Optimize VMAF core: AVX2 PSNR, thread pool job pool, and micro-optimizations#1464
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tolgaki wants to merge 3 commits intoNetflix:masterfrom
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Optimize VMAF core: AVX2 PSNR, thread pool job pool, and micro-optimizations#1464tolgaki wants to merge 3 commits intoNetflix:masterfrom
tolgaki wants to merge 3 commits intoNetflix:masterfrom
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tolgaki
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Feb 15, 2026
tolgaki
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- AVX2 PSNR SSE computation (32 pixels/iteration with runtime CPU dispatch)
- AVX2 SAD for motion feature
- Thread pool job object pool (free list + 64-byte inline data buffer)
- Thread pool thundering herd fix (signal vs broadcast)
- Feature collector initial capacity 8 -> 512
- integer_adm.c: pow(2, N) -> bit shifts/constants; eliminate redundant float conversions
- integer_vif.c: Remove unnecessary epsilon; cache g*g
- predict.c: Stack-allocate SVM nodes; lazy-cache name_with_opt
- convolution.c: Hoist stride multiplication out of inner loops
- Comprehensive test suite (11 tests covering all optimized paths)
- All 18 meson tests pass
Phase 1 - Hot-path computation optimizations: - Hoist stride multiplications out of inner convolution loops (convolution.c) - Replace runtime pow(2,N) calls with compile-time bit shifts and ldexp across integer_adm.c and integer_motion.c (~20 instances) - Remove unnecessary epsilon and cache g*g in VIF statistic loops, eliminating redundant FP division per pixel (integer_vif.c) - Eliminate redundant float conversions in ADM decouple by using integer-domain angle/sign checks instead (integer_adm.c) Phase 1 - Threading and allocation optimizations: - Fix thundering herd: use pthread_cond_signal instead of broadcast in thread pool job enqueue (thread_pool.c) - Use stack allocation for SVM node array in predict path, avoiding per-frame malloc/free churn (predict.c) - Cache generated feature names in model to avoid repeated context creation/destruction per prediction (predict.c, model.h, model.c) - Increase feature vector initial capacity from 8 to 512 to reduce realloc frequency for typical workloads (feature_collector.c) Phase 2 - SIMD coverage: - Add AVX2 SAD (Sum of Absolute Differences) for motion feature, processing 16 uint16 elements per iteration (motion_avx2.c/h) https://claude.ai/code/session_01MaZDdztsZcd5y2PQo1av2G
- Add AVX2 SSE computation for 8-bit PSNR (32 pixels/iteration via cvtepu8_epi16 + madd_epi16), with scalar C fallback and runtime CPU dispatch - Add thread pool job object pool with free list recycling and 64-byte inline data buffer to eliminate per-job malloc/free overhead - Add comprehensive test suite (test_perf_optimizations) covering: thread pool (1000 jobs, data passing), feature collector (capacity 512, 20 features), predict (score consistency with name caching), PSNR/VIF/ADM/motion feature extractors, and end-to-end VMAF scoring https://claude.ai/code/session_01MaZDdztsZcd5y2PQo1av2G
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