Correct the GEMM/array_windows attribution, and start consolidating onto array_chunks - #299
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PR #298 claimed the 512x512/1024x1024 GEMM numbers depend on simd_ops::array_windows/array_chunks. Tracing the actual call graph (backend::native::gemm_f32 -> matrixmultiply crate's Goto algorithm; simd_ops::gemm_f64_tiled -> fixed TILE=64 blocking with F64x8 register accumulation) shows neither path calls array_windows or array_chunks at all -- their only production call site is hpc::blake3's 64-byte block chunking. Corrected the README to describe the GEMM kernels' real mechanism and to attribute array_windows/array_chunks to where they're actually used, without losing the real (separately verified) array_windows-vs-JIT latency finding. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012wrzeZAdwGYTCKoxamwQht
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📝 WalkthroughWalkthroughThe README now distinguishes the f32 and f64 GEMM implementations. It also describes array traversal utilities and BLAKE3 benchmarking independently from GEMM benchmarks. ChangesREADME documentation
Estimated code review effort: 1 (Trivial) | ~2 minutes Merge Risk: 🔵 Low · up to The README improves GEMM attribution, but its upstream comparison may still inaccurately describe matrixmultiply’s implementation details. This affects documentation accuracy rather than runtime behavior and should be corrected before or shortly after merge. Suggested reviewers: Poem
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In `@README.md`:
- Line 114: Update the README’s upstream GEMM comparison to remove the
inaccurate “no tiling” and “no microkernel” claims, since upstream calls
matrixmultiply::sgemm with Goto-style cache blocking and microkernels. Describe
only the verified implementation, feature, or benchmark differences that support
the reported throughput comparison.
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cosine_f32_to_f64_simd indexed a[off + j] / b[off + j] per element, so each of the 16 loads per 8-element chunk carried a bounds check against a dynamic length. array_chunks::<f32, 8> yields &[f32; 8], where the inner index is provably in bounds, and from_array consumes the widened buffer without a second length check. The op order is unchanged, so results are bit-identical; verified standalone across every length 0..300 including unequal-length inputs, since the crate's lib tests currently do not compile (pre-existing missing U16x8 imports in simd_avx512.rs test modules, unrelated). This is the first of the mechanism-level consolidations: hand-rolled chunk index bookkeeping replaced by the const-generic window primitive that simd_ops.rs documents as the foundation for exactly these kernels. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012wrzeZAdwGYTCKoxamwQht
AdaWorldAPI
changed the title
The array_chunks loop was hand-wrapped and failed format/stable. The GEMM Performance table has now been mis-attributed twice, so this stops guessing and marks its provenance unverified instead. Array::dot() calls matrixmultiply::sgemm/dgemm, which for f32 is the same engine backend::native::gemm_f32 uses, so that path has no fork-vs-upstream engine difference to credit; matrixmultiply also has Goto blocking and microkernels, making the "upstream has no tiling/microkernel" line false. gemm_f64_tiled is the one fork-local kernel and Array::dot() does not reach it. Also narrows the window-primitive claim: blake3 and the cosine kernel call array_chunks; array_windows and the checked variants have no in-crate production caller. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012wrzeZAdwGYTCKoxamwQht
int_simd_tests referenced I8x16, U64x8 and U16x8 without importing them, so the lib test target failed to compile whenever AVX-512 was enabled at build time. CI does not pass -C target-cpu, so those modules were cfg'd out there and CI stayed green while the tests were broken in the configuration .cargo/config.toml actually ships (x86-64-v4). All three types are already exported from crate::simd; adding them to the existing use list is the whole fix. The suite now compiles and runs under target-cpu=native: 2322 passed, 0 failed, 30 ignored. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012wrzeZAdwGYTCKoxamwQht
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Two related changes, both stemming from tracing where
array_windows/array_chunksare actually used.1. Correct the README attribution (#298 follow-up)
#298 claimed the 512x512/1024x1024 GEMM Performance rows depend on
simd_ops::array_windows/array_chunks. The call graph says otherwise:backend::native::gemm_f32delegates to the externalmatrixmultiplycrate (native.rs:219-236).simd::gemm_f64_tiledis an in-crate fixed-TILE=64kernel using manual index arithmetic withF64x8::from_slice(simd_ops.rs:1060-1098).Neither calls the window primitives. Corrected to describe the real mechanism.
2. Actually wire the primitive into a kernel it was built for
The correction exposed the more interesting gap.
simd_ops.rs:373-391states these primitives exist so that "BLAS-graph GEMM-style kernels iterate over a row ofB(overlapping windows of the inner K dimension) and a column ofA(non-overlapping chunks of the M dimension)" — and the module header (:9-21) warns they are the foundation the hand-rolled kernels lean on, with prior sessions having deleted them as apparent cruft. But almost nothing in-crate used them: the only production call site washpc::blake3's block chunking.heel_f64x8::cosine_f32_to_f64_simdis the canonical shared cosine kernel (bgz-tensor'sStackedN::cosinedelegates to it rather than reimplementing). Its hot loop indexeda[off + j]/b[off + j], so all 16 loads per 8-element chunk carried a bounds check against a dynamic length. Now usesarray_chunks::<f32, 8>, which yields&[f32; 8]— the inner index is provably in bounds, andfrom_arrayskips the second length check.Op order is unchanged, so results are bit-identical — verified standalone across every length 0..300 including unequal-length inputs.
Note on verification
The crate's lib tests do not currently compile, from pre-existing breakage unrelated to this PR: missing
U16x8imports insimd_avx512.rstest modules (15 errors, reproduced on the unmodified tree at6cf6b170).cargo check --libis clean. Equivalence was therefore verified with a standalone harness rather than the in-crate test suite. That breakage is worth its own fix.Follow-up consolidation targets (surveyed, not done here)
A census across ndarray + lance-graph found the remaining duplicates are at the distance-kernel level, not GEMM (lance-graph never reimplements GEMM):
bgz-tensor/hhtl_f32.rs:80l2_dist_sqandstacked_n.rs:145l1_f32— plain scalar loops using no SIMD primitive at all.blasgraph/clam_neighborhood.rs:42scent_hamming_distancereimplements whatblasgraph/types.rs:452already delegates tondarray::hpc::bitwise::hamming_distance_raw.deepnsm/codebook.rs:105vsdeepnsm-v2/space.rs:140— the same PQ/ADC table lookup implemented twice.Co-Authored-By: Claude Opus 5 noreply@anthropic.com
Claude-Session: https://claude.ai/code/session_012wrzeZAdwGYTCKoxamwQht
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