Vector similarity computations end up dominating a lot of modern search / retrieval workloads, so we’ve been experimenting with SIMD acceleration inside Elasticsearch’s vector search engine.

This write-up goes into:

• how SIMD helps with vector similarity calculations
• implementation details and tradeoffs
• where the gains actually come from
• practical benchmark results

The interesting part for me was how much low-level CPU optimization still mattered even with higher-level ANN improvements.

Would be curious to hear from others working on vector DBs / retrieval systems / HPC-style workloads.

https://www.elastic.co/search-labs/blog/elasticsearch-vector-search-simdvec-engine