May 2026 monthly summary (lancedb/lance). Focused on delivering high-value performance improvements to distance computations used in indexing and similarity search. Implemented a SIMD-accelerated SQDistCalculator with runtime SIMD backend selection to maximize throughput across architectures without manual tuning. Key deliverables: - SIMD-accelerated distance calculations for L2 and Cosine distances on quantized u8 vectors, replacing the previous scalar path. - Runtime auto-selection of the best SIMD backend (AVX-512 VNNI, AVX2) with a scalar fallback for unsupported architectures, ensuring compatibility and best available performance. - Code change anchored by a targeted commit that wires l2_u8() into the SQDistCalculator, replacing direct scalar calls. Impact: - Significant performance uplift for distance computations, enabling higher indexing throughput and lower query latency in similarity search across diverse hardware. - Improved maintainability and future-proofing by centralizing runtime SIMD dispatch logic. Technologies/skills demonstrated: - SIMD intrinsics and runtime dispatch (AVX2, AVX-512 VNNI) - Performance-oriented refactoring in Rust - Cross-architecture compatibility considerations and testing - Collaboration and code review practices (co-authored-by references in the commit)