","setup_id":"gaussian_x1/nautilus/search_fit","axis":"breakdown","metric":"checkpoint_io","unit":"s","method_id":"search-fit-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/search_fit/breakdown/checkpoint_io","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/search_fit/breakdown/sampler_bookkeeping","setup_id":"gaussian_x1/nautilus/search_fit","axis":"breakdown","metric":"sampler_bookkeeping","unit":"s","method_id":"search-fit-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/search_fit/breakdown/sampler_bookkeeping","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/search_fit/breakdown/samples_output","setup_id":"gaussian_x1/nautilus/search_fit","axis":"breakdown","metric":"samples_output","unit":"s","method_id":"search-fit-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/search_fit/breakdown/samples_output","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/search_fit/breakdown/visualisation","setup_id":"gaussian_x1/nautilus/search_fit","axis":"breakdown","metric":"visualisation","unit":"s","method_id":"search-fit-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/search_fit/breakdown/visualisation","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/search_fit/breakdown/other","setup_id":"gaussian_x1/nautilus/search_fit","axis":"breakdown","metric":"other","unit":"s","method_id":"search-fit-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/search_fit/breakdown/other","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/runtime/wall","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"runtime","metric":"wall","unit":"s","method_id":"bare-sampler-exclusive-component-wall/wall","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/runtime/wall","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/nn_training","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"nn_training","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/nn_training","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/bound_geometry","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"bound_geometry","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/bound_geometry","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/proposal_sampling","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"proposal_sampling","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/proposal_sampling","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/likelihood","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"likelihood","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/likelihood","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/checkpoint_io","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"checkpoint_io","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"inapplicable","record_id":null,"reason":"the bare sampler is run without a checkpoint file, so it writes none"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/sampler_bookkeeping","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"sampler_bookkeeping","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/bare_sampler/breakdown/sampler_bookkeeping","reason":"the cell's committed median row"},{"id":"select/gaussian_x1/nautilus/bare_sampler/breakdown/other","setup_id":"gaussian_x1/nautilus/bare_sampler","axis":"breakdown","metric":"other","unit":"s","method_id":"bare-sampler-exclusive-component-wall/components","identity":{"device":"cpu","backend":"numpy","precision":"float64","library_version":"2026.10.12.1","software":{"PyAutoFit":"911fe05e1c6b570df51789f0ab5b2cbf54c1f43a","PyAutoNerves":"7f3055b7f3d1967f372bdb41ee732135bca0e68d","nautilus-sampler":"1.0.5","numpy":"2.2.6","python":"3.12.10","scikit-learn":"1.5.1","scipy":"1.17.1"}},"host":"DESKTOP-H143S82","status":"unreviewed","record_id":"gaussian_x1/nautilus/bare_sampler/breakdown/other","reason":"the cell's committed median row"}],"hazards":[],"recommendations":[],"comparisons":[],"limitations":["Laptop CPU only, pinned to one host (DESKTOP-H143S82, WSL2) so Pulse's same-host rule pairs every record; the epic's release-sweep pin (euclid-ral-gpu-2, an A100 node) does not fit CPU Dynesty runs. Cross-machine reproduction on RAL is a later campaign.","Wall times vary up to ±40 % between sessions on this host, so breakdown shares are the comparable quantity, not seconds; the EP / graphical / parity rows are single fresh-process fits (repetitions 1).","Component times are exclusive monkey-patched wall-clock intervals; 'other' is wall minus the instrumented total, not a measured phase.","The EP / graphical rows carry a producer-side reproduction note against the committed autofit_workspace_developer baselines (runtime-drift-2x-1ms, in each row's `reference`); it is not a Pulse comparison, because those baselines recorded no host and no record pair exists.","The N30 EP and graphical rows reproduce the committed sanity FAIL; their wall and memory are real measurements of a fit whose parameter recovery fails, not of a correct fit.","Every record is unreviewed: the producer declares measurements, the Brain's profiling conductor judges them; nothing here is a verdict.","No temporal comparisons: one baseline per setup."]},"instance":"fit","label":"autofit","repo":"https://github.com/PyAutoLabs/autofit_profiling","commit":"58e56b9646da2181061b2e8d7907d6db2542ee07","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autofit_profiling/58e56b9646da2181061b2e8d7907d6db2542ee07/dashboard/","local":false,"remote_shards":true}</script><p data-id="load-status" role="status" aria-live="polite"></p><div data-id="navigation"></div><section data-id="results" aria-label="Selected setup" hidden></section><noscript><details><summary>Original setup evidence (JavaScript disabled)</summary><ul></ul></details></noscript></section><!-- decision-history:20f0256fc1d56eda82a910f81ddb71d393e76eeaf26e486d9c836b00282b99f9 --></div></details><details class="board-section"><summary><h2>Decision History</h2></summary><div class="board-section-body"><section id="decision-history"><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/interferometer-route-gate-cost-model.md" target="_blank" rel="noopener" title="Opens in a new tab">Route interferometer curvature by a calibrated two-constant cost model</a><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/adaptsplit-zero-weight-delaunay-only.md" target="_blank" rel="noopener" title="Opens in a new tab">Exclude zero-weight mappings from AdaptSplit pixel signals on Delaunay meshes only</a><a class="model-choice" href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md" target="_blank" rel="noopener" title="Opens in a new tab">Keep Jacobi PDIP as the Mapper default and raw+polish as the MGE-only default</a></section></div></details><ul class="boards"><li class="muted" style="padding:.3rem 0">Boards:</li><li><a data-organ="organism" href="https://pyautolabs.github.io/PyAutoScientist/">Scientist</a></li><li><a data-organ="brain" href="https://pyautolabs.github.io/PyAutoBrain/">Brain</a></li><li><a data-organ="mind" href="https://pyautolabs.github.io/PyAutoMind/">Mind</a></li><li><a data-organ="cortex" href="https://pyautolabs.github.io/PyAutoCortex/">Cortex</a></li><li><a data-organ="memory" href="https://pyautolabs.github.io/PyAutoMemory/">Memory</a></li><li><a data-organ="eyes" href="https://pyautolabs.github.io/PyAutoEyes/">Eyes</a></li><li><a data-organ="ears" href="https://pyautolabs.github.io/PyAutoEars/">Ears</a></li><li><a data-organ="heart" href="https://pyautolabs.github.io/PyAutoHeart/">Heart</a></li><li><a data-organ="hands" href="https://pyautolabs.github.io/PyAutoHands/">Hands</a></li><li><a data-organ="insight" href="https://pyautolabs.github.io/PyAutoInsight/">Insight</a></li><li><a data-organ="dna" href="https://pyautolabs.github.io/PyAutoDNA/">DNA</a></li><li><a data-organ="nerves" href="https://pyautolabs.github.io/PyAutoNerves/">Nerves</a></li><li><a data-organ="broca" href="https://pyautolabs.github.io/PyAutoBroca/">Broca</a></li><li><a data-organ="gut" href="https://pyautolabs.github.io/PyAutoGut/">Gut</a></li></ul></main><script>const MAX_PROMPT_CHARS = 50000;
 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 8cb253d..2e06da1 100644
--- a/dashboard.md
+++ b/dashboard.md
@@ -1,6 +1,6 @@
 # PyAutoPulse — profiling dashboard
 
-<!-- pulse-campaigns:4e132b2d2378f34b9712646cb9a497b82be62a3d7d484f8136ab2bc19cc70303 -->
+<!-- pulse-campaigns:19faf009965e3df26e5113e7c9ff26b4dcb4a5df3d156bf43e8838997abca73d -->
 ## Profiling Check In
 
 <details><summary>Full check-in prompt</summary>
@@ -25,13 +25,13 @@ After taking action, report what changed, what the evidence supports and what re
 
 </details>
 
-Last check-in: 2026-10-13T17:51:40Z (review date, not measurement freshness).
+Last check-in: 2026-10-17T11:09:26Z (review date, not measurement freshness).
 
 ## Active campaigns
 
 <details><summary>Setup baseline collection and scientific acceptance — needs-decision</summary>
 
-Reviewed 2026-10-13. 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.
+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.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/pull/416)
 
@@ -44,7 +44,7 @@ Reviewed 2026-10-13. Review the Pulse collection choices (candidate input hashes
 
 <details><summary>Point-source image plane · CPU — active</summary>
 
-Reviewed 2026-10-13. 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.
+Reviewed 2026-10-17. 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.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04)
 
@@ -56,7 +56,7 @@ Reviewed 2026-10-13. No new committed campaign measurements. Phases 1–4 remain
 
 <details><summary>Point-source image plane · A100 — needs-decision</summary>
 
-Reviewed 2026-10-13. No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement.
+Reviewed 2026-10-17. No new image-plane fit measurement found. Correctness fix #768 remains shipped; current libraries now have release 2026.10.12.1, not a measured performance baseline. Image-plane admission measurement still needs authorization. #411 merged 2026-10-13, recording the paired-round MDI reinterpretation; no new measurement. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_gpu_breakdown.md)
 
@@ -68,7 +68,7 @@ Reviewed 2026-10-13. No new image-plane fit measurement found. Correctness fix #
 
 <details><summary>Point-source source plane — parked</summary>
 
-Reviewed 2026-10-13. Core remains parked; autolens_inference#21 reorganized setup evidence but added no point-source gradient admission measurement. #408 merged median fields into 11 runtime cells; #412 merged 2026-10-13, completing headline/repeat support. No new runtime sweep; keep Blackjax parked.
+Reviewed 2026-10-17. Core remains parked; autolens_inference#21 reorganized setup evidence but added no point-source gradient admission measurement. #408 merged median fields into 11 runtime cells; #412 merged 2026-10-13, completing headline/repeat support. No new runtime sweep; keep Blackjax parked. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/point_source_source_plane.md)
 
@@ -81,7 +81,7 @@ Reviewed 2026-10-13. Core remains parked; autolens_inference#21 reorganized setu
 
 <details><summary>Cluster PointSolver robustness and performance — active</summary>
 
-Reviewed 2026-10-13. No campaign wiki/results change or new matching implementation claim found. Representative cluster baseline and observable-overflow policy remain unstarted. Choose one bounded phase; inherited single-source results do not qualify a representative cluster baseline.
+Reviewed 2026-10-17. No campaign wiki/results change or new matching implementation claim found. Representative cluster baseline and observable-overflow policy remain unstarted. Choose one bounded phase; inherited single-source results do not qualify a representative cluster baseline. Check-in 2026-10-17: no project wiki/results/issue change since 2026-10-13 beyond autolens_profiling#420/#422/#424 (interferometer only); releases 2026.10.16.1/.2 and 2026.10.17.1 supply no new measurement.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/cluster_pointsolver.md)
 
@@ -94,23 +94,23 @@ Reviewed 2026-10-13. No campaign wiki/results change or new matching implementat
 
 <details><summary>Interferometer likelihood and streaming — active</summary>
 
-Reviewed 2026-10-15. Aris large-mask work (PyAutoPulse#62): #630/#631/#632 merged 2026-10-15, pending release; knowledge section and two human decisions recorded; phase-2 record at PR-open (autolens_profiling#420); L1 Cholesky reuse (PyAutoArray#633) in development, L2 direct_conv symmetry filed and unstarted. Streaming 1e8 CPU/A100 rows and the #332 memo after-measurement still need authorization; curvature preload needs the fixed-mapper science decision; FFT levers remain unstarted.
+Reviewed 2026-10-17. Aris large-mask (PyAutoPulse#62): library PRs PyAutoArray#630/#631/#632/#634 RELEASED in 2026.10.16.1 (2026-10-16; 2026.10.16.2 and 2026.10.17.1 followed with no further PyAutoArray change). Phase-2 route-gate record autolens_profiling#420 merged 2026-10-15; #422 (gate-60 wording historical) and #424 (lint re-pin after #634) merged 2026-10-16; project main fd29902. L1 Cholesky reuse (#634) measured on the NumPy path: solve+log-det 144.5→50.9 ms at 1,405 vertices, 1,268→373 ms at 2,947 (full call −22–24% / −51%). L2 direct_conv symmetry (PyAutoArray#635) PAUSED by the human 2026-10-16, uncommitted on worktree feature/direct-conv-symmetric (base e59d5fa9), no PR: synthetic Aris-512 serial RAL witness (job 411835) 1.81×/1.85×/1.90× at S 1,428/2,540/5,712, computed entries bit-identical; real Aris data on the laptop 1.32× (centroid order) / 1.46× (ymin order), work fraction 0.59/0.51; human decision 2026-10-16: rank by ascending ymin, default gate 0.5→0.7; v3 (ymin + 0.7) present in the worktree diff, its test run, the RAL real-data witness (≥1.3× curvature step), ship and write-up are owed. Streaming 1e8 CPU/A100 rows, the #332 memo after-measurement, curvature preload (fixed-mapper science decision) and FFT levers unchanged.
 
-[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/interferometer_likelihood.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) — ready: Phase 1 Mind draft (2026-10-04) still unstarted; run it through start_dev. Human science call first - no production SLaM stage holds the mapper fixed today.
 - [fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_nnls_memo_scattered_stream_guard.md) — active: Library half shipped: PyAutoArray#615 (closes #613) merged 2026-10-06, in 2026.10.7.1. Local solver-only witness (n=576): iid memo on/off 1.49x → 1.17x; walk 0.18x unchanged. Owed: re-run the autolens_profiling#332 ALMA Delaunay 2.17x after-measurement (needs compute authorization).
 - [Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_streaming_scaling.md) — active: Phase 1 CPU cells + results/streaming_scaling rows merged (autolens_profiling#375, 2026-10-04); wiki/campaigns/interferometer_streaming.md. Optional: 1e8 CPU row and A100 rows (compute authorization). Library candidate unfiled: chunk transformer.image_from in apply_sparse_operator (memory).
-- [Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_large_mask_aris.md) — active: Knowledge written 2026-10-15 (cost structure, routing model, levers, collaborator settings) and the two human decisions recorded. #630/#631/#632 merged, pending release. Phase-2 record at PR-open (autolens_profiling#420): re-pin its table on merge. L1 Cholesky reuse (PyAutoArray#633) in development; L2 direct_conv symmetry filed, unstarted.
+- [Interferometer large-mask likelihood (Aris 512×512): sparse data-vector memory, AdaptSplit log-det backend parity, numba route-gate cost model](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_large_mask_aris.md) — active: Released: #630/#631/#632/#634 in 2026.10.16.1 (2026-10-16). Phase-2 record #420 merged 2026-10-15 (table re-pinned at fd29902); #422/#424 merged 2026-10-16. L2 (PyAutoArray#635) paused 2026-10-16 with v3 (ymin order, gate 0.7) uncommitted in worktree feature/direct-conv-symmetric; synthetic witness PASS 1.81–1.90× (RAL 411835), real-data laptop 1.46× at ymin order; owed on resume: verify v3 + full suite, RAL real-data witness ≥1.3× (data and private library clones staged under scratch/aris_l2_20261016), Heart check, ship_library PR, write-up (results note, Knowledge section, decision record direct-conv-symmetric-gate). κ=1.32 means the #632 cost-model constant for direct_conv is effectively refit when L2 ships.
 - [Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space pixel scale on the A100](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_w_tilde_fft_size_levers.md) — ready: Unstarted (no PR/issue/branch/results, 2026-10-04). Select one bounded step when prioritised.
 
 </details>
 
 <details><summary>Linear-solver accuracy and cost — active</summary>
 
-Reviewed 2026-10-13. 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.
+Reviewed 2026-10-17. 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.
 
 [Campaign evidence](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/decisions/linear-solver-mapper-default-jacobi.md)
 
@@ -121,7 +121,7 @@ Reviewed 2026-10-13. Retain the 2026-10-08 human decision: Jacobi for Mapper, ra
 
 <details><summary>Production imaging and likelihood breakdown — active</summary>
 
-Reviewed 2026-10-13. No new production-over-sampling or post-certified measurements. #408 now adds median headline fields to runtime cells; #412 merged 2026-10-13 and extends remaining headline surfaces. Production presets, memo-blind harness, cumulative MGE steps and quick-update costs remain separate pending work. Scope production-preset phase 1 (CPU smoke only); A100 measurement separately authorized.
+Reviewed 2026-10-17. No new production-over-sampling or post-certified measurements. #408 now adds median headline fields to runtime cells; #412 merged 2026-10-13 and extends remaining headline surfaces. Production presets, memo-blind harness, cumulative MGE steps and quick-update costs remain separate pending work. Scope production-preset phase 1 (CPU smoke only); A100 measurement separately authorized. New related library evidence: PyAutoArray#624/#625 (numba Sibson kernel; DelaunayNN within 2× Delaunay on Aris 1,405) released 2026.10.16.1; follow-up PyAutoArray#629 (imaging DelaunayNN still ~2.7× Delaunay at sub_size 4: unique_mappings 0.30 vs 0.10 s, adaptive_pixel_signals 0.16 vs 0.01 s; laptop, threads=1, load 3–5, not a controlled measurement) is open and its Mind draft expects a Pulse campaign delaunay-nn-cpu that does not yet exist — human decision whether to open one.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/post_certified_breakdown.md)
 
@@ -135,7 +135,7 @@ Reviewed 2026-10-13. No new production-over-sampling or post-certified measureme
 
 <details><summary>Measurement reliability and profiling tools — active</summary>
 
-Reviewed 2026-10-13. 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.
+Reviewed 2026-10-17. Timing audit #410–#412 and raw HST/MGE pilot producer #416 merged; implementation issues #362/#415 closed. No fresh production measurements or scientific acceptance. C1/C11 repeat counts, C5 estimator policy, memo interpretation, fresh runtime repeats and other tools remain pending. Check-in 2026-10-17: autolens_profiling#418 (pilot qualification design) open since 2026-10-13 with CI green and no review; #417 open; no new measurements.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/pull/416)
 
@@ -145,7 +145,7 @@ Reviewed 2026-10-13. Timing audit #410–#412 and raw HST/MGE pilot producer #41
 - [A gradient-cost probe: forward vs `value_and_grad` ms/eval and a strict FD check, on any registry cell](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/gradient_cost_probe.md) — ready: 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.
 - [Numba breakdown harness: perturb the instance so the operated-matrix memo cannot hide a step](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/numba_breakdown_harness_memo_blind.md) — ready: Unchanged and unissued (PyAutoArray#496 closed 2026-08-27). Select one bounded step when prioritised.
 - [Search settings-estimation + profiling infrastructure (n_starts / batch_size / n_batch)](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/search_settings_estimation_infrastructure.md) — ready: Unchanged and unissued (autolens_profiling#82 closed 2026-08-18). Select one bounded step when prioritised.
-- [jax_compile/probe.py lost its cell builder with the searches tier — give profiling its own](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/jax_compile_probe_needs_own_cell_builder.md) — active: 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.
+- [jax_compile/probe.py lost its cell builder with the searches tier — give profiling its own](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/jax_compile_probe_needs_own_cell_builder.md) — active: 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.
 - [profile_lens_aggregator.py cannot run from the autolens_workspace_developer root: no config/ directory](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/profile_lens_aggregator_needs_config_dir.md) — ready: Unchanged: workspace_developer main still has no root config/ (bug presumed live, not re-run). Select one bounded step when prioritised.
 - [jax_profiling/gradient/imaging/pixelization.py: 3.2% of its pin move is unattributed](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/gradient_pixelization_pin_residual_drift.md) — ready: Unchanged and unissued (PyAutoArray#490 merged 2026-08-26). Select one bounded step when prioritised.
 - [Pair JAX/XLA env vars with measured compile and run times, per backend](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pair_jax_xla_env_vars_with_measured.md) — ready: 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.
@@ -154,19 +154,19 @@ Reviewed 2026-10-13. Timing audit #410–#412 and raw HST/MGE pilot producer #41
 
 <details><summary>PyAutoFit profiling bootstrap — active</summary>
 
-Reviewed 2026-10-14. B4a merged (autofit_profiling#3, Pulse#60); B4b at PR (issue autofit_profiling#4) ports the EP / graphical N3/N10/N30 baselines and the analytic parity cell (runtime + memory records, same laptop host as B4a). Truth logL sums and both N30 sanity FAILs reproduce exactly; every wall is faster than the committed developer baseline beyond 2x (improved). Receipt/snapshot refresh after its Pages deploy. Epic 2 not opened.
+Reviewed 2026-10-17. 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.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autofit_profiling/issues/4)
 
 ### Active tasks
 
-- [autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/autofit_profiling_bootstrap.md) — active: B4a merged (autofit_profiling#3, Pulse#60); B4b at PR (issue autofit_profiling#4) ports the EP / graphical N3/N10/N30 baselines and the analytic parity cell (runtime + memory records, same laptop host as B4a). Truth logL sums and both N30 sanity FAILs reproduce exactly; every wall is faster than the committed developer baseline beyond 2x (improved). Receipt/snapshot refresh after its Pages deploy. Epic 2 not opened.
+- [autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/autofit_profiling_bootstrap.md) — active: 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.
 
 </details>
 
 <details><summary>Critical curves and evaluation grids — needs-decision</summary>
 
-Reviewed 2026-10-13. 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.
+Reviewed 2026-10-17. 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.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/critical_curves.md)
 
@@ -177,7 +177,7 @@ Reviewed 2026-10-13. No new campaign measurement. Phase 3b cap fix remains shipp
 
 <details><summary>Certified positive solver — parked</summary>
 
-Reviewed 2026-10-13. 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.
+Reviewed 2026-10-17. 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.
 
 [Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/4db4d988e520a82219152743748c3188d485a4ba/wiki/campaigns/certified_positive_solver.md)
 
diff --git a/tasks/autofit_profiling_bootstrap.md b/tasks/autofit_profiling_bootstrap.md
index 96bada0..8d4c104 100644
--- a/tasks/autofit_profiling_bootstrap.md
+++ b/tasks/autofit_profiling_bootstrap.md
@@ -72,6 +72,12 @@ not refresh measurements or authorize compute. Historical task text is retained.
 - Analytic parity (seed 0, N = 5): PARITY PASS 41/41 for closed form vs minimal EP vs graphical vs EP; closed-form `sigma_q95` 12.1536 matches the banked 12.15; `analytic_gaussian_priors.py` recorded as parked NEEDS_FIX, not run.
 - This Pulse branch refreshes the fixture; the `fit` receipt and snapshot are refreshed here after the autofit_profiling PR merges and its Pages deploy is live. Measurements are `unreviewed`; the Brain's profiling conductor judges.
 
+## Check-in 2026-10-17
+
+- B4b merged: autofit_profiling#5 (issue #4 closed, 2026-10-15 12:22Z) ports the EP / graphical N3/N10/N30 baselines and the analytic parity cell; Pulse#61 added the fit fixture and campaign row (merged 2026-10-15). autofit_profiling#6 repointed RAL paths to the category layout (merged 2026-10-15).
+- Pulse `fit` receipt ok at autofit_profiling `58e56b9` (31 records, 0 comparisons, fetched 2026-10-15T22:58Z): the post-deploy receipt/snapshot refresh owed by the previous check-in is done.
+- No new measurements since B4b. Epic 2 is not opened; the search-extensibility B5c inference work is Insight's, not Pulse's. Next bounded step: a human-scoped epic-2 campaign row.
+
 ---
 
 # autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic
diff --git a/tasks/interferometer_large_mask_aris.md b/tasks/interferometer_large_mask_aris.md
index 98eb745..5b366e3 100644
--- a/tasks/interferometer_large_mask_aris.md
+++ b/tasks/interferometer_large_mask_aris.md
@@ -280,3 +280,13 @@ Not worth it (measured on the 125×125 setup unless stated):
   now routes it automatically.
 - **Leave `interferometer_numba_nnz_per_source_max` unset** (the cost model) unless running an A/B.
   Scripts that set it explicitly keep the old hard-gate meaning.
+
+### Check-in 2026-10-17
+
+- **Released.** PyAutoArray#630, #631, #632 and #634 shipped in 2026.10.16.1 (2026-10-16); 2026.10.16.2 and 2026.10.17.1 contain no further PyAutoArray change. Earlier "pending release" wording above is historical.
+- **Project records.** autolens_profiling#420 (phase-2 routing table under the cost model + log-det parity, issue #419) merged 2026-10-15 22:55Z; #422 (gate-60 wording marked historical, Pulse knowledge linked) and #424 (lint re-pin of abstract.py anchors and shared-Cholesky route probe after #634) merged 2026-10-16. Project main `fd29902`.
+- **L1 (#634), measured on the NumPy path (PyAutoPulse#62 comment 2026-10-15T23:28Z).** solve+log-det 144.5 → 50.9 ms at 1,405 vertices; 1,268 → 373 ms at 2,947; full call −22–24% / −51%. Reconstruction within conditioning (cond 7.7e14), log det bit-identical, logL ≤ 1.1e-11.
+- **L2 (PyAutoArray#635), paused by the human 2026-10-16.** Uncommitted on worktree `feature/direct-conv-symmetric` (base `e59d5fa9`), no PR. Serial numba, `euclid-ral-compute-1`, load < 1, A-C-C-A order, synthetic Aris-512 geometry (341² extent, 91,361 pixels, nnz 274,083): library kernel v2 (RAL 411835) 1.81× / 1.85× / 1.90× at S 1,428 / 2,540 / 5,712, spreads ≤ 1.004, computed entries bit-identical, full matrix differs only by the full kernel's own asymmetry (≤ 1.3e-15). Real Aris data on the laptop: columns are tall (median span 90 of 341 rows, likely multiple imaging), work fraction 0.587 (centroid order) / 0.511 (ymin order); measured full 40.4 s vs 30.5 s / 27.6 s = 1.32× / 1.46×; κ = 1.32 model predicts 1.29× / 1.48×. Jobs 411833/411834/411835 COMPLETED on `ral` (411832 FAILED after 11 s, superseded).
+- **Human decision 2026-10-16 (recorded on #635, not yet a Pulse decision record):** rank columns by ascending ymin; raise the default `interferometer_direct_conv_symmetric_max_work_fraction` from 0.5 to 0.7 (kernel predicted to win while κf < 1, f < 0.76). v3 implementing this is in the worktree diff (gate 0.7, ymin ordering); its test run is unconfirmed.
+- **Owed on resume (from the #635 checkpoint):** verify v3 and the full suites; RAL real-data witness on `ral`, serial (`l2_realdata.py --n-pixels 512 --mesh-step 8 --repeats 2`), gate ≥ 1.3× on the curvature step with matching logL; re-confirm the synthetic witness with ymin order; Heart check; `/ship_library` PR; write-up (autolens_profiling results note with the decision rule, this Knowledge section, decision record `direct-conv-symmetric-gate`). When L2 ships, the #632 `c_dc` constant is effectively scaled by min(1, κf) and the Aris-512 1,459-vertex cell is predicted to flip from FFT to direct_conv.
+- **RAL staging verified 2026-10-17.** `/mnt/ral/jnightin/scratch/aris_l2_20261016/` holds `aris_data/`, `PyAutoArray` and `PyAutoArray_l2` (the v3 patch still to be applied), PyAutoNerves/PyAutoFit/PyAutoGalaxy/PyAutoLens mains, `numba_cache/`, the witness scripts, patches and the 411832–411835 job outputs.