Implement k-mer distance metrics and constrained generation

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.

pkg/obikmer/kmer_lcs_archsimd_arm64_test.go

@@ -0,0 +1,31 @@
+//go:build goexperiment.simd && arm64
+
+package obikmer
+
+import (
+	"math/rand"
+	"testing"
+)
+
+func TestKmerLCSBatchNEONMatchesReference(t *testing.T) {
+	rng := rand.New(rand.NewSource(5))
+	for _, k := range []int{1, 2, 5, 11, 21, 31} {
+		for _, n := range []int{0, 1, 7, 8, 9, 100} {
+			query := randomKmer(rng, k)
+			partners := make([]uint64, n)
+			for i := range partners {
+				partners[i] = randomKmer(rng, k)
+			}
+			out := make([]KmerLCSResult, n)
+			kmerLCSBatchNEON(query, partners, k, out)
+			for i, p := range partners {
+				if want := KmerLCS(query, p, k); out[i] != want {
+					t.Fatalf("k=%d n=%d partner %d: got %+v, want %+v", k, n, i, out[i], want)
+				}
+			}
+		}
+	}
+}
+
+func BenchmarkKmerLCSNEON21(b *testing.B) { benchBatch(b, 21, kmerLCSBatchNEON) }
+func BenchmarkKmerLCSNEON11(b *testing.B) { benchBatch(b, 11, kmerLCSBatchNEON) }