SIMD

Building a Custom Golang Vector Database Engine with HNSW Answer-first: Building a custom Go vector database engine with Hierarchical Navigable Small World (HNSW) graphs enables high-throughput vector similarity indexing, memory-mapped SIMD distance calculations, and fast ANN retrieval. This guide analyzes custom HNSW indexing in pure Go, providing microbenchmarks of pure Go AVX2/AVX-512 vector distance unrolling against Rust-based Qdrant and C++ Faiss across 1M 768-dimensional embeddings. Prerequisite: Advanced systems programming in Go 1.25+, understanding of multidimensional vector geometry (L2, Cosine distance), CPU cache hierarchy, SIMD vectorization (AVX2), and graph traversal algorithms (HNSW). ...