diff --git a/campaigns.yaml b/campaigns.yaml
index 24b5093..11458a8 100644
--- a/campaigns.yaml
+++ b/campaigns.yaml
@@ -1,38 +1,42 @@
 version: 1
-last_checkin: '2026-10-04T17:18:38Z'
+last_checkin: '2026-10-07T18:43:45Z'
 campaigns:
 - id: setup-baseline
   title: Setup baseline collection and scientific acceptance
   status: needs-decision
-  updated: '2026-10-06'
-  next: 'Specification is draft (autolens_profiling#384); no baseline collection or acceptance authorized.
-    Resolve immutable software and dependency lock, dataset hashes/settings, hardware/threads/load and
-    timing/cache/memory methodology/witnesses; freeze the manifest before separately authorizing collection.
-    Compile probe builder remains unavailable; archived timings are context only.'
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/62a6ad1e82735e360c7cf25be62253a286493b17/wiki/campaigns/setup_baseline.md
+  updated: '2026-10-07'
+  next: 'Specification merged as draft v1 (autolens_profiling#385, 2026-10-06; issue #384 closed). Six unknowns
+    remain: no frozen revisions/env lock; no collection authorization/window; hardware/affinity identity unselected;
+    each cell needs complete settings, input hashes, witness + tolerances; compile per-cell builder unavailable;
+    proposed fp64/mixed coverage is not evidence of support. Human freezes the manifest before separately
+    authorizing collection.'
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/setup_baseline.md
 - id: point-source-image-cpu
   title: Point-source image plane · CPU
   status: active
-  updated: '2026-10-04'
-  next: 'Phases 1–4 shipped; PyAutoLens#764 (extent warning) released in 2026.10.4.1. Next: docs-only autolens_profiling
-    PR writing campaign completion evidence and fixing stale wiki/index lines (#763 ''in flight'' → #764 merged).
-    RAL leftover folders present; mirror sync unverified.'
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/point_source_image_plane_cpu.md
+  updated: '2026-10-07'
+  next: 'Phases 1–4 shipped; completion evidence merged (autolens_profiling#373, 2026-10-04). Closing the epic is
+    the human''s call. Owed leftovers (RAL cleanup, test move, register_model grad-zero prompt, CI smoke cells,
+    constant_folding A/B, nopad deletion, quiet re-runs) are in the Mind draft point_source_cpu_campaign_owed_leftovers.md.
+    RAL leftover folders not re-listed 2026-10-07 (unverified).'
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/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: '2026-10-04'
-  next: 'Phase 0+1 shipped (profiling#353 merged 2026-09-30; wiki stale). Forward-mode gradient NaN is unfixed and
-    live in 2026.10.4.1 but only a Mind draft. Human decisions: issue that bug; authorize the autolens_inference
-    image-plane fit measurement.'
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/point_source_gpu_breakdown.md
+  updated: '2026-10-07'
+  next: 'Phase 0+1 shipped. Forward-mode gradient NaN (PyAutoLens#767) fixed by PyAutoLens#768 (merged 2026-10-04)
+    and released in 2026.10.7.1 (correctness). Remaining human decision: authorize the autolens_inference image-plane
+    fit measurement. Heart unit-test timing rose on the rewritten #768 test file; not a controlled cost comparison
+    (gradient_cost_probe is the instrument).'
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/point_source_gpu_breakdown.md
 - id: point-source-source
   title: Point-source source plane
   status: parked
-  updated: '2026-10-04'
-  next: 'Core complete. Nautilus leaf autolens_inference#17 merged 2026-10-02 (wiki still says open). Blackjax parked:
-    gradient-sampler admission not met. Warm-up task awaits the human''s option (a)/(b)/(c).'
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/point_source_source_plane.md
+  updated: '2026-10-07'
+  next: 'Core complete; parked. Nautilus leaf autolens_inference#17 merged 2026-10-02; no gradient-sampler leaf yet,
+    so Blackjax stays parked (admission not met). Warm-up task option (a) merged as autolens_profiling#374 (2026-10-04);
+    imaging cells and a release sweep remain.'
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/point_source_source_plane.md
 - id: cluster-pointsolver
   title: Cluster PointSolver robustness and performance
   status: active
@@ -44,18 +48,21 @@ campaigns:
 - id: interferometer
   title: Interferometer likelihood and streaming
   status: active
-  updated: '2026-10-04'
-  next: 'Last CPU cell ran (RAL job 375978_3 COMPLETED 1:11:36, JSON on RAL, not on main) but misses the ≤1e-3 nat
-    CPU/A100 witness: human decision needed. Streaming phases 3–5 shipped; the scaling measurement needs compute
-    authorization. Curvature, memo and FFT tasks unstarted.'
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/interferometer_likelihood.md
+  updated: '2026-10-07'
+  next: 'Decision matrix complete: last cell merged (autolens_profiling#372, 2026-10-04; 1.59e-3 nat CPU/A100 gap
+    accepted by the human). Streaming scaling phase 1 CPU rows merged (#375); 1e8 CPU and A100 rows need compute
+    authorization. fnnls memo back-off released in 2026.10.7.1 (PyAutoArray#615); the #332 after-measurement is
+    owed. Curvature preload and W~ FFT tasks unstarted.'
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/interferometer_likelihood.md
 - id: linear-solver
   title: Linear-solver accuracy and cost
-  status: blocked
-  updated: '2026-10-04'
-  next: 'Release half met: PyAutoArray#595 is in 2026.10.2.1 and 2026.10.4.1. RAL mirror sync (HPCPullPyAuto, plus
-    a dependency-floor diff) unverified; phase 3 GPU/vmap parity and timing waits on it.'
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/linear_solver_accuracy.md
+  status: ready
+  updated: '2026-10-07'
+  next: 'Human decision 2026-10-07: the shared RAL mirror /mnt/ral/jnightin/PyAuto is NEVER synced (HPCPullPyAuto) while
+    euclid_dr1 depends on it. Phase 3 GPU/vmap parity and timing run from a PRIVATE checkout at tag 2026.10.7.1 (contains
+    PyAutoArray#595) with its own environment; note Nerves at that tag excludes jax 0.10.*, RAL has jax 0.10.2. Queue
+    was empty at ~18:40Z (one probe); mirror HEAD/tag unverified and no longer needed.'
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/linear_solver_accuracy.md
 - id: imaging
   title: Production imaging and likelihood breakdown
   status: active
@@ -67,23 +74,26 @@ campaigns:
 - id: measurement-tools
   title: Measurement reliability and profiling tools
   status: active
-  updated: '2026-10-04'
-  next: 'Only timing_noise_audit is issued (autolens_profiling#362: open, no comments, unassigned); start its audit
-    phase via start_dev. Other nine tasks unchanged and unissued; mass_field superseded confirmed.'
-  evidence: https://github.com/PyAutoLabs/PyAutoPulse/tree/f1801514ddcf2272b832c2b53713cd2257aea5c0/tasks
+  updated: '2026-10-07'
+  next: 'Only timing_noise_audit is issued (autolens_profiling#362: open, 0 comments, unassigned, unchanged since
+    2026-10-02); start its audit phase via start_dev. New related Mind draft draft/bug/autolens_profiling/call_accounting_ci_timing_threshold.md
+    (2026-10-06). Other tasks unchanged and unissued.'
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/issues/362
 - id: autofit
   title: PyAutoFit profiling bootstrap
-  status: needs-decision
-  updated: '2026-10-04'
-  next: PyAutoLabs/autofit_profiling does not exist (gh, 2026-10-04). Human decision pending on repo creation; no
-    producer registration until it exists with ported baselines.
-  evidence: https://github.com/PyAutoLabs/PyAutoPulse/tree/f1801514ddcf2272b832c2b53713cd2257aea5c0/tasks
+  status: ready
+  updated: '2026-10-07'
+  next: PyAutoLabs/autofit_profiling created 2026-10-07 (one Initial commit, LICENSE only, no PRs); the repo-creation
+    decision is answered. Porting/bootstrap unstarted; no producer registration until baselines are ported.
+  evidence: https://github.com/PyAutoLabs/autofit_profiling
 - id: critical-curves
   title: Critical curves and evaluation grids
   status: needs-decision
-  updated: '2026-10-04'
-  next: 'Release gate met: PyAutoGalaxy#646 first released in 2026.10.4.1. 2026-10-04: /prm of autolens_workspace_test#343 was denied by the auto-mode classifier ("Merge Without Review"); the human runs it outside auto mode to close the evaluation-grid-cap-field task.'
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/critical_curves.md
+  updated: '2026-10-07'
+  next: 'Phase 3b cap fix shipped: PyAutoGalaxy#646 released in 2026.10.4.1 and autolens_workspace_test#343 merged
+    2026-10-04. Remaining per the wiki Next line: investigate seed coverage/path completion before cluster engine
+    selection (human picks whether to scope it). Wiki page itself is stale (last edited 2026-10-02, still says #646 open).'
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/critical_curves.md
 - id: certified-solver
   title: Certified positive solver
   status: parked
@@ -98,11 +108,12 @@ tasks:
   status: needs-decision
   priority: high
   path: tasks/setup_baseline_campaign.md
-  updated: '2026-10-06'
-  next: 'Human resolves draft manifest choices and freezes the specification; collection requires separate
-    compute authorization. The report prepares evidence for human scientific review and never promotes
-    baselines. CPU production arrays use ral only; compile capability gap remains open.'
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/62a6ad1e82735e360c7cf25be62253a286493b17/baseline/campaign.json
+  updated: '2026-10-07'
+  next: 'Specification merged as draft v1 (autolens_profiling#385, 2026-10-06). Human resolves the six recorded
+    unknowns and freezes the specification; collection requires separate compute authorization. The report prepares
+    evidence for human scientific review and never promotes baselines. CPU production arrays use ral only.'
+  issue: https://github.com/PyAutoLabs/autolens_profiling/issues/384
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/baseline/campaign.json
 - id: runtime_cell_single_jit_gpu_warmup
   campaign: point-source-source
   title: Runtime cells' A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady
@@ -110,19 +121,23 @@ tasks:
   status: active
   priority: low
   path: tasks/runtime_cell_single_jit_gpu_warmup.md
-  updated: '2026-10-04'
-  next: 'Execution 2026-10-04: human chose option (a). Branch feature/runtime-single-jit-median @87a7fcc (issue autolens_profiling#371) parked at Heart RED. Local CPU witness: single_jit 0.417 ms, median 0.388 ms (p10 0.320, p90 0.545). Imaging cells not yet wired.'
+  updated: '2026-10-07'
+  next: 'Option (a) merged (autolens_profiling#374, 2026-10-04): full_pipeline_single_jit_median_ms (p10/p90) beside
+    unchanged single_jit; GPU headline labelled "first block after compile". Imaging release-sweep cells not wired.
+    No release sweep on main yet (results/ commits since 2026-10-04: #375 streaming rows, a README refactor), so the
+    new field has no sweep rows.'
   issue: https://github.com/PyAutoLabs/autolens_profiling/issues/371
-  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/scripts/point_source_source/likelihood_runtime/source_plane_solved.py
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/scripts/point_source_source/source_plane/likelihood_runtime_solved.py
 - id: timing_noise_audit
   campaign: measurement-tools
   title: Audit timing tests and profiling gates for measurement noise
   status: ready
   priority: high
   path: tasks/timing_noise_audit.md
-  updated: '2026-10-04'
-  next: Issued as autolens_profiling#362 (open, 0 comments, unassigned). Start its read-and-classify audit phase
-    via start_dev.
+  updated: '2026-10-07'
+  next: 'Issued as autolens_profiling#362 (open, 0 comments, unassigned, unchanged since 2026-10-02). Related new
+    Mind draft draft/bug/autolens_profiling/call_accounting_ci_timing_threshold.md (2026-10-06). Start the read-and-classify
+    audit phase via start_dev.'
   issue: https://github.com/PyAutoLabs/autolens_profiling/issues/362
 - id: mge_likelihood_breakdown_steps_are_cumulative_an
   campaign: measurement-tools
@@ -138,9 +153,11 @@ tasks:
   status: ready
   priority: low
   path: tasks/gradient_cost_probe.md
-  updated: '2026-10-04'
-  next: Unchanged and unissued (workspace_developer#117 closed 2026-07-28). Select one bounded step when prioritised.
-  evidence: https://github.com/PyAutoLabs/autolens_workspace_developer/issues/117
+  updated: '2026-10-07'
+  next: '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.'
+  evidence: https://github.com/PyAutoLabs/PyAutoLens/pull/768
 - id: numba_breakdown_harness_memo_blind
   campaign: measurement-tools
   title: 'Numba breakdown harness: perturb the instance so the operated-matrix memo cannot hide a step'
@@ -182,21 +199,24 @@ tasks:
 - id: mge_nnls_fix_pyautoarray_571_slam_60
   campaign: linear-solver
   title: 'Linear-solver programme phase 3: GPU/vmap/A100 timing and parity rows for the solver corpus'
-  status: blocked
+  status: ready
   priority: medium
   path: tasks/mge_nnls_fix_pyautoarray_571_slam_60.md
-  updated: '2026-10-04'
-  next: 'RAL sync SKIPPED 2026-10-04: 180 euclid_dr1 tasks RUNNING on the shared base; mirror at 2026-09-27 state, 16–35 commits behind tag 2026.10.4.1; a sync pulls MAINS and also needs a jax reinstall (Nerves main excludes jax 0.10.2). Next: sync when `squeue -u jnightin -t R` shows no euclid_dr1 jobs on the shared base, or the human accepts the risk.'
+  updated: '2026-10-07'
+  next: 'Human decision 2026-10-07: do not HPCPullPyAuto the shared mirror (euclid_dr1 needs it). Next bounded step:
+    plan the phase-3 A100 cells from a private RAL checkout at tag 2026.10.7.1 with a jax that satisfies Nerves
+    (jax 0.10.* excluded; RAL has 0.10.2), then request compute authorization for the parity/timing rows.'
   evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/595
 - id: autofit_profiling_bootstrap
   campaign: autofit
   title: 'autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic'
-  status: needs-decision
+  status: ready
   priority: normal
   path: tasks/autofit_profiling_bootstrap.md
-  updated: '2026-10-04'
-  next: Human answers the single repo-creation question (PyAutoLabs/autofit_profiling absent, 2026-10-04) before
-    any porting.
+  updated: '2026-10-07'
+  next: PyAutoLabs/autofit_profiling created 2026-10-07 (LICENSE-only Initial commit, no PRs). Bootstrap/porting
+    unstarted; start the first bounded phase via start_dev. No producer registration until baselines are ported.
+  evidence: https://github.com/PyAutoLabs/autofit_profiling
 - id: cluster_pointsolver_speed
   campaign: cluster-pointsolver
   title: Cluster PointSolver — robustness, analysis settings and performance
@@ -220,39 +240,47 @@ tasks:
   campaign: interferometer
   title: Interferometer decision matrix — fill the last cell (CPU rect 39² at alma_high r5.0) and flip the wiki
     row to shipped
-  status: active
+  status: complete
   priority: normal
   path: tasks/interferometer_decision_matrix_last_cell.md
-  updated: '2026-10-04'
-  next: 'Execution 2026-10-04: human decision ACCEPT the 1.59e-3 nat CPU/A100 gap (precedent r3.5 at 1.5e-3). Branch feature/interferometer-decision-matrix-last-cell @93a002a committed locally; ship PARKED at Heart RED (release validation FAILED, stage integrate). Caveat: the A100 row''s own A/B shows certified solver = PDIP to 0.0 nat, so the gap may be F/D on older revisions rather than solver. Correction: PyAutoArray#582 first released 2026.9.27.2. Next: ship once Heart clears.'
+  updated: '2026-10-07'
+  next: 'Complete: autolens_profiling#372 merged 2026-10-04 (RAL 375978_3; 1.59e-3 nat CPU/A100 gap accepted by the
+    human). Every decision-matrix cell is filled. Caveat retained: the gap may be F/D on older revisions rather than solver.'
   issue: https://github.com/PyAutoLabs/autolens_profiling/issues/369
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/interferometer_likelihood.md
 - id: interferometer_fixed_mapper_curvature_preload
   campaign: interferometer
   title: 'Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`'
   status: ready
   priority: normal
   path: tasks/interferometer_fixed_mapper_curvature_preload.md
-  updated: '2026-10-04'
-  next: Phase 1 sliced 2026-10-04 into a Mind draft (PyAutoArray preload plumbing + CPU breakdown lever, no GPU); run it through start_dev. Human science call first - no production SLaM stage holds the mapper fixed today.
+  updated: '2026-10-07'
+  next: Phase 1 Mind draft (2026-10-04) still unstarted; run it through start_dev. Human science call first - no production SLaM stage holds the mapper fixed today.
   evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/55a453cf/draft/feature/autoarray/interferometer_curvature_preload_phase1.md
 - id: interferometer_nnls_memo_scattered_stream_guard
   campaign: interferometer
   title: 'fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)'
-  status: ready
+  status: active
   priority: medium
   path: tasks/interferometer_nnls_memo_scattered_stream_guard.md
-  updated: '2026-10-04'
-  next: Unstarted (no PR/issue/branch/results, 2026-10-04). Select one bounded step when prioritised.
+  updated: '2026-10-07'
+  next: 'Library half shipped: PyAutoArray#615 (closes #613) merged 2026-10-06, in 2026.10.7.1. Local solver-only
+    witness (n=576): iid memo on/off 1.49x → 1.17x; walk 0.18x unchanged. Owed: re-run the autolens_profiling#332
+    ALMA Delaunay 2.17x after-measurement (needs compute authorization).'
+  issue: https://github.com/PyAutoLabs/PyAutoArray/issues/613
+  evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/615
 - id: interferometer_streaming_scaling
   campaign: interferometer
   title: 'Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities'
   status: active
   priority: high
   path: tasks/interferometer_streaming_scaling.md
-  updated: '2026-10-04'
-  next: 'Execution 2026-10-04: branch feature/interferometer-streaming-scaling @4a0ef46 (issue autolens_profiling#368) parked at Heart RED. Laptop CPU 8-thread indicative rows recorded (chunk 65536 to 5e7 vis; in-memory first failure 1e6 under a 10 GB cap; parity 1.2e-9 nats at 5e5; 1e8 skipped). Go/no-go overtaken; recorded as release evidence for 2026.10.4.1. nufft_chunk_size arm hits a second memory wall in transformer.image_from (library candidate, no issue filed).'
+  updated: '2026-10-07'
+  next: 'Phase 1 CPU cells + results/streaming_scaling rows merged (autolens_profiling#375, 2026-10-04); wiki/campaigns/interferometer_streaming.md.
+    Optional: 1e8 CPU row and A100 rows (compute authorization). Library candidate unfiled: chunk transformer.image_from
+    in apply_sparse_operator (memory).'
   issue: https://github.com/PyAutoLabs/autolens_profiling/issues/368
-  evidence: https://github.com/PyAutoLabs/PyAutoArray/pull/601
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/interferometer_streaming.md
 - id: interferometer_w_tilde_fft_size_levers
   campaign: interferometer
   title: 'Interferometer W~ curvature matrix is FFT-bound on the mask extent: pruned padded FFT and real-space pixel
@@ -277,9 +305,11 @@ tasks:
   status: needs-decision
   priority: high
   path: tasks/point_source_image_plane_gpu_breakdown.md
-  updated: '2026-10-04'
-  next: 'Execution 2026-10-04: forward-mode NaN issued as PyAutoLens#767 (reproduced on CPU fp64: forward [nan]x5, reverse finite; stack 2026.10.4.1+3 b695e57b6); Mind registry 0112ccba. The autolens_inference image-plane fit measurement still awaits human authorisation.'
-  evidence: https://github.com/PyAutoLabs/PyAutoLens/issues/767
+  updated: '2026-10-07'
+  next: 'Forward-mode NaN (PyAutoLens#767) closed: fixed by PyAutoLens#768 (merged 2026-10-04, 0659903d4), released
+    in 2026.10.7.1. The autolens_inference image-plane fit measurement still awaits human authorisation.'
+  issue: https://github.com/PyAutoLabs/PyAutoLens/issues/767
+  evidence: https://github.com/PyAutoLabs/PyAutoLens/pull/768
 - id: point_source_source_plane_chi_squared_speed
   campaign: point-source-source
   title: Point-source source-plane chi-squared speed-up campaign — remaining candidates (phases 1–2e shipped)
@@ -296,10 +326,12 @@ tasks:
   status: ready
   priority: low
   path: tasks/pointsolver_cpu_speed_campaign_remainder.md
-  updated: '2026-10-04'
-  next: 'Execution 2026-10-04: docs-only branch feature/point-source-wiki-reconcile @0560284 (issue autolens_profiling#370), parked at Heart RED. Still owed: RAL cleanup, register_model grad-zero prompt, test move, CI smoke cells, nopad deletion.'
+  updated: '2026-10-07'
+  next: 'Completion evidence merged (autolens_profiling#373, 2026-10-04). Still owed, via Mind draft draft/maintenance/autolens/point_source_cpu_campaign_owed_leftovers.md:
+    RAL cleanup, test move, register_model grad-zero prompt, CI smoke cells, constant_folding A/B, nopad deletion,
+    quiet re-runs.'
   issue: https://github.com/PyAutoLabs/autolens_profiling/issues/370
-  evidence: https://github.com/PyAutoLabs/PyAutoLens/pull/764
+  evidence: https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04
 - id: post_certified_solver_likelihood_breakdown
   campaign: imaging
   title: Assess remaining likelihood bottlenecks after certified solver integration on CPU and GPUs
@@ -335,9 +367,9 @@ tasks:
   status: ready
   priority: high
   path: tasks/pair_jax_xla_env_vars_with_measured.md
-  updated: '2026-10-04'
-  next: 'Unchanged and unissued. Context: jax-version policy changes merged to library mains 2026-10-04, unreleased.
-    Select one bounded step when prioritised.'
+  updated: '2026-10-07'
+  next: '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.'
 - id: quick_update_plotting_cost
   campaign: imaging
   title: Quick-update plotting cost — minutes per update, and it is not JAX compile
diff --git a/dashboard.html b/dashboard.html
index ad00aad..9551611 100644
--- a/dashboard.html
+++ b/dashboard.html
@@ -645,7 +645,7 @@
 Work on GitHub:
 - PyAutoPulse: https://github.com/PyAutoLabs/PyAutoPulse
 - autolens_profiling: https://github.com/PyAutoLabs/autolens_profiling</textarea></details><div class="orchestration-freshness" data-freshness="grey"><details data-freshness-stamp data-refreshed-at="2026-10-07T18:28:24Z"><summary><span data-freshness-label>Last updated 2026-10-07 18:28:24 UTC</span></summary><time datetime="2026-10-07T18:28:24Z">2026-10-07 18:28:24 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></nav><main><!-- pulse-campaigns:cbea1eba629dc0064077ad5d1af613005d3a58f8a6d17b54b253eb6468e6d379 --><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>1 task · next step</summary><p>Specification is draft (autolens_profiling#384); no baseline collection or acceptance authorized. Resolve immutable software and dependency lock, dataset hashes/settings, hardware/threads/load and timing/cache/memory methodology/witnesses; freeze the manifest before separately authorizing collection. Compile probe builder remains unavailable; archived timings are context only.</p><span class="review-meta">Reviewed 2026-10-06</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></h4><p class="review-meta">needs-decision · high</p><p>Human resolves draft manifest choices and freezes the specification; collection requires separate compute authorization. The report prepares evidence for human scientific review and never promotes baselines. CPU production arrays use ral only; compile capability gap remains open.</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/62a6ad1e82735e360c7cf25be62253a286493b17/wiki/campaigns/setup_baseline.md" 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>Phases 1–4 shipped; PyAutoLens#764 (extent warning) released in 2026.10.4.1. Next: docs-only autolens_profiling PR writing campaign completion evidence and fixing stale wiki/index lines (#763 &#x27;in flight&#x27; → #764 merged). RAL leftover folders present; mirror sync unverified.</p><span class="review-meta">Reviewed 2026-10-04</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>Execution 2026-10-04: docs-only branch feature/point-source-wiki-reconcile @0560284 (issue autolens_profiling#370), parked at Heart RED. Still owed: RAL cleanup, register_model grad-zero prompt, test move, CI smoke cells, nopad deletion.</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/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/point_source_image_plane_cpu.md" 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>Phase 0+1 shipped (profiling#353 merged 2026-09-30; wiki stale). Forward-mode gradient NaN is unfixed and live in 2026.10.4.1 but only a Mind draft. Human decisions: issue that bug; authorize the autolens_inference image-plane fit measurement.</p><span class="review-meta">Reviewed 2026-10-04</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></h4><p class="review-meta">needs-decision · high</p><p>Execution 2026-10-04: forward-mode NaN issued as PyAutoLens#767 (reproduced on CPU fp64: forward [nan]x5, reverse finite; stack 2026.10.4.1+3 b695e57b6); Mind registry 0112ccba. The autolens_inference image-plane fit measurement still awaits human authorisation.</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/a93f37a7ade16fcb7673a7777dfde505183728ed/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 complete. Nautilus leaf autolens_inference#17 merged 2026-10-02 (wiki still says open). Blackjax parked: gradient-sampler admission not met. Warm-up task awaits the human&#x27;s option (a)/(b)/(c).</p><span class="review-meta">Reviewed 2026-10-04</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>Execution 2026-10-04: human chose option (a). Branch feature/runtime-single-jit-median @87a7fcc (issue autolens_profiling#371) parked at Heart RED. Local CPU witness: single_jit 0.417 ms, median 0.388 ms (p10 0.320, p90 0.545). Imaging cells not yet wired.</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: gradient-sampler admission not met (autolens_inference has only the Nautilus source-plane leaf, #17 merged 2026-10-02). Blackjax stays 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/a93f37a7ade16fcb7673a7777dfde505183728ed/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>Unchanged since 2026-09-27; nothing issued or claimed. Wiki still points at the removed Mind draft (now Pulse tasks/). Pick one bounded phase: observable-overflow invalid-result policy + CI witness, or a representative-data baseline cell.</p><span class="review-meta">Reviewed 2026-10-04</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/a93f37a7ade16fcb7673a7777dfde505183728ed/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>Last CPU cell ran (RAL job 375978_3 COMPLETED 1:11:36, JSON on RAL, not on main) but misses the ≤1e-3 nat CPU/A100 witness: human decision needed. Streaming phases 3–5 shipped; the scaling measurement needs compute authorization. Curvature, memo and FFT tasks unstarted.</p><span class="review-meta">Reviewed 2026-10-04</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_decision_matrix_last_cell.md">Interferometer decision matrix — fill the last cell (CPU rect 39² at alma_high r5.0) and flip the wiki row to shipped</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/369" aria-label="Issue for Interferometer decision matrix — fill the last cell (CPU rect 39² at alma_high r5.0) and flip the wiki row to shipped" title="Issue">↗</a></h4><p class="review-meta">active · normal</p><p>Execution 2026-10-04: human decision ACCEPT the 1.59e-3 nat CPU/A100 gap (precedent r3.5 at 1.5e-3). Branch feature/interferometer-decision-matrix-last-cell @93a002a committed locally; ship PARKED at Heart RED (release validation FAILED, stage integrate). Caveat: the A100 row&#x27;s own A/B shows certified solver = PDIP to 0.0 nat, so the gap may be F/D on older revisions rather than solver. Correction: PyAutoArray#582 first released 2026.9.27.2. Next: ship once Heart clears.</p></article><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 sliced 2026-10-04 into a Mind draft (PyAutoArray preload plumbing + CPU breakdown lever, no GPU); 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></h4><p class="review-meta">ready · medium</p><p>Unstarted (no PR/issue/branch/results, 2026-10-04). Select one bounded step when prioritised.</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>Execution 2026-10-04: branch feature/interferometer-streaming-scaling @4a0ef46 (issue autolens_profiling#368) parked at Heart RED. Laptop CPU 8-thread indicative rows recorded (chunk 65536 to 5e7 vis; in-memory first failure 1e6 under a 10 GB cap; parity 1.2e-9 nats at 5e5; 1e8 skipped). Go/no-go overtaken; recorded as release evidence for 2026.10.4.1. nufft_chunk_size arm hits a second memory wall in transformer.image_from (library candidate, no issue filed).</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/a93f37a7ade16fcb7673a7777dfde505183728ed/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>1 task · next step</summary><p>Release half met: PyAutoArray#595 is in 2026.10.2.1 and 2026.10.4.1. RAL mirror sync (HPCPullPyAuto, plus a dependency-floor diff) unverified; phase 3 GPU/vmap parity and timing waits on it.</p><span class="review-meta">Reviewed 2026-10-04</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/mge_nnls_fix_pyautoarray_571_slam_60.md">Linear-solver programme phase 3: GPU/vmap/A100 timing and parity rows for the solver corpus</a></h4><p class="review-meta">blocked · medium</p><p>RAL sync SKIPPED 2026-10-04: 180 euclid_dr1 tasks RUNNING on the shared base; mirror at 2026-09-27 state, 16–35 commits behind tag 2026.10.4.1; a sync pulls MAINS and also needs a jax reinstall (Nerves main excludes jax 0.10.2). Next: sync when `squeue -u jnightin -t R` shows no euclid_dr1 jobs on the shared base, or the human accepts the risk.</p></article></details></td><td><span class="pill">blocked</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/linear_solver_accuracy.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 results since the pin. Over-sampling phase 1: add --instrument production presets to the JAX Delaunay cells (CPU smoke only); PyAutoArray#606 S/N over-sampling helper (in 2026.10.4.1) is new context. Post-certified Blocked-by is stale (resolved 2026-09-27).</p><span class="review-meta">Reviewed 2026-10-04</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/a93f37a7ade16fcb7673a7777dfde505183728ed/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>9 tasks · next step</summary><p>Only timing_noise_audit is issued (autolens_profiling#362: open, no comments, unassigned); start its audit phase via start_dev. Other nine tasks unchanged and unissued; mass_field superseded confirmed.</p><span class="review-meta">Reviewed 2026-10-04</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/timing_noise_audit.md">Audit timing tests and profiling gates for measurement noise</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/362" aria-label="Issue for Audit timing tests and profiling gates for measurement noise" title="Issue">↗</a></h4><p class="review-meta">ready · high</p><p>Issued as autolens_profiling#362 (open, 0 comments, unassigned). Start its read-and-classify audit phase via start_dev.</p></article><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>Unchanged and unissued (workspace_developer#117 closed 2026-07-28). 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">ready · normal</p><p>Unchanged and unissued; scripts/misc/jax_compile/probe.py still on main (run state unverified). Select one bounded step when prioritised.</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: jax-version policy changes merged to library mains 2026-10-04, unreleased. 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/PyAutoPulse/tree/f1801514ddcf2272b832c2b53713cd2257aea5c0/tasks" 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>PyAutoLabs/autofit_profiling does not exist (gh, 2026-10-04). Human decision pending on repo creation; no producer registration until it exists with ported baselines.</p><span class="review-meta">Reviewed 2026-10-04</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">needs-decision · normal</p><p>Human answers the single repo-creation question (PyAutoLabs/autofit_profiling absent, 2026-10-04) before any porting.</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/PyAutoPulse/tree/f1801514ddcf2272b832c2b53713cd2257aea5c0/tasks" 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>Release gate met: PyAutoGalaxy#646 first released in 2026.10.4.1. 2026-10-04: /prm of autolens_workspace_test#343 was denied by the auto-mode classifier (&quot;Merge Without Review&quot;); the human runs it outside auto mode to close the evaluation-grid-cap-field task.</p><span class="review-meta">Reviewed 2026-10-04</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/a93f37a7ade16fcb7673a7777dfde505183728ed/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 by choice; wiki unchanged since ab1e4fd (2026-09-27). Its three library/workspace Mind drafts remain in Mind. Retain parked; read the project ledger before proposing new work.</p><span class="review-meta">Reviewed 2026-10-04</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/a93f37a7ade16fcb7673a7777dfde505183728ed/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 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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":"719459fefb40c7e5be157f652818dbf923830557","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autolens_profiling/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging 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href="https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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></div></details></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></nav><main><!-- pulse-campaigns:4e0f23962c43df5cbd5160664b545e7fa49d02359e5cc6aa5b67bb5320be8425 --><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>1 task · next step</summary><p>Specification merged as draft v1 (autolens_profiling#385, 2026-10-06; issue #384 closed). Six unknowns remain: no frozen revisions/env lock; no collection authorization/window; hardware/affinity identity unselected; each cell needs complete settings, input hashes, witness + tolerances; compile per-cell builder unavailable; proposed fp64/mixed coverage is not evidence of support. Human freezes the manifest before separately authorizing collection.</p><span class="review-meta">Reviewed 2026-10-07</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>Specification merged as draft v1 (autolens_profiling#385, 2026-10-06). Human resolves the six recorded unknowns and freezes the specification; collection requires separate compute authorization. The report prepares evidence for human scientific review and never promotes baselines. CPU production arrays use ral only.</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/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/setup_baseline.md" 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>Phases 1–4 shipped; completion evidence merged (autolens_profiling#373, 2026-10-04). Closing the epic is the human&#x27;s call. Owed leftovers (RAL cleanup, test move, register_model grad-zero prompt, CI smoke cells, constant_folding A/B, nopad deletion, quiet re-runs) are in the Mind draft point_source_cpu_campaign_owed_leftovers.md. RAL leftover folders not re-listed 2026-10-07 (unverified).</p><span class="review-meta">Reviewed 2026-10-07</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>Completion evidence merged (autolens_profiling#373, 2026-10-04). Still owed, via Mind draft draft/maintenance/autolens/point_source_cpu_campaign_owed_leftovers.md: RAL cleanup, test move, register_model grad-zero prompt, CI smoke cells, constant_folding A/B, nopad deletion, quiet re-runs.</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/719459fefb40c7e5be157f652818dbf923830557/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>Phase 0+1 shipped. Forward-mode gradient NaN (PyAutoLens#767) fixed by PyAutoLens#768 (merged 2026-10-04) and released in 2026.10.7.1 (correctness). Remaining human decision: authorize the autolens_inference image-plane fit measurement. Heart unit-test timing rose on the rewritten #768 test file; not a controlled cost comparison (gradient_cost_probe is the instrument).</p><span class="review-meta">Reviewed 2026-10-07</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>Forward-mode NaN (PyAutoLens#767) closed: fixed by PyAutoLens#768 (merged 2026-10-04, 0659903d4), released in 2026.10.7.1. The autolens_inference image-plane fit measurement still awaits human authorisation.</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/719459fefb40c7e5be157f652818dbf923830557/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 complete; parked. Nautilus leaf autolens_inference#17 merged 2026-10-02; no gradient-sampler leaf yet, so Blackjax stays parked (admission not met). Warm-up task option (a) merged as autolens_profiling#374 (2026-10-04); imaging cells and a release sweep remain.</p><span class="review-meta">Reviewed 2026-10-07</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>Option (a) merged (autolens_profiling#374, 2026-10-04): full_pipeline_single_jit_median_ms (p10/p90) beside unchanged single_jit; GPU headline labelled &quot;first block after compile&quot;. Imaging release-sweep cells not wired. No release sweep on main yet (results/ commits since 2026-10-04: #375 streaming rows, a README refactor), so the new field has no sweep rows.</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: gradient-sampler admission not met (autolens_inference has only the Nautilus source-plane leaf, #17 merged 2026-10-02). Blackjax stays 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/719459fefb40c7e5be157f652818dbf923830557/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>Unchanged since 2026-09-27; nothing issued or claimed. Wiki still points at the removed Mind draft (now Pulse tasks/). Pick one bounded phase: observable-overflow invalid-result policy + CI witness, or a representative-data baseline cell.</p><span class="review-meta">Reviewed 2026-10-04</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/a93f37a7ade16fcb7673a7777dfde505183728ed/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>4 tasks · next step</summary><p>Decision matrix complete: last cell merged (autolens_profiling#372, 2026-10-04; 1.59e-3 nat CPU/A100 gap accepted by the human). Streaming scaling phase 1 CPU rows merged (#375); 1e8 CPU and A100 rows need compute authorization. fnnls memo back-off released in 2026.10.7.1 (PyAutoArray#615); the #332 after-measurement is owed. Curvature preload and W~ FFT tasks unstarted.</p><span class="review-meta">Reviewed 2026-10-07</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_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/719459fefb40c7e5be157f652818dbf923830557/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>1 task · next step</summary><p>Human decision 2026-10-07: the shared RAL mirror /mnt/ral/jnightin/PyAuto is NEVER synced (HPCPullPyAuto) while euclid_dr1 depends on it. Phase 3 GPU/vmap parity and timing run from a PRIVATE checkout at tag 2026.10.7.1 (contains PyAutoArray#595) with its own environment; note Nerves at that tag excludes jax 0.10.*, RAL has jax 0.10.2. Queue was empty at ~18:40Z (one probe); mirror HEAD/tag unverified and no longer needed.</p><span class="review-meta">Reviewed 2026-10-07</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/mge_nnls_fix_pyautoarray_571_slam_60.md">Linear-solver programme phase 3: GPU/vmap/A100 timing and parity rows for the solver corpus</a></h4><p class="review-meta">ready · medium</p><p>Human decision 2026-10-07: do not HPCPullPyAuto the shared mirror (euclid_dr1 needs it). Next bounded step: plan the phase-3 A100 cells from a private RAL checkout at tag 2026.10.7.1 with a jax that satisfies Nerves (jax 0.10.* excluded; RAL has 0.10.2), then request compute authorization for the parity/timing rows.</p></article></details></td><td><span class="pill">ready</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/linear_solver_accuracy.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 results since the pin. Over-sampling phase 1: add --instrument production presets to the JAX Delaunay cells (CPU smoke only); PyAutoArray#606 S/N over-sampling helper (in 2026.10.4.1) is new context. Post-certified Blocked-by is stale (resolved 2026-09-27).</p><span class="review-meta">Reviewed 2026-10-04</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/a93f37a7ade16fcb7673a7777dfde505183728ed/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>9 tasks · next step</summary><p>Only timing_noise_audit is issued (autolens_profiling#362: open, 0 comments, unassigned, unchanged since 2026-10-02); start its audit phase via start_dev. New related Mind draft draft/bug/autolens_profiling/call_accounting_ci_timing_threshold.md (2026-10-06). Other tasks unchanged and unissued.</p><span class="review-meta">Reviewed 2026-10-07</span><h3>Active tasks</h3><article class="campaign-task"><h4><a href="https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/timing_noise_audit.md">Audit timing tests and profiling gates for measurement noise</a><a class="icon-link" href="https://github.com/PyAutoLabs/autolens_profiling/issues/362" aria-label="Issue for Audit timing tests and profiling gates for measurement noise" title="Issue">↗</a></h4><p class="review-meta">ready · high</p><p>Issued as autolens_profiling#362 (open, 0 comments, unassigned, unchanged since 2026-10-02). Related new Mind draft draft/bug/autolens_profiling/call_accounting_ci_timing_threshold.md (2026-10-06). Start the read-and-classify audit phase via start_dev.</p></article><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">ready · normal</p><p>Unchanged and unissued; scripts/misc/jax_compile/probe.py still on main (run state unverified). Select one bounded step when prioritised.</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/issues/362" 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>PyAutoLabs/autofit_profiling created 2026-10-07 (one Initial commit, LICENSE only, no PRs); the repo-creation decision is answered. Porting/bootstrap unstarted; no producer registration until baselines are ported.</p><span class="review-meta">Reviewed 2026-10-07</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">ready · normal</p><p>PyAutoLabs/autofit_profiling created 2026-10-07 (LICENSE-only Initial commit, no PRs). Bootstrap/porting unstarted; start the first bounded phase via start_dev. No producer registration until baselines are ported.</p></article></details></td><td><span class="pill">ready</span></td><td><div class="link-icons"><a class="icon-link" href="https://github.com/PyAutoLabs/autofit_profiling" 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>Phase 3b cap fix shipped: PyAutoGalaxy#646 released in 2026.10.4.1 and autolens_workspace_test#343 merged 2026-10-04. Remaining per the wiki Next line: investigate seed coverage/path completion before cluster engine selection (human picks whether to scope it). Wiki page itself is stale (last edited 2026-10-02, still says #646 open).</p><span class="review-meta">Reviewed 2026-10-07</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/719459fefb40c7e5be157f652818dbf923830557/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 by choice; wiki unchanged since ab1e4fd (2026-09-27). Its three library/workspace Mind drafts remain in Mind. Retain parked; read the project ledger before proposing new work.</p><span class="review-meta">Reviewed 2026-10-04</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/a93f37a7ade16fcb7673a7777dfde505183728ed/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 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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":"719459fefb40c7e5be157f652818dbf923830557","shard_base":"https://raw.githubusercontent.com/PyAutoLabs/autolens_profiling/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/results/breakdown/imaging/delaunay_hpc_a100_fp64_walk_early_exit.json">imaging 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href="https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json">imaging / mge / hst — scripts/misc/jax_compile/results/local_gpu_NVIDIA_A100_80GB_PCIe/mge.json</a></li><li><a href="https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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/719459fefb40c7e5be157f652818dbf923830557/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></div></details></main><script>const MAX_PROMPT_CHARS = 50000;
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diff --git a/dashboard.md b/dashboard.md
index d854d07..a874ed0 100644
--- a/dashboard.md
+++ b/dashboard.md
@@ -1,6 +1,6 @@
 # PyAutoPulse — profiling dashboard
 
-<!-- pulse-campaigns:cbea1eba629dc0064077ad5d1af613005d3a58f8a6d17b54b253eb6468e6d379 -->
+<!-- pulse-campaigns:4e0f23962c43df5cbd5160664b545e7fa49d02359e5cc6aa5b67bb5320be8425 -->
 ## Profiling Check In
 
 <details><summary>Full check-in prompt</summary>
@@ -25,55 +25,55 @@ After taking action, report what changed, what the evidence supports and what re
 
 </details>
 
-Last check-in: 2026-10-04T17:18:38Z (review date, not measurement freshness).
+Last check-in: 2026-10-07T18:43:45Z (review date, not measurement freshness).
 
 ## Active campaigns
 
 <details><summary>Setup baseline collection and scientific acceptance — needs-decision</summary>
 
-Reviewed 2026-10-06. Specification is draft (autolens_profiling#384); no baseline collection or acceptance authorized. Resolve immutable software and dependency lock, dataset hashes/settings, hardware/threads/load and timing/cache/memory methodology/witnesses; freeze the manifest before separately authorizing collection. Compile probe builder remains unavailable; archived timings are context only.
+Reviewed 2026-10-07. Specification merged as draft v1 (autolens_profiling#385, 2026-10-06; issue #384 closed). Six unknowns remain: no frozen revisions/env lock; no collection authorization/window; hardware/affinity identity unselected; each cell needs complete settings, input hashes, witness + tolerances; compile per-cell builder unavailable; proposed fp64/mixed coverage is not evidence of support. Human freezes the manifest before separately authorizing collection.
 
-[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/62a6ad1e82735e360c7cf25be62253a286493b17/wiki/campaigns/setup_baseline.md)
+[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/setup_baseline.md)
 
 ### Active tasks
 
-- [Freeze the setup baseline specification, then collect and review evidence](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/setup_baseline_campaign.md) — needs-decision: Human resolves draft manifest choices and freezes the specification; collection requires separate compute authorization. The report prepares evidence for human scientific review and never promotes baselines. CPU production arrays use ral only; compile capability gap remains open.
+- [Freeze the setup baseline specification, then collect and review evidence](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/setup_baseline_campaign.md) — needs-decision: Specification merged as draft v1 (autolens_profiling#385, 2026-10-06). Human resolves the six recorded unknowns and freezes the specification; collection requires separate compute authorization. The report prepares evidence for human scientific review and never promotes baselines. CPU production arrays use ral only.
 
 </details>
 
 <details><summary>Point-source image plane · CPU — active</summary>
 
-Reviewed 2026-10-04. Phases 1–4 shipped; PyAutoLens#764 (extent warning) released in 2026.10.4.1. Next: docs-only autolens_profiling PR writing campaign completion evidence and fixing stale wiki/index lines (#763 &#x27;in flight&#x27; → #764 merged). RAL leftover folders present; mirror sync unverified.
+Reviewed 2026-10-07. Phases 1–4 shipped; completion evidence merged (autolens_profiling#373, 2026-10-04). Closing the epic is the human&#x27;s call. Owed leftovers (RAL cleanup, test move, register_model grad-zero prompt, CI smoke cells, constant_folding A/B, nopad deletion, quiet re-runs) are in the Mind draft point_source_cpu_campaign_owed_leftovers.md. RAL leftover folders not re-listed 2026-10-07 (unverified).
 
-[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/point_source_image_plane_cpu.md)
+[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04)
 
 ### Active tasks
 
-- [Point-source (single-source) CPU campaign — carried leftovers and completion evidence](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pointsolver_cpu_speed_campaign_remainder.md) — ready: Execution 2026-10-04: docs-only branch feature/point-source-wiki-reconcile @0560284 (issue autolens_profiling#370), parked at Heart RED. Still owed: RAL cleanup, register_model grad-zero prompt, test move, CI smoke cells, nopad deletion.
+- [Point-source (single-source) CPU campaign — carried leftovers and completion evidence](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/pointsolver_cpu_speed_campaign_remainder.md) — ready: Completion evidence merged (autolens_profiling#373, 2026-10-04). Still owed, via Mind draft draft/maintenance/autolens/point_source_cpu_campaign_owed_leftovers.md: RAL cleanup, test move, register_model grad-zero prompt, CI smoke cells, constant_folding A/B, nopad deletion, quiet re-runs.
 
 </details>
 
 <details><summary>Point-source image plane · A100 — needs-decision</summary>
 
-Reviewed 2026-10-04. Phase 0+1 shipped (profiling#353 merged 2026-09-30; wiki stale). Forward-mode gradient NaN is unfixed and live in 2026.10.4.1 but only a Mind draft. Human decisions: issue that bug; authorize the autolens_inference image-plane fit measurement.
+Reviewed 2026-10-07. Phase 0+1 shipped. Forward-mode gradient NaN (PyAutoLens#767) fixed by PyAutoLens#768 (merged 2026-10-04) and released in 2026.10.7.1 (correctness). Remaining human decision: authorize the autolens_inference image-plane fit measurement. Heart unit-test timing rose on the rewritten #768 test file; not a controlled cost comparison (gradient_cost_probe is the instrument).
 
-[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/point_source_gpu_breakdown.md)
+[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/point_source_gpu_breakdown.md)
 
 ### Active tasks
 
-- [Point-source A100 speed-up campaign: profile and optimize with the shared breakdown](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_image_plane_gpu_breakdown.md) — needs-decision: Execution 2026-10-04: forward-mode NaN issued as PyAutoLens#767 (reproduced on CPU fp64: forward [nan]x5, reverse finite; stack 2026.10.4.1+3 b695e57b6); Mind registry 0112ccba. The autolens_inference image-plane fit measurement still awaits human authorisation.
+- [Point-source A100 speed-up campaign: profile and optimize with the shared breakdown](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_image_plane_gpu_breakdown.md) — needs-decision: Forward-mode NaN (PyAutoLens#767) closed: fixed by PyAutoLens#768 (merged 2026-10-04, 0659903d4), released in 2026.10.7.1. The autolens_inference image-plane fit measurement still awaits human authorisation.
 
 </details>
 
 <details><summary>Point-source source plane — parked</summary>
 
-Reviewed 2026-10-04. Core complete. Nautilus leaf autolens_inference#17 merged 2026-10-02 (wiki still says open). Blackjax parked: gradient-sampler admission not met. Warm-up task awaits the human&#x27;s option (a)/(b)/(c).
+Reviewed 2026-10-07. Core complete; parked. Nautilus leaf autolens_inference#17 merged 2026-10-02; no gradient-sampler leaf yet, so Blackjax stays parked (admission not met). Warm-up task option (a) merged as autolens_profiling#374 (2026-10-04); imaging cells and a release sweep remain.
 
-[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/point_source_source_plane.md)
+[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/point_source_source_plane.md)
 
 ### Active tasks
 
-- [Runtime cells&#x27; A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/runtime_cell_single_jit_gpu_warmup.md) — active: Execution 2026-10-04: human chose option (a). Branch feature/runtime-single-jit-median @87a7fcc (issue autolens_profiling#371) parked at Heart RED. Local CPU witness: single_jit 0.417 ms, median 0.388 ms (p10 0.320, p90 0.545). Imaging cells not yet wired.
+- [Runtime cells&#x27; A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/runtime_cell_single_jit_gpu_warmup.md) — active: Option (a) merged (autolens_profiling#374, 2026-10-04): full_pipeline_single_jit_median_ms (p10/p90) beside unchanged single_jit; GPU headline labelled &quot;first block after compile&quot;. Imaging release-sweep cells not wired. No release sweep on main yet (results/ commits since 2026-10-04: #375 streaming rows, a README refactor), so the new field has no sweep rows.
 - [Point-source source-plane chi-squared speed-up campaign — remaining candidates (phases 1–2e shipped)](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/point_source_source_plane_chi_squared_speed.md) — blocked: Still blocked: gradient-sampler admission not met (autolens_inference has only the Nautilus source-plane leaf, #17 merged 2026-10-02). Blackjax stays parked.
 
 </details>
@@ -93,29 +93,28 @@ Reviewed 2026-10-04. Unchanged since 2026-09-27; nothing issued or claimed. Wiki
 
 <details><summary>Interferometer likelihood and streaming — active</summary>
 
-Reviewed 2026-10-04. Last CPU cell ran (RAL job 375978_3 COMPLETED 1:11:36, JSON on RAL, not on main) but misses the ≤1e-3 nat CPU/A100 witness: human decision needed. Streaming phases 3–5 shipped; the scaling measurement needs compute authorization. Curvature, memo and FFT tasks unstarted.
+Reviewed 2026-10-07. Decision matrix complete: last cell merged (autolens_profiling#372, 2026-10-04; 1.59e-3 nat CPU/A100 gap accepted by the human). Streaming scaling phase 1 CPU rows merged (#375); 1e8 CPU and A100 rows need compute authorization. fnnls memo back-off released in 2026.10.7.1 (PyAutoArray#615); the #332 after-measurement is owed. Curvature preload and W~ FFT tasks unstarted.
 
-[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/interferometer_likelihood.md)
+[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/interferometer_likelihood.md)
 
 ### Active tasks
 
-- [Interferometer decision matrix — fill the last cell (CPU rect 39² at alma_high r5.0) and flip the wiki row to shipped](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/interferometer_decision_matrix_last_cell.md) — active: Execution 2026-10-04: human decision ACCEPT the 1.59e-3 nat CPU/A100 gap (precedent r3.5 at 1.5e-3). Branch feature/interferometer-decision-matrix-last-cell @93a002a committed locally; ship PARKED at Heart RED (release validation FAILED, stage integrate). Caveat: the A100 row&#x27;s own A/B shows certified solver = PDIP to 0.0 nat, so the gap may be F/D on older revisions rather than solver. Correction: PyAutoArray#582 first released 2026.9.27.2. Next: ship once Heart clears.
-- [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 sliced 2026-10-04 into a Mind draft (PyAutoArray preload plumbing + CPU breakdown lever, no GPU); 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) — ready: Unstarted (no PR/issue/branch/results, 2026-10-04). Select one bounded step when prioritised.
-- [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: Execution 2026-10-04: branch feature/interferometer-streaming-scaling @4a0ef46 (issue autolens_profiling#368) parked at Heart RED. Laptop CPU 8-thread indicative rows recorded (chunk 65536 to 5e7 vis; in-memory first failure 1e6 under a 10 GB cap; parity 1.2e-9 nats at 5e5; 1e8 skipped). Go/no-go overtaken; recorded as release evidence for 2026.10.4.1. nufft_chunk_size arm hits a second memory wall in transformer.image_from (library candidate, no issue filed).
+- [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 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 — blocked</summary>
+<details><summary>Linear-solver accuracy and cost — ready</summary>
 
-Reviewed 2026-10-04. Release half met: PyAutoArray#595 is in 2026.10.2.1 and 2026.10.4.1. RAL mirror sync (HPCPullPyAuto, plus a dependency-floor diff) unverified; phase 3 GPU/vmap parity and timing waits on it.
+Reviewed 2026-10-07. Human decision 2026-10-07: the shared RAL mirror /mnt/ral/jnightin/PyAuto is NEVER synced (HPCPullPyAuto) while euclid_dr1 depends on it. Phase 3 GPU/vmap parity and timing run from a PRIVATE checkout at tag 2026.10.7.1 (contains PyAutoArray#595) with its own environment; note Nerves at that tag excludes jax 0.10.*, RAL has jax 0.10.2. Queue was empty at ~18:40Z (one probe); mirror HEAD/tag unverified and no longer needed.
 
-[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/linear_solver_accuracy.md)
+[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/linear_solver_accuracy.md)
 
 ### Active tasks
 
-- [Linear-solver programme phase 3: GPU/vmap/A100 timing and parity rows for the solver corpus](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/mge_nnls_fix_pyautoarray_571_slam_60.md) — blocked: RAL sync SKIPPED 2026-10-04: 180 euclid_dr1 tasks RUNNING on the shared base; mirror at 2026-09-27 state, 16–35 commits behind tag 2026.10.4.1; a sync pulls MAINS and also needs a jax reinstall (Nerves main excludes jax 0.10.2). Next: sync when `squeue -u jnightin -t R` shows no euclid_dr1 jobs on the shared base, or the human accepts the risk.
+- [Linear-solver programme phase 3: GPU/vmap/A100 timing and parity rows for the solver corpus](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/mge_nnls_fix_pyautoarray_571_slam_60.md) — ready: Human decision 2026-10-07: do not HPCPullPyAuto the shared mirror (euclid_dr1 needs it). Next bounded step: plan the phase-3 A100 cells from a private RAL checkout at tag 2026.10.7.1 with a jax that satisfies Nerves (jax 0.10.* excluded; RAL has 0.10.2), then request compute authorization for the parity/timing rows.
 
 </details>
 
@@ -135,41 +134,41 @@ Reviewed 2026-10-04. No new results since the pin. Over-sampling phase 1: add --
 
 <details><summary>Measurement reliability and profiling tools — active</summary>
 
-Reviewed 2026-10-04. Only timing_noise_audit is issued (autolens_profiling#362: open, no comments, unassigned); start its audit phase via start_dev. Other nine tasks unchanged and unissued; mass_field superseded confirmed.
+Reviewed 2026-10-07. Only timing_noise_audit is issued (autolens_profiling#362: open, 0 comments, unassigned, unchanged since 2026-10-02); start its audit phase via start_dev. New related Mind draft draft/bug/autolens_profiling/call_accounting_ci_timing_threshold.md (2026-10-06). Other tasks unchanged and unissued.
 
-[Campaign evidence](https://github.com/PyAutoLabs/PyAutoPulse/tree/f1801514ddcf2272b832c2b53713cd2257aea5c0/tasks)
+[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/issues/362)
 
 ### Active tasks
 
-- [Audit timing tests and profiling gates for measurement noise](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/timing_noise_audit.md) — ready: Issued as autolens_profiling#362 (open, 0 comments, unassigned). Start its read-and-classify audit phase via start_dev.
+- [Audit timing tests and profiling gates for measurement noise](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/timing_noise_audit.md) — ready: Issued as autolens_profiling#362 (open, 0 comments, unassigned, unchanged since 2026-10-02). Related new Mind draft draft/bug/autolens_profiling/call_accounting_ci_timing_threshold.md (2026-10-06). Start the read-and-classify audit phase via start_dev.
 - [MGE likelihood_breakdown steps are cumulative and `linear_gaussians` is reported as 0](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/mge_likelihood_breakdown_steps_are_cumulative_an.md) — ready: Unchanged and unissued (no GitHub refs, 2026-10-04). Select one bounded step when measurement-tools is prioritised.
-- [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: Unchanged and unissued (workspace_developer#117 closed 2026-07-28). Select one bounded step when prioritised.
+- [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) — ready: Unchanged and unissued; scripts/misc/jax_compile/probe.py still on main (run state unverified). Select one bounded step when prioritised.
 - [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: jax-version policy changes merged to library mains 2026-10-04, unreleased. 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.
 
 </details>
 
-<details><summary>PyAutoFit profiling bootstrap — needs-decision</summary>
+<details><summary>PyAutoFit profiling bootstrap — ready</summary>
 
-Reviewed 2026-10-04. PyAutoLabs/autofit_profiling does not exist (gh, 2026-10-04). Human decision pending on repo creation; no producer registration until it exists with ported baselines.
+Reviewed 2026-10-07. PyAutoLabs/autofit_profiling created 2026-10-07 (one Initial commit, LICENSE only, no PRs); the repo-creation decision is answered. Porting/bootstrap unstarted; no producer registration until baselines are ported.
 
-[Campaign evidence](https://github.com/PyAutoLabs/PyAutoPulse/tree/f1801514ddcf2272b832c2b53713cd2257aea5c0/tasks)
+[Campaign evidence](https://github.com/PyAutoLabs/autofit_profiling)
 
 ### Active tasks
 
-- [autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/autofit_profiling_bootstrap.md) — needs-decision: Human answers the single repo-creation question (PyAutoLabs/autofit_profiling absent, 2026-10-04) before any porting.
+- [autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic](https://github.com/PyAutoLabs/PyAutoPulse/blob/main/tasks/autofit_profiling_bootstrap.md) — ready: PyAutoLabs/autofit_profiling created 2026-10-07 (LICENSE-only Initial commit, no PRs). Bootstrap/porting unstarted; start the first bounded phase via start_dev. No producer registration until baselines are ported.
 
 </details>
 
 <details><summary>Critical curves and evaluation grids — needs-decision</summary>
 
-Reviewed 2026-10-04. Release gate met: PyAutoGalaxy#646 first released in 2026.10.4.1. 2026-10-04: /prm of autolens_workspace_test#343 was denied by the auto-mode classifier (&quot;Merge Without Review&quot;); the human runs it outside auto mode to close the evaluation-grid-cap-field task.
+Reviewed 2026-10-07. Phase 3b cap fix shipped: PyAutoGalaxy#646 released in 2026.10.4.1 and autolens_workspace_test#343 merged 2026-10-04. Remaining per the wiki Next line: investigate seed coverage/path completion before cluster engine selection (human picks whether to scope it). Wiki page itself is stale (last edited 2026-10-02, still says #646 open).
 
-[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/a93f37a7ade16fcb7673a7777dfde505183728ed/wiki/campaigns/critical_curves.md)
+[Campaign evidence](https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/critical_curves.md)
 
 ### Active tasks
 
diff --git a/tasks/autofit_profiling_bootstrap.md b/tasks/autofit_profiling_bootstrap.md
index 2cd1ae8..c086894 100644
--- a/tasks/autofit_profiling_bootstrap.md
+++ b/tasks/autofit_profiling_bootstrap.md
@@ -8,6 +8,11 @@ The original task below is retained verbatim. Current scheduling state lives in
 - VERIFIED: `gh repo view PyAutoLabs/autofit_profiling` → does not exist; no local checkout. The Mind source draft was removed in Mind `dbbe5b22` (2026-10-03, move to Pulse); it is not in Mind complete/ or active.md, so this file is the live record.
 - Human decision still pending: the repo-creation question (Witness). No producer registration until the repo exists with ported baselines.
 
+## Check-in 2026-10-07
+
+- https://github.com/PyAutoLabs/autofit_profiling created 2026-10-07T11:38Z: one Initial commit with LICENSE only, no PRs. The repo-creation question is answered.
+- Status → `ready`: porting/bootstrap unstarted; no producer registration until baselines are ported.
+
 ---
 
 # autofit_profiling: bootstrap the repo + general PyAutoFit profiling epic
diff --git a/tasks/gradient_cost_probe.md b/tasks/gradient_cost_probe.md
index ba5f5be..c88ee04 100644
--- a/tasks/gradient_cost_probe.md
+++ b/tasks/gradient_cost_probe.md
@@ -8,6 +8,10 @@ The original task below is retained verbatim. Current scheduling state lives in
 - VERIFIED: [autolens_workspace_developer#117](https://github.com/PyAutoLabs/autolens_workspace_developer/issues/117) closed 2026-07-28; no change since the pin. The Mind source draft was removed in Mind `dbbe5b22` (2026-10-03, move to Pulse); it is not in Mind complete/ or active.md, so this file is the live record.
 - Next: select one bounded step when prioritised.
 
+## Check-in 2026-10-07
+
+- Context: Heart unit-test timing (PyAutoLens py3.13, 2026-10-07) flagged test__forward_gradient_matches_reverse_through_fitness 5.03→12.60 s and test__multi_start_gradient_point_source_fit_runs_in_forward_mode 15.51→30.38 s. https://github.com/PyAutoLabs/PyAutoLens/pull/768 rewrote that test file, so this is not a controlled library-cost comparison; this probe is the right instrument. Unissued.
+
 ---
 
 # A gradient-cost probe: forward vs `value_and_grad` ms/eval and a strict FD check, on any registry cell
diff --git a/tasks/interferometer_decision_matrix_last_cell.md b/tasks/interferometer_decision_matrix_last_cell.md
index a50bbb4..01718d5 100644
--- a/tasks/interferometer_decision_matrix_last_cell.md
+++ b/tasks/interferometer_decision_matrix_last_cell.md
@@ -18,6 +18,12 @@ The original task below is retained verbatim. Current scheduling state lives in
 - Caveat (executing agent): the A100 row's own A/B shows certified solver = PDIP to 0.0 nat, so the gap may come from F/D on older revisions rather than the solver. Not verified.
 - Correction: PyAutoArray#582 was first released in 2026.9.27.2.
 
+## Check-in 2026-10-07
+
+- https://github.com/PyAutoLabs/autolens_profiling/pull/372 (issue #369) merged 2026-10-04T20:40Z: CPU rect 39² alma_high r5.0 from RAL 375978_3; the human accepted the 1.59e-3 nat CPU/A100 gap on 2026-10-04.
+- Wiki Next line confirms every decision-matrix cell is measured, blocked or not measured with a citation (https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/interferometer_likelihood.md).
+- Status → `complete`.
+
 ---
 
 # Interferometer decision matrix — fill the last cell (CPU rect 39² at alma_high r5.0) and flip the wiki row to shipped
diff --git a/tasks/interferometer_fixed_mapper_curvature_preload.md b/tasks/interferometer_fixed_mapper_curvature_preload.md
index 318f069..0d2122e 100644
--- a/tasks/interferometer_fixed_mapper_curvature_preload.md
+++ b/tasks/interferometer_fixed_mapper_curvature_preload.md
@@ -18,6 +18,10 @@ The original task below is retained verbatim. Current scheduling state lives in
 - Finding for the human: no production stage qualifies as configured - interferometer SLaM `source_pix_2` fixes the mass but leaves `weight_power`/`weight_floor` free, which change the mapping. Phase 1 measures a regularization-only configuration; adopting such a stage is a science decision.
 - Later phases: search-side opt-in (`fixed_mapper=True`), JAX jit/vmap + compile size, A100/jvla rows, data-vector preload, SLaM regularization-only stage, mixed MGE+mapper, dense path.
 
+## Check-in 2026-10-07
+
+- Mind draft `draft/feature/autoarray/interferometer_curvature_preload_phase1.md` (2026-10-04) still unstarted.
+
 ---
 
 # Interferometer fixed-mapper searches: reuse the W~ curvature matrix across likelihood calls via `preloads.curvature_matrix`
diff --git a/tasks/interferometer_nnls_memo_scattered_stream_guard.md b/tasks/interferometer_nnls_memo_scattered_stream_guard.md
index 5a29900..65387f5 100644
--- a/tasks/interferometer_nnls_memo_scattered_stream_guard.md
+++ b/tasks/interferometer_nnls_memo_scattered_stream_guard.md
@@ -8,6 +8,12 @@ The original task below is retained verbatim. Current scheduling state lives in
 - VERIFIED 2026-10-04: no PR, issue, branch or results on GitHub; unstarted. No Mind active claim.
 - Next: select one bounded step when the interferometer campaign prioritises it.
 
+## Check-in 2026-10-07
+
+- Correction: the 2026-10-04 "unstarted" was superseded. https://github.com/PyAutoLabs/PyAutoArray/pull/615 (closes PyAutoArray#613) "back off the fnnls warm-start memo on scattered evaluation streams" merged 2026-10-06 (58bdda0a), released in 2026.10.7.1. Mind record complete/2026/10/nnls-memo-scattered-backoff.md.
+- Local witness (n=576, solver-only, min of 3, 3 seeds, 64 solves): iid memo on/off 1.49x → 1.17x; walk 0.18x unchanged.
+- Status → `active`: the autolens_profiling#332 ALMA Delaunay 2.17x after-measurement was not re-run and needs compute authorization.
+
 ---
 
 # fnnls warm-start memo: stop it slowing scattered evaluation streams (interferometer CPU, autolens_profiling#332)
diff --git a/tasks/interferometer_streaming_scaling.md b/tasks/interferometer_streaming_scaling.md
index aef6a73..60d8b86 100644
--- a/tasks/interferometer_streaming_scaling.md
+++ b/tasks/interferometer_streaming_scaling.md
@@ -19,6 +19,11 @@ The original task below is retained verbatim. Current scheduling state lives in
 - Parity 1.2e-9 nats at 5e5. The 1e8 row was skipped (budget).
 - Go/no-go overtaken; recorded as release evidence for 2026.10.4.1.
 
+## Check-in 2026-10-07
+
+- https://github.com/PyAutoLabs/autolens_profiling/pull/375 (issue #368) merged 2026-10-04T20:49Z: phase 1 CPU cells + results/streaming_scaling rows; https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/interferometer_streaming.md.
+- Optional next: 1e8 CPU row and A100 rows (compute authorization). Library candidate still unfiled: chunk transformer.image_from in apply_sparse_operator.
+
 ---
 
 # Campaign: interferometer streaming (array-free) vs in-memory — memory and time scaling to 2e8 visibilities
diff --git a/tasks/mge_nnls_fix_pyautoarray_571_slam_60.md b/tasks/mge_nnls_fix_pyautoarray_571_slam_60.md
index c890f0d..4df0de9 100644
--- a/tasks/mge_nnls_fix_pyautoarray_571_slam_60.md
+++ b/tasks/mge_nnls_fix_pyautoarray_571_slam_60.md
@@ -17,6 +17,12 @@ The original task below is retained verbatim. Current scheduling state lives in
 - HPCPullPyAuto pulls MAINS, and Nerves main excludes jax 0.10.2 (installed on RAL and locally), so a sync also needs a jax reinstall.
 - Next: sync when `squeue -u jnightin -t R` shows no euclid_dr1 jobs on the shared base, or the human accepts the risk.
 
+## Check-in 2026-10-07
+
+- RAL 2026-10-07 ~18:40Z: `squeue -u jnightin` showed 0 RUNNING, 0 PENDING (one probe). The 2026-10-04 blocker condition (no euclid_dr1 jobs on the shared base) is met.
+- Three later probes timed out: shared mirror /mnt/ral/jnightin/PyAuto HEAD/tag UNVERIFIED.
+- Human decision (same day, in chat): **never** HPCPullPyAuto the shared mirror while euclid_dr1 depends on it. Phase 3 runs from a private RAL checkout at tag 2026.10.7.1 (contains PyAutoArray#595) with its own environment; Nerves at that tag excludes jax 0.10.* and RAL has jax 0.10.2, so the private env needs a compliant jax. Status → `ready`; compute for the A100 rows still needs separate authorization.
+
 ---
 
 # Linear-solver programme phase 3: GPU/vmap/A100 timing and parity rows for the solver corpus
diff --git a/tasks/pair_jax_xla_env_vars_with_measured.md b/tasks/pair_jax_xla_env_vars_with_measured.md
index c9d463a..757240a 100644
--- a/tasks/pair_jax_xla_env_vars_with_measured.md
+++ b/tasks/pair_jax_xla_env_vars_with_measured.md
@@ -9,6 +9,10 @@ Original task retained verbatim below. Current scheduling state: `campaigns.yaml
 - Context: jax-version policy changes (Mind `complete/2026/10/jax-lapack-compatibility-repair.md`) merged to library mains 2026-10-04, unreleased.
 - Next: select one bounded step when prioritised.
 
+## Check-in 2026-10-07
+
+- Context: the jax-version policy is now released; all 2026.10.7.1 libraries require Nerves with the JAX deadlock exclusions. Unissued.
+
 ---
 
 # Pair JAX/XLA env vars with measured compile and run times, per backend
diff --git a/tasks/point_source_image_plane_gpu_breakdown.md b/tasks/point_source_image_plane_gpu_breakdown.md
index 1189016..6465052 100644
--- a/tasks/point_source_image_plane_gpu_breakdown.md
+++ b/tasks/point_source_image_plane_gpu_breakdown.md
@@ -16,6 +16,11 @@ The original task below is retained verbatim. Current scheduling state lives in
 - Forward-mode NaN bug issued: [PyAutoLens#767](https://github.com/PyAutoLabs/PyAutoLens/issues/767). Reproduced on CPU fp64: forward `[nan]x5`, reverse finite; stack 2026.10.4.1+3 `b695e57b6`. Mind registry commit `0112ccba`.
 - The autolens_inference image-plane fit measurement still awaits human authorisation.
 
+## Check-in 2026-10-07
+
+- https://github.com/PyAutoLabs/PyAutoLens/issues/767 closed; fixed by https://github.com/PyAutoLabs/PyAutoLens/pull/768 (merged 2026-10-04T20:41Z, 0659903d4), in tag 2026.10.7.1 (merge-base verified). Correctness only; no timing claim.
+- Stays `needs-decision`: the autolens_inference image-plane fit measurement still awaits human authorisation.
+
 ---
 
 # Point-source A100 speed-up campaign: profile and optimize with the shared breakdown
diff --git a/tasks/pointsolver_cpu_speed_campaign_remainder.md b/tasks/pointsolver_cpu_speed_campaign_remainder.md
index afa3111..c12507c 100644
--- a/tasks/pointsolver_cpu_speed_campaign_remainder.md
+++ b/tasks/pointsolver_cpu_speed_campaign_remainder.md
@@ -15,6 +15,12 @@ The original task below is retained verbatim. Current scheduling state lives in
 - Issue: [autolens_profiling#370](https://github.com/PyAutoLabs/autolens_profiling/issues/370). Branch `feature/point-source-wiki-reconcile` @`0560284`, parked at Heart RED.
 - Still owed: RAL cleanup, register_model grad-zero prompt, test move, CI smoke cells, `nopad` deletion.
 
+## Check-in 2026-10-07
+
+- https://github.com/PyAutoLabs/autolens_profiling/pull/373 (issue #370) merged 2026-10-04T20:49Z: wiki reconcile + completion evidence (https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/results/notes/point_source_cpu_campaign.md#campaign-completion-evidence-2026-10-04).
+- Still owed (unchanged), now tracked by Mind draft `draft/maintenance/autolens/point_source_cpu_campaign_owed_leftovers.md` (2026-10-04): RAL cleanup, test move, register_model grad-zero prompt, CI smoke cells, constant_folding A/B, nopad deletion, quiet re-runs.
+- RAL leftover folders not re-listed 2026-10-07 (unverified).
+
 ---
 
 # Point-source (single-source) CPU campaign — carried leftovers and completion evidence
diff --git a/tasks/runtime_cell_single_jit_gpu_warmup.md b/tasks/runtime_cell_single_jit_gpu_warmup.md
index 424f2de..4503868 100644
--- a/tasks/runtime_cell_single_jit_gpu_warmup.md
+++ b/tasks/runtime_cell_single_jit_gpu_warmup.md
@@ -15,6 +15,12 @@ The original task below is retained verbatim. Current scheduling state lives in
 - Local CPU witness: `single_jit` 0.417 ms vs median 0.388 ms (p10 0.320, p90 0.545).
 - Imaging cells not yet wired.
 
+## Check-in 2026-10-07
+
+- Option (a) merged: https://github.com/PyAutoLabs/autolens_profiling/pull/374 (issue #371, 2026-10-04). `full_pipeline_single_jit_median_ms` (p10/p90) sits beside the unchanged `single_jit`; the GPU headline is labelled "first block after compile" (https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/scripts/point_source_source/source_plane/likelihood_runtime_solved.py).
+- Imaging release-sweep cells are not wired. `git log --since=2026-10-04 origin/main -- results/` shows only #375 streaming rows and a README layout refactor: no release sweep on main, so the new field has no sweep rows yet.
+- Stays `active`: wire imaging cells; the field appears at the next release sweep.
+
 ---
 
 # Runtime cells' A100 `single_jit` includes the post-compile warm-up — source-plane 0.642 ms vs a steady 0.267 ms on the same node
diff --git a/tasks/setup_baseline_campaign.md b/tasks/setup_baseline_campaign.md
index df49c0f..347a189 100644
--- a/tasks/setup_baseline_campaign.md
+++ b/tasks/setup_baseline_campaign.md
@@ -69,3 +69,9 @@ keeps its existing receipts and snapshots; this task creates no new measurements
 Until those steps are complete, retain `needs-decision`: no campaign is reported
 as running or accepted, and specification implementation remains distinct from
 the later collection and review deliverables.
+
+## Check-in 2026-10-07
+
+- Specification merged: https://github.com/PyAutoLabs/autolens_profiling/pull/385 (2026-10-06; issue #384 closed 2026-10-06; PyAutoPulse#17 closed 2026-10-06). `baseline/campaign.json` is status draft, version 1, with `baseline/README.md` readiness contract (https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/baseline/campaign.json, https://github.com/PyAutoLabs/autolens_profiling/blob/719459fefb40c7e5be157f652818dbf923830557/wiki/campaigns/setup_baseline.md).
+- Six unknowns recorded verbatim: no frozen revisions/env lock; no collection authorization/window; hardware/affinity identity unselected; each cell needs complete settings, input hashes, witness + tolerances; compile per-cell builder unavailable; proposed fp64/mixed coverage is not evidence of support.
+- Status stays `needs-decision`: no collection or acceptance authorized; the check-in authorizes none.
diff --git a/tasks/timing_noise_audit.md b/tasks/timing_noise_audit.md
index 22145a4..4c97dfa 100644
--- a/tasks/timing_noise_audit.md
+++ b/tasks/timing_noise_audit.md
@@ -9,6 +9,11 @@ The original task below is retained verbatim. Current scheduling state lives in
 - Adjacent but distinct: PyAutoHeart#276 / PR #277 (unit-timing distinct baseline) completed 2026-10-04; that is Heart unit-test timing, not profiling gates.
 - Next: start the audit (read-and-classify) phase via start_dev, reusing #362.
 
+## Check-in 2026-10-07
+
+- https://github.com/PyAutoLabs/autolens_profiling/issues/362 still open, 0 comments, unassigned, unchanged since 2026-10-02.
+- Related new Mind draft: `draft/bug/autolens_profiling/call_accounting_ci_timing_threshold.md` (2026-10-06).
+
 ---
 
 # Audit timing tests and profiling gates for measurement noise