CPU dense decision and diagnostic evidence

The eight-accumulator CPU candidate was rejected at the fixed-work preflight. Its first configuration converged with different iterations and matvecs even though both revisions passed true-residual checks. No candidate CPU A/B timing campaign was started, so this is neither a measured slowdown nor a demonstrated speedup. Keep the baseline production arithmetic; retain useful independent boundary, cancellation and non-finite coverage. CPU 2048 prepared optimization remains open.

This report records diagnostics from baseline 84c88c98c895103395e1b6d6e98f7618848a14d3 and rejected candidate abd655a20a6835b6b95b2e6c3aeae2b69b9957aa. It is not a current-main NumPy/PyTorch scorecard. The 5% adoption threshold is unchanged. Fresh API, Session and prepared scopes remain separate; no backend, fusion or size AUTO policy is introduced.

Fixed-work rejection

The refined source was frozen before candidate performance outcomes. The first configuration is CPU-release, dense float64, native kernel, standard CG without preconditioner, fused operations, fresh API, one CPU thread. For dense-64, two checks each reproduce baseline 49/51/0 versus candidate 48/50/0 (iterations/matvecs/restarts). Both true residuals pass the original tolerance. Rounding-group changes are numerically valid here but ineligible for this declared same-work optimization trial. There is no work normalization, selective size exception, timing comparison, or relaxed tolerance.

All checks from the two completed first-configuration workers

Revision	Case	Implementation	I/M/R	Checks	Maximum true residual	Minimum threshold
baseline	dense-64	native	49/51/0	2	1.8582645e-06	3.0576161e-06
baseline	dense-64	numpy-out	49/51/0	2	1.8734617e-06	3.0576161e-06
baseline	dense-64	torch-eager-out	49/51/0	2	1.9437482e-06	3.0576161e-06
baseline	dense-256	native	67/70/1	2	4.8815545e-06	5.0262292e-06
baseline	dense-256	numpy-out	67/70/1	2	4.8752604e-06	5.0262292e-06
baseline	dense-256	torch-eager-out	67/70/1	2	4.8872893e-06	5.0262292e-06
baseline	dense-1024	native	70/73/1	2	9.6567151e-06	1.0405785e-05
baseline	dense-1024	numpy-out	70/73/1	2	9.6567151e-06	1.0405785e-05
baseline	dense-1024	torch-eager-out	70/73/1	2	9.6567151e-06	1.0405785e-05
baseline	dense-2048	native	70/73/1	2	1.2911386e-05	1.4810685e-05
baseline	dense-2048	numpy-out	70/73/1	2	1.2911386e-05	1.4810685e-05
baseline	dense-2048	torch-eager-out	70/73/1	2	1.2911385e-05	1.4810685e-05
current	dense-64	native	48/50/0	2	2.307155e-06	3.0576161e-06
current	dense-64	numpy-out	49/51/0	2	1.8734617e-06	3.0576161e-06
current	dense-64	torch-eager-out	49/51/0	2	1.9437482e-06	3.0576161e-06
current	dense-256	native	67/70/1	2	4.9381732e-06	5.0262292e-06
current	dense-256	numpy-out	67/70/1	2	4.8752604e-06	5.0262292e-06
current	dense-256	torch-eager-out	67/70/1	2	4.8872893e-06	5.0262292e-06
current	dense-1024	native	70/73/1	2	9.6567151e-06	1.0405785e-05
current	dense-1024	numpy-out	70/73/1	2	9.6567151e-06	1.0405785e-05
current	dense-1024	torch-eager-out	70/73/1	2	9.6567151e-06	1.0405785e-05
current	dense-2048	native	70/73/1	2	1.2911385e-05	1.4810685e-05
current	dense-2048	numpy-out	70/73/1	2	1.2911386e-05	1.4810685e-05
current	dense-2048	torch-eager-out	70/73/1	2	1.2911385e-05	1.4810685e-05

The enclosing preflight manifest remains failed; later declared configurations were not completed by this aborted CPU trial. The full per-check values and source identities are preserved in the accompanying structured audit.

Actual code-generation inspection

GNU C++ 16.0.1 used -O3 -DNDEBUG -std=c++20 -fPIC -Wall -Wextra -Wpedantic. All inspected helpers use 128-bit SSE2, without AVX or FMA. The refined low/high four-element arrays preserve the first eight-accumulator prototype’s per-lane recurrence and balanced reduction, but change association relative to the four-accumulator baseline. Static instruction and frame counts do not establish runtime performance, cache behavior or bandwidth.

Actual serial two-row helper observations

Observation	Baseline	713c8ae	abd655a
Packed accumulation chains	4	8	8
Distinct bulk-loop XMM register names	7	13	13
Bulk-loop FP stack references	0	0	0
Instructions per eight columns	36	34	34
Unconditional frame bytes	0 on multiple-of-four path	168	104
Entry array-zeroing bytes	0	128	64
Post-bulk FP stores before optional four-column tail (bytes)	0	64	64
Two-row helper symbol bytes	475	918	835
Whole dense object .text bytes	3774	4667	4539

The refinement partially reduces setup and footprint; its unconditional frame, canary and post-loop stores remain. No further source variant was selected from performance outcomes. Earlier rejected four-row and four-accumulator unroll experiments are separate evidence and are not repeated or relabelled here.

Previously preserved actual object SHA256 identities

Representation	Preserved object	SHA256
Baseline	codegen-halves/baseline.o	9315d269ab22598ddd4227a0fa6080681569b9fdf5c0b2baff07a4b43cbfdf95
713c8ae	codegen/current-713-stage.o	778f5045420972e87161c5dd291a1f269ad6b5da807273ade4734c0563454b6b
abd655a	codegen-halves/current.o	5b82b88229d0dcfd998974b5bc926413f08aae25dc4d3e1b7589cda18c7d7284

These object hashes were checked in the earlier static audit; report rendering does not rehash binaries. The full frame/instruction audit remains preserved as cpu-codegen-halves-note.rst.

Baseline same-call engine attribution

The exact shared solver engine and actual CPU backends are instrumented on the same dense-2048 fixture (seed 20261017). There are 24 ordinary captures and six additional provider-wrapped captures, each in a fresh process with three warmups and ten measured engine calls. Every warmup and sample has a converged status, stable I/M/R within its capture, and an independent wider-long-double true residual check outside timing. Jacobi preparation, workspace allocation, x reset, Python dispatch and public solve validation are outside these engine intervals.

The matvec-entry percentage is the median of each call’s summed apply, apply_and_dot and residual_squared_norm times divided by its engine time. Composite entries include vector arithmetic. The nested private CBLAS timer isolates actual provider-entry elapsed time inside those composites; it includes provider checks/dispatch and timer overhead, rather than exclusive microkernel CPU time. Provider time must not be added to entry sums. All percentages below come from same-call ratios; separate capture medians are not subtracted.

All engine captures; times are medians in milliseconds

Capture	I/M/R	Engine ms	Entry sum ms	Remainder ms	Matvec-entry %	Nested provider %	Maximum true-residual/threshold
cpu-release-native-standard-none-fused0	70/73/1	100.8635	100.83195	0.0281545	99.720549	n/a	0.87176155
cpu-release-native-standard-none-fused1	70/73/1	103.91455	103.88041	0.026821	99.790351	n/a	0.87176155
cpu-release-native-standard-jacobi-fused0	3/5/0	6.1359045	6.133435	0.002723	99.655595	n/a	0.83641225
cpu-release-native-standard-jacobi-fused1	3/5/0	5.8770945	5.875022	0.0021835	99.728347	n/a	0.83641225
cpu-release-native-explicit-none-fused0	68/137/0	158.36342	158.27195	0.083896	99.752594	n/a	0.98130942
cpu-release-native-explicit-none-fused1	68/137/0	159.26138	159.21603	0.046001	99.72865	n/a	0.98130942
cpu-openmp-native-standard-none-fused0	70/73/1	48.546378	48.520315	0.0257545	99.248994	n/a	0.87176155
cpu-openmp-native-standard-none-fused1	70/73/1	52.606431	52.584942	0.02666	99.224025	n/a	0.87176155
cpu-openmp-native-standard-jacobi-fused0	3/5/0	3.450463	3.448252	0.002453	99.151357	n/a	0.83641225
cpu-openmp-native-standard-jacobi-fused1	3/5/0	3.456848	3.454399	0.002546	99.104059	n/a	0.83641225
cpu-openmp-native-explicit-none-fused0	68/137/0	101.59005	101.48882	0.046475	99.519448	n/a	0.98130942
cpu-openmp-native-explicit-none-fused1	68/137/0	93.244557	93.199611	0.0442705	99.289699	n/a	0.98130942
cpu-blas-native-standard-none-fused0	70/73/1	90.747881	90.725233	0.023825	99.697779	n/a	0.87176155
cpu-blas-native-standard-none-fused1	70/73/1	90.203702	90.170068	0.028774	99.783773	n/a	0.87176155
cpu-blas-native-standard-jacobi-fused0	3/5/0	6.6076875	6.6001355	0.0017715	99.715629	n/a	0.83641225
cpu-blas-native-standard-jacobi-fused1	3/5/0	6.198311	6.1967285	0.0015495	99.640093	n/a	0.83641225
cpu-blas-native-explicit-none-fused0	68/137/0	171.74281	171.68967	0.053137	99.801063	n/a	0.98130942
cpu-blas-native-explicit-none-fused1	68/137/0	192.42125	192.38192	0.045734	99.794583	n/a	0.98130942
cpu-blas-blas-standard-none-fused0	70/73/1	84.548345	84.516131	0.0312445	99.656482	n/a	0.87176155
cpu-blas-blas-standard-none-fused1	70/73/1	79.859586	79.823885	0.0326855	99.726432	n/a	0.87176155
cpu-blas-blas-standard-jacobi-fused0	3/5/0	5.456052	5.453814	0.0020045	99.623192	n/a	0.83641225
cpu-blas-blas-standard-jacobi-fused1	3/5/0	5.3576465	5.356163	0.002013	99.718472	n/a	0.83641225
cpu-blas-blas-explicit-none-fused0	68/137/0	150.38634	150.36122	0.0264545	99.759554	n/a	0.98130942
cpu-blas-blas-explicit-none-fused1	68/137/0	141.27486	141.24558	0.025433	99.734617	n/a	0.98130942
provider-standard-none-fused0	70/73/1	76.042181	76.010869	0.02529	99.664691	99.514132	0.87176155
provider-standard-none-fused1	70/73/1	73.564548	73.541472	0.033333	99.741645	99.587221	0.87176155
provider-standard-jacobi-fused0	3/5/0	5.433049	5.4304385	0.0025535	99.543501	99.32157	0.83641225
provider-standard-jacobi-fused1	3/5/0	5.3692645	5.366724	0.002565	99.692583	99.522189	0.83641225
provider-explicit-none-fused0	68/137/0	136.8543	136.6764	0.0458755	99.735184	99.521313	0.98130942
provider-explicit-none-fused1	68/137/0	135.29772	135.25119	0.053275	99.732051	99.572332	0.98130942

Across the six provider-wrapped captures, the median fraction of an engine interval spent inside the actual private GEMV entry ranges from 99.322% to 99.587%. This supports investigating actual GEMV/provider/storage behavior before optimizing unrelated vector operations. It does not determine the cause of an earlier full-solver NumPy gap. Oracle matrix reads between calls and instrumentation overhead limit generalization to uninstrumented solves.

Actual private provider identity after a checked GEMV:

{
  "blasint_bytes": 4,
  "config": "OpenBLAS 0.3.30 NO_LAPACK NO_LAPACKE DYNAMIC_ARCH NO_AFFINITY USE_LOCKING Haswell SINGLE_THREADED",
  "environment_OPENBLAS_CORETYPE": null,
  "framework_score": false,
  "identity_dgemv_check_passes": true,
  "metric": "private_openblas_identity",
  "parallel_kind": 0,
  "reported_num_threads": 1,
  "schema_version": 1,
  "selected_core_after_dgemv": "Haswell"
}

All per-entry call/time medians are retained in the structured audit. Their separate medians are descriptive and need not sum to the median engine time.

Same-buffer provider and storage probe

A separate component diagnostic crosses two providers with two storage owners: actual baseline private BLAS through CpuBackend::apply and NumPy dot(out=); the exact fixture’s NumPy allocation and an actual owning cg::DenseMatrix snapshot made with std::vector<double>. Both providers use the same matrix, vector and output addresses for a given storage choice. The fixture’s rhs b is the fixed GEMV vector; these are not CG search directions. Native snapshot construction and all identity/oracle checks are outside timing. The NumPy borrowed allocation is not a claim about the full NumPy prepared adapter’s separate copied matrix.

For each size, all four fresh processes are retained in A/B/B/A label order. A and B reverse treatment enumeration before the same Williams scheduler; they use the same source and bridge, not different optimization builds. Each process uses CPU 0 and one thread in all providers, three warmups per treatment and 36 complete four-treatment Williams rounds with one call per treatment. No global THP setting or NumPy environment variable is changed.

Every fresh-process component median in milliseconds

Process	Size	Private / NumPy A	NumPy / NumPy A	Private / C++ A	NumPy / C++ A
dense2048-p1-A	2048	1.22442	1.2408155	1.268276	1.2350245
dense2048-p2-B	2048	1.372966	1.439262	1.415382	1.42037
dense2048-p3-B	2048	1.0745405	1.100359	0.979196	1.025904
dense2048-p4-A	2048	1.1087915	1.0762205	1.182623	1.165265
dense1024-p1-A	1024	0.1485445	0.1442195	0.1485685	0.1483445
dense1024-p2-B	1024	0.146474	0.1546845	0.1486875	0.151545
dense1024-p3-B	1024	0.155286	0.1519175	0.1584395	0.15542
dense1024-p4-A	1024	0.146485	0.148404	0.1498585	0.15461

Within-process ratios; values above one mean the numerator is slower

Process	Private/NumPy on NumPy A	Private/NumPy on C++ A	C++/NumPy A private	C++/NumPy A NumPy
dense2048-p1-A	0.98678651	1.0269238	1.0358178	0.99533291
dense2048-p2-B	0.9539375	0.99648824	1.0308937	0.98687383
dense2048-p3-B	0.97653629	0.95447137	0.91126951	0.93233572
dense2048-p4-A	1.0302642	1.0148962	1.0665874	1.0827382
dense1024-p1-A	1.029989	1.00151	1.0001616	1.0286022
dense1024-p2-B	0.94692099	0.98114421	1.0151119	0.97970385
dense1024-p3-B	1.0221732	1.019428	1.0203077	1.0230553
dense1024-p4-A	0.98706908	0.96926784	1.0230297	1.0418183

The observed 2048 workers do not show a consistent large provider or ownership penalty. These component medians are not a full-solver score, a 5% adoption result, or a calibrated confidence interval. Between-process variation and treatment-dependent cache state remain visible. No aggregate winner is selected. The probe does not reproduce all prepared-adapter allocations, CG operands, or real solver memory-access sequences. Provider calls include their different Python/ctypes dispatch boundaries; this is not exclusive kernel timing.

Product correctness and ownership evidence

Each worker performs four prechecks, twelve warmup checks and 144 timed-call checks, all outside timing. The oracle uses wider-long-double row products and sums, with a double forward-error bound plus a long-double oracle bound. All 160 checks per process pass. The maximum observed error and maximum rowwise bound below summarize different rows/calls; their ratio is not the per-row gate. Input and both matrix byte hashes remain unchanged.

Every worker’s independent product checks

Process	Precision bits	Passing checks	Maximum absolute error	Maximum row bound	Canonical fixture SHA256
dense2048-p1-A	64	160	6.0822458e-12	2.2138527e-08	8bda02fed9ccafa8c12f857ad51962b02d8c860ee96a006078aed872c40cae6c
dense2048-p2-B	64	160	6.0822458e-12	2.2138527e-08	8bda02fed9ccafa8c12f857ad51962b02d8c860ee96a006078aed872c40cae6c
dense2048-p3-B	64	160	6.0822458e-12	2.2138527e-08	8bda02fed9ccafa8c12f857ad51962b02d8c860ee96a006078aed872c40cae6c
dense2048-p4-A	64	160	6.0822458e-12	2.2138527e-08	8bda02fed9ccafa8c12f857ad51962b02d8c860ee96a006078aed872c40cae6c
dense1024-p1-A	64	160	4.5670134e-12	8.2972642e-09	204aa61929e5e8d34102ac2d0bda5714b6f0d17f8bf4796a7f71b535a21053bb
dense1024-p2-B	64	160	4.5670134e-12	8.2972642e-09	204aa61929e5e8d34102ac2d0bda5714b6f0d17f8bf4796a7f71b535a21053bb
dense1024-p3-B	64	160	4.5670134e-12	8.2972642e-09	204aa61929e5e8d34102ac2d0bda5714b6f0d17f8bf4796a7f71b535a21053bb
dense1024-p4-A	64	160	4.5670134e-12	8.2972642e-09	204aa61929e5e8d34102ac2d0bda5714b6f0d17f8bf4796a7f71b535a21053bb

THP observations and limits

The allocator policy was a source-grounded hypothesis: NumPy 2.2.6 can request MADV_HUGEPAGE for sufficiently large allocations, whereas the native owning snapshot uses ordinary vector storage. See the NumPy global-state documentation and versioned allocator source. The following records describe actual overlapping VMAs, not a guessed allocator policy. AnonHugePages is VMA residency; THPeligible and hg are eligibility/advice observations. A VMA may include unrelated allocations, so its huge-page bytes cannot be assigned wholly to a matrix. KernelPageSize alone is not a huge-page-residency test.

At 2048, the distinct VMA-wide AnonHugePages values are 0 kB, 30720 kB for the NumPy matrix and 0 kB for the C++ matrix. The table retains every overlapping matrix mapping in both phases. These residency/advice/eligibility fields remain unchanged across the probe. The component timings do not establish that this policy difference caused the historical prepared-solver gap. No allocator change follows from this evidence. All exact VMA records, including matrix/vector/output mappings before and after every worker, are retained below and in the structured audit.

Every overlapping matrix VMA; AnonHugePages applies to the entire mapping

Process	Phase	Buffer	VMA address range	AnonHugePages	THPeligible	VmFlags	VMA exceeds buffer
dense2048-p1-A	before	borrowed-numpy	7d56eaa1c000-7d56eaa1d000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p1-A	before	borrowed-numpy	7d56eaa1d000-7d56eca1d000	30720 kB	1	rd wr mr mw me ac sd hg	True
dense2048-p1-A	before	owned-cpp	7d56e6a1b000-7d56e8a1c000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p1-A	after	borrowed-numpy	7d56eaa1c000-7d56eaa1d000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p1-A	after	borrowed-numpy	7d56eaa1d000-7d56eca1d000	30720 kB	1	rd wr mr mw me ac sd hg	True
dense2048-p1-A	after	owned-cpp	7d56e6a1b000-7d56e8a1c000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p2-B	before	borrowed-numpy	7b9f36d1c000-7b9f36d1d000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p2-B	before	borrowed-numpy	7b9f36d1d000-7b9f38d1d000	30720 kB	1	rd wr mr mw me ac sd hg	True
dense2048-p2-B	before	owned-cpp	7b9f32d1b000-7b9f34d1c000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p2-B	after	borrowed-numpy	7b9f36d1c000-7b9f36d1d000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p2-B	after	borrowed-numpy	7b9f36d1d000-7b9f38d1d000	30720 kB	1	rd wr mr mw me ac sd hg	True
dense2048-p2-B	after	owned-cpp	7b9f32d1b000-7b9f34d1c000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p3-B	before	borrowed-numpy	76ae53a1c000-76ae53a1d000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p3-B	before	borrowed-numpy	76ae53a1d000-76ae55a1d000	30720 kB	1	rd wr mr mw me ac sd hg	True
dense2048-p3-B	before	owned-cpp	76ae4fa1b000-76ae51a1c000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p3-B	after	borrowed-numpy	76ae53a1c000-76ae53a1d000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p3-B	after	borrowed-numpy	76ae53a1d000-76ae55a1d000	30720 kB	1	rd wr mr mw me ac sd hg	True
dense2048-p3-B	after	owned-cpp	76ae4fa1b000-76ae51a1c000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p4-A	before	borrowed-numpy	717aabc1c000-717aabc1d000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p4-A	before	borrowed-numpy	717aabc1d000-717aadc1d000	30720 kB	1	rd wr mr mw me ac sd hg	True
dense2048-p4-A	before	owned-cpp	717aa7c1b000-717aa9c1c000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p4-A	after	borrowed-numpy	717aabc1c000-717aabc1d000	0 kB	0	rd wr mr mw me ac sd	True
dense2048-p4-A	after	borrowed-numpy	717aabc1d000-717aadc1d000	30720 kB	1	rd wr mr mw me ac sd hg	True
dense2048-p4-A	after	owned-cpp	717aa7c1b000-717aa9c1c000	0 kB	0	rd wr mr mw me ac sd	True
dense1024-p1-A	before	borrowed-numpy	7b3d56d1c000-7b3d56d1d000	0 kB	0	rd wr mr mw me ac sd	True
dense1024-p1-A	before	borrowed-numpy	7b3d56d1d000-7b3d5751d000	6144 kB	1	rd wr mr mw me ac sd hg	True
dense1024-p1-A	before	owned-cpp	12796000-13e2e000	0 kB	0	rd wr mr mw me ac sd	True
dense1024-p1-A	after	borrowed-numpy	7b3d56d1c000-7b3d56d1d000	0 kB	0	rd wr mr mw me ac sd	True
dense1024-p1-A	after	borrowed-numpy	7b3d56d1d000-7b3d5751d000	6144 kB	1	rd wr mr mw me ac sd hg	True
dense1024-p1-A	after	owned-cpp	12796000-13e2e000	0 kB	0	rd wr mr mw me ac sd	True
dense1024-p2-B	before	borrowed-numpy	7ab58191c000-7ab58191d000	0 kB	0	rd wr mr mw me ac sd	True
dense1024-p2-B	before	borrowed-numpy	7ab58191d000-7ab58211d000	6144 kB	1	rd wr mr mw me ac sd hg	True
dense1024-p2-B	before	owned-cpp	30516000-31bae000	0 kB	0	rd wr mr mw me ac sd	True
dense1024-p2-B	after	borrowed-numpy	7ab58191c000-7ab58191d000	0 kB	0	rd wr mr mw me ac sd	True
dense1024-p2-B	after	borrowed-numpy	7ab58191d000-7ab58211d000	6144 kB	1	rd wr mr mw me ac sd hg	True
dense1024-p2-B	after	owned-cpp	30516000-31bae000	0 kB	0	rd wr mr mw me ac sd	True
dense1024-p3-B	before	borrowed-numpy	78d372a1c000-78d372a1d000	0 kB	0	rd wr mr mw me ac sd	True
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dense2048-p1-A: observed buffers, policies and every overlapping VMA

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dense2048-p2-B: observed buffers, policies and every overlapping VMA

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dense2048-p3-B: observed buffers, policies and every overlapping VMA

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dense2048-p4-A: observed buffers, policies and every overlapping VMA

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dense1024-p1-A: observed buffers, policies and every overlapping VMA

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dense1024-p2-B: observed buffers, policies and every overlapping VMA

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dense1024-p3-B: observed buffers, policies and every overlapping VMA

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dense1024-p4-A: observed buffers, policies and every overlapping VMA

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Decision and remaining CPU work

Retain baseline production arithmetic and explicit native/BLAS and fusion choices. The eight-accumulator candidate is ineligible under the fixed-work gate. Preserve meaningful independent tests and these negative/diagnostic results instead of adopting a size exception, changing tolerances, or retrying arithmetic variants against observed results.

The next justified CPU investigation is attribution inside actual prepared adapter calls: observe the actual matrix pointer/owner, GEMV operand sequence and provider calls before proposing another implementation. This probe shows that a large private-provider or native-ownership penalty is not universal for the shared rhs GEMV; it does not explain the complete solver gap. Such a new diagnostic must preserve numerical work and allocation/provider policies and be declared before measurement. No further production variant is justified by the present component evidence.

The full A/A calibration is separate evidence: inconclusive controls must remain inconclusive, and a finite set of controls neither proves future host stability nor estimates a universal false-positive rate. No control or started campaign is replaced, pooled across scopes, or omitted from an adoption decision. No CPU speedup or new win over NumPy/PyTorch is claimed by this report.

Artifact identity and regeneration

The structured audit records all source JSON hashes, exact per-process argv, baseline/core/provider/bridge identities, full work and numerical gates, per-entry engine medians, all worker medians and all VMA observations. The renderer verifies each CPU diagnostic stdout hash against its execution manifest and each storage worker against completion.json. It checks frozen identity equality and complete sampling, but does not rehash binaries or rerun benchmarks. The archive/binary hashes below are the already verified worker identity records, not new hashing performed during report rendering.

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  },
  "fixture_hashes": {
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    "dense-2048": "8bda02fed9ccafa8c12f857ad51962b02d8c860ee96a006078aed872c40cae6c",
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  },
  "private_blas_archive": "/home/threemonth03/Downloads/Conjugate-Gradient-Method-library/build/reliable-performance-20261018/builds/baseline/cpu-blas/_deps/cg-openblas/private/lib/libcgpy_openblas.a"
}

Regenerate only from the preserved local evidence after quiet measurements finish. This is artifact rendering, not a command that reconstructs missing baseline binaries or reruns performance samples:

python3 build/reliable-performance-20261018/render_cpu_decision.py \
  --output build/reliable-performance-20261018/cpu-decision-report.rst \
  --analysis build/reliable-performance-20261018/cpu-decision-audit.json

All source-artifact SHA256 values read by this rendering:

Small source/JSON artifacts only

Relative artifact	SHA256
cpu-candidate-decision.json	aa270020eb558f4be85b539a0ec459856940cdf2fe2f366e9a910a49315ed687
cpu-codegen-halves-note.rst	83f78bb7061fcefd65c1809ee68c5a8931aa5e1e5c16b0ffa8392b775c013af5
cpu-profiles/compile-manifest.json	2b22fe31909a04d6edab729893281917ffacd2016ba09b90e443ebb900969636
cpu-profiles/cpu-blas-blas-explicit-none-fused0.json	6525880bcb8cbef523d9dabfaac23bb003a846435320dcba1506223edeffec70
cpu-profiles/cpu-blas-blas-explicit-none-fused1.json	82d3cddb3c7013713377d4aed7445e573e18f88715f3edc89a655dea74bba8cd
cpu-profiles/cpu-blas-blas-standard-jacobi-fused0.json	4ae9abc2920582dd01f7194958ddb173b8f62c6e67df36d6ea092df93a828d0c
cpu-profiles/cpu-blas-blas-standard-jacobi-fused1.json	a834ede091e6672e4599e744baef837e8c1a47dd3cc4c1425ede9c5ca0157d11
cpu-profiles/cpu-blas-blas-standard-none-fused0.json	c7e395524af754e4aa04e1e08f5977491ba1d5d15a5c7d3e2f6400f1e365eaed
cpu-profiles/cpu-blas-blas-standard-none-fused1.json	9530f642642e06110793f8e19f2d685736d6604811411cef407e6bd7770f3113
cpu-profiles/cpu-blas-native-explicit-none-fused0.json	4c1ff1c79b544a3cb1e1fdfe3e532bedde0102f7cbde3b4b4bd7fcbf038501cd
cpu-profiles/cpu-blas-native-explicit-none-fused1.json	ed24b4c43abac1cdfc2bfe85270f35985e8edd4f4aef0a6999dd41eeee99d496
cpu-profiles/cpu-blas-native-standard-jacobi-fused0.json	c1a05c23ea8e13aa36183085da80e27263de4cdbbb3e1b704d8d0935736939da
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cpu-profiles/cpu-release-native-explicit-none-fused0.json	f1d262f19fc2c5205d65420d79dc71fced02852cc3b6d833a2f98f1be4ed8a47
cpu-profiles/cpu-release-native-explicit-none-fused1.json	ff8b886ba0148a7a412671ee865f77ffcda8b807ed4702a42b99724e1a8e3d6f
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cpu-profiles/execution-manifest.json	322797b7bd79d2b91408c15c78d3c1cb691f9d2cecba7dbc3e20dfd270752aa6
cpu-profiles/execution-plan.json	416ed10a1392e0a4f998d72242a1dc13063cc81ec00c46779443eba9b0eeab16
cpu-profiles/fixtures.json	3f6d71680c9048e623efed5a7bdec6fb5f1535cb33f04c4af5fe3e9b38c1b742
cpu-profiles/private-blas-identity.json	9c8ae849cedcea6f62d901762fad541fc2ebd5f67809526c0cb2378ee395a34c
cpu-profiles/provider-compile-manifest.json	4cc0ecd7a647c0c1229f5be45d24f758d61154c6d993c266f8734437c302aa14
cpu-profiles/provider-explicit-none-fused0.json	082f6d814e06332308624cc5e942fa9d1c4a3e54608e9036a51f1d0fa581fe77
cpu-profiles/provider-explicit-none-fused1.json	bbd5ad08a6338fd60cfd6d2e0f82adee5c33a924937940a76cd71a1c1016b810
cpu-profiles/provider-standard-jacobi-fused0.json	50bdc6f0abfa1e09490ac9e0fcc8652a785525128cfa54dd393b3898f517feee
cpu-profiles/provider-standard-jacobi-fused1.json	4c475f305148d413932aa88188a1e800021263aa9e9747c616dadaa13f1fdcd0
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