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.go

@@ -0,0 +1,62 @@
+//go:build goexperiment.simd && arm64
+
+package obikmer
+
+import "simd/archsimd"
+
+// neonLanes is the number of 16-bit lanes of a NEON 128-bit register.
+const neonLanes = 8
+
+// kmerLCSBatchNEON is kmerLCSBatch written directly on archsimd NEON types
+// (see kmer_lcs_simd.go for the cell encoding and recurrence).
+func kmerLCSBatchNEON(query uint64, partners []uint64, k int, out []KmerLCSResult) {
+	var qbase [32]uint16
+	for i := 0; i < k; i++ {
+		qbase[i] = uint16(query>>(2*uint(k-1-i))) & 3
+	}
+
+	pbase := make([]uint16, k*neonLanes)
+	row := make([]uint16, (k+1)*neonLanes)
+	one := archsimd.BroadcastUint16x8(1)
+	bonus := archsimd.BroadcastUint16x8(lcsScoreUnit)
+
+	for start := 0; start < len(partners); start += neonLanes {
+		chunk := partners[start:min(start+neonLanes, len(partners))]
+
+		for lane, p := range chunk {
+			for j := 0; j < k; j++ {
+				pbase[j*neonLanes+lane] = uint16(p>>(2*uint(k-1-j))) & 3
+			}
+		}
+
+		for j := 0; j <= k; j++ {
+			archsimd.BroadcastUint16x8(uint16(lcsLenBase - j)).Store(row[j*neonLanes:])
+		}
+
+		for i := 1; i <= k; i++ {
+			qv := archsimd.BroadcastUint16x8(qbase[i-1])
+			diag := archsimd.LoadUint16x8(row)
+			left := archsimd.BroadcastUint16x8(uint16(lcsLenBase - i))
+			left.Store(row)
+
+			for j := 1; j <= k; j++ {
+				up := archsimd.LoadUint16x8(row[j*neonLanes:])
+				pv := archsimd.LoadUint16x8(pbase[(j-1)*neonLanes:])
+				match := qv.Equal(pv)
+				best := diag.Add(bonus.Masked(match)).Max(up).Max(left).Sub(one)
+				best.Store(row[j*neonLanes:])
+				diag = up
+				left = best
+			}
+		}
+
+		last := row[k*neonLanes:]
+		for lane := range chunk {
+			v := int(last[lane])
+			out[start+lane] = KmerLCSResult{
+				LCS:      v / lcsScoreUnit,
+				AlignLen: lcsLenBase - v%lcsScoreUnit,
+			}
+		}
+	}
+}