mirror of
https://github.com/metabarcoding/obitools4.git
synced 2026-11-09 09:24:41 +00:00
Eric Coissac
a0d8187fd6
Adds support for Hamming distance, constrained k-mer generation, and optimized Longest Common Subsequence (LCS) calculations using scalar, batch, and SIMD methods. This includes new methods for k-mer distance calculation, structures for k-mer constraints, and SIMD-based batch processing for LCS to improve performance and correctness.
32 lines
848 B
Go
32 lines
848 B
Go
//go:build goexperiment.simd && arm64
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package obikmer
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import (
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"math/rand"
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"testing"
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)
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func TestKmerLCSBatchNEONMatchesReference(t *testing.T) {
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rng := rand.New(rand.NewSource(5))
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for _, k := range []int{1, 2, 5, 11, 21, 31} {
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for _, n := range []int{0, 1, 7, 8, 9, 100} {
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query := randomKmer(rng, k)
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partners := make([]uint64, n)
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for i := range partners {
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partners[i] = randomKmer(rng, k)
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}
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out := make([]KmerLCSResult, n)
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kmerLCSBatchNEON(query, partners, k, out)
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for i, p := range partners {
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if want := KmerLCS(query, p, k); out[i] != want {
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t.Fatalf("k=%d n=%d partner %d: got %+v, want %+v", k, n, i, out[i], want)
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}
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}
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}
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}
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}
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func BenchmarkKmerLCSNEON21(b *testing.B) { benchBatch(b, 21, kmerLCSBatchNEON) }
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func BenchmarkKmerLCSNEON11(b *testing.B) { benchBatch(b, 11, kmerLCSBatchNEON) }
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