perf: use vek32 SIMD for EuclideanDistance#19
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EuclideanDistance used a plain Go loop while CosineDistance already used vek32 with AVX2 SIMD acceleration. Replace the loop with vek32.Distance which provides the same SIMD optimization. Benchmark at 1536 dimensions (OpenAI default) on AMD Ryzen 7 7745HX: Before: EuclideanDistance 335 ns/op After: EuclideanDistance 47 ns/op (7.1x faster) Cosine: CosineDistance 80 ns/op (unchanged, already SIMD) Also adds BenchmarkEuclideanDistance to distance_test.go.
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Summary
EuclideanDistanceused a plain Go loop whileCosineDistancealready usedvek32with AVX2 SIMD acceleration. This replaces the loop withvek32.Distance, resolving the TODO atdistance.go:20.Benchmark at 1536 dimensions (OpenAI default) on AMD Ryzen 7 7745HX:
The
vekdependency is already ingo.mod— no new dependencies.Also adds
BenchmarkEuclideanDistancetodistance_test.gofor parity with the existingBenchmarkCosineSimilarity.Test plan
go test ./...— all tests passgo vet ./...— cleango test -bench=BenchmarkEuclidean -benchmem— confirms 7x speedup🤖 Generated with Claude Code