March 2025 focused on a targeted library upgrade in wazuh-indexer. Delivered a ZSTD compression library upgrade to 1.5.6-1, including Gradle dependency updates and SHA256 checksum updates for license files to ensure build reproducibility and license compliance. No major bugs were reported this month; the upgrade reduces risk associated with older libraries and prepares the stack for future performance improvements. Key achievements include: - ZSTD lib bump to 1.5.6-1 (commit e0a67fd9ca949b14b90dc206231d90158bc35b38) (#17674) - Updated Gradle dependencies to reflect the new ZSTD version - Updated SHA256 checksums for ZSTD license files to maintain integrity and license compliance - Maintained build stability and readiness for future optimizations

February 2025 monthly summary for facebookresearch/faiss focusing on bug fix and benchmarking reliability. Delivered a precise patch to bench_scalar_quantizer_distance to correct parameter order for n and d, ensuring correct dimensional handling and preventing runtime errors or misleading benchmark results. The change is minimal and confined to the function signature, with no API surface changes beyond correct usage.

January 2025 — Delivered and integrated a new AVX-512 Sapphire Rapids optimization build mode for the k-NN component in opensearch-project/k-NN. This includes enabling FAISS_OPT_LEVEL=avx512_spr, updating CI workflows, build scripts, JNI configurations, and corresponding documentation and tests. The work establishes a hardware-accelerated path for Sapphire Rapids CPUs, improving future query throughput and efficiency, and strengthens our build-time optimization capabilities.

Summary for 2024-12: Delivered AVX-512-based acceleration for Hamming distance in Faiss, with a new avx512_spr architecture mode and a popcnt-based optimization. These changes establish groundwork for future speedups and enable higher throughput in similarity search on AVX-512 CPUs. No explicit major bugs were reported this month; focus was on performance enablement and architecture support. Business value includes faster nearest-neighbor search at scale, reduced CPU time per query, and better resource utilization across large deployments. Technologies demonstrated include modern CPU vector intrinsics (AVX-512, _mm512_popcnt_epi64), C/C++ build configuration for architecture-specific optimizations, and performance-driven code changes.