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perf: use vek32 SIMD for EuclideanDistance #19

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Merged
ammario merged 1 commit into from
Apr 9, 2026
Merged

perf: use vek32 SIMD for EuclideanDistance #19

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9 changes: 1 addition & 8 deletions distance.go
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Original file line number Diff line number Diff line change
@@ -1,7 +1,6 @@
package hnsw

import (
"math"
"reflect"

"github.com/viterin/vek/vek32"
Expand All @@ -17,13 +16,7 @@ func CosineDistance(a, b []float32) float32 {

// EuclideanDistance computes the Euclidean distance between two vectors.
func EuclideanDistance(a, b []float32) float32 {
// TODO: can we speedup with vek?
var sum float32 = 0
for i := range a {
diff := a[i] - b[i]
sum += diff * diff
}
return float32(math.Sqrt(float64(sum)))
return vek32.Distance(a, b)
}

var distanceFuncs = map[string]DistanceFunc{
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9 changes: 9 additions & 0 deletions distance_test.go
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,15 @@ func TestCosineSimilarity(t *testing.T) {
require.InDelta(t, 0, CosineDistance(a, b), 0.000001)
}

func BenchmarkEuclideanDistance(b *testing.B) {
v1 := randFloats(1536)
v2 := randFloats(1536)
b.ResetTimer()
for i := 0; i < b.N; i++ {
EuclideanDistance(v1, v2)
}
}

func BenchmarkCosineSimilarity(b *testing.B) {
v1 := randFloats(1536)
v2 := randFloats(1536)
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