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Michael Norris
8d8881ee3f
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5022 I used the C++ benchmark in https://github.com/facebookresearch/faiss/pull/4971 but modified it a lot to measure on my machine. ``` The BLAS threshold for flat distance computation was a fixed query count (nq < 20), regardless of vector dimension. This meant BLAS was used for as few as 20 queries even at d=128, where the sequential SIMD path is 10-25x faster. The optimal crossover between sequential and BLAS depends on the total work in the query matrix (nq × d), not just query count. Benchmarking across dimensions shows the crossover at roughly nq × d ≈ 128,000: ┌──────┬────────────────────┬──────────────────────────────┐ │ d │ Old threshold (nq) │ New effective threshold (nq) │ ├──────┼────────────────────┼──────────────────────────────┤ │ 128 │ 20 │ 1000 │ ├──────┼────────────────────┼──────────────────────────────┤ │ 256 │ 20 │ 500 │ ├──────┼────────────────────┼──────────────────────────────┤ │ 512 │ 20 │ 250 │ ├──────┼────────────────────┼──────────────────────────────┤ │ 1024 │ 20 │ 125 │ ├──────┼────────────────────┼──────────────────────────────┤ │ 2048 │ 20 │ 63 │ └──────┴────────────────────┴──────────────────────────────┘ This change replaces the comparison nq < threshold with nq * d < threshold and sets the default to 128,000. Speedup vs old default (nb=100,000, L2 (same as IP for me), compiled with optimizations): nq | d=128 | d=256 | d=512 | d=1024 | d=2048 ----|-------|-------|-------|--------|------- 20 | 22.8x | 9.3x | 4.5x | 2.6x | 1.4x 50 | 19.4x | 9.3x | 4.7x | 2.0x | 1.2x 100 | 13.3x | 5.6x | 2.1x | 1.0x | 1.0x 200 | 8.2x | 2.7x | 1.1x | 1.0x | 1.0x 500 | 3.2x | 1.0x | 1.0x | 1.0x | 1.0x 1000 | 1.0x | 1.0x | 1.0x | 1.0x | 1.0x No regressions observed — the worst case is d=1024, nq=100 at 0.99x (within noise). ``` Reviewed By: mdouze Differential Revision: D99032435 fbshipit-source-id: 45153e1d5add3ab20efa0e1e90cfc18533f4ac97