Refactor MatchWords indexing and introduce seed indexing

Corrects the indexing strategy for seed searching, improving performance and introducing a new seed index structure for efficient pattern lookup.

This commit updates the core matching logic by shifting the indexing strategy to use two halves of k/2 bases, resulting in significant speed improvements for sequence matching. It also introduces a new `seedIndex` structure for fast pattern retrieval and implements comprehensive performance testing logic for matching functions. Future features, such as bit-parallel search, are deferred.
This commit is contained in:
Eric Coissac committed 2026-11-03 22:38:11 +11:00
1 parent 02c8ded683
commit 1ab512ab5c
5 files changed
+301 -102

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+56 -3
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@@ -295,9 +295,9 @@ Variantes du protocole :
2. Arrêt anticipé sous le quorum de 90 % : **écartée, mesuré inutile** (Teleostei : 255/255 mots 2. Arrêt anticipé sous le quorum de 90 % : **écartée, mesuré inutile** (Teleostei : 255/255 mots
conservés ; Cyprinidae : 1 201/1 208). Le parallélisme par séquence est déjà dans la variante 1. conservés ; Cyprinidae : 1 201/1 208). Le parallélisme par séquence est déjà dans la variante 1.
3. Bit-parallèle multi-mots en Go (SIMD) : **non écrite**. Cible : les ~74 % de balayage C. 3. Bit-parallèle multi-mots en Go (SIMD) : **non écrite**. Cible : les ~74 % de balayage C.
4. Index de voisinage / Aho-Corasick : écartée pour e >= 2 par le calcul (graines de 4 à 5 bases, 4. Index de voisinage / Aho-Corasick : **j'avais écarté cette piste à tort** (voir section 13) : mon calcul
~250 000 candidats par graine et par mot sur 65 Mb) ; `obicorazick` reste pertinent pour e = 0. ne considérait que le découpage en e+1 morceaux (graines de 4 bases). Le découpage en deux moitiés avec
Non mesurée. voisinage à distance floor(e/2) est très sélectif et a gagné.
Décision en suspens : écrire la variante 3 maintenant, ou d'abord finir le pipeline (paires et indices) Décision en suspens : écrire la variante 3 maintenant, ou d'abord finir le pipeline (paires et indices)
et profiler l'ensemble pour savoir si l'appariement domine encore. et profiler l'ensemble pour savoir si l'appariement domine encore.
@@ -357,3 +357,56 @@ contient la taxonomie NCBI complète).
Non fait : option `-U` (pas de multi-match), séquence de référence `-R`, sets d'amorces, sortie CSV, Non fait : option `-U` (pas de multi-match), séquence de référence `-R`, sets d'amorces, sortie CSV,
CLI. Prochaine étape : variante 3 (bit-parallèle multi-mots, SIMD). CLI. Prochaine étape : variante 3 (bit-parallèle multi-mots, SIMD).
## 13. Mode A, étape 5 : recherche approchée par index de graines (2026-11-03)
**Correction d'une erreur de ma part.** À la section 11 j'écartais l'index de graines « par le calcul » :
je n'avais considéré que le principe des tiroirs avec e+1 morceaux (graines de 4 à 5 bases, des
centaines de milliers de candidats). C'était la mauvaise configuration. Avec **deux moitiés** de
k/2 bases : une fenêtre à <= e = 2d+1 erreurs a au plus d = floor(e/2) erreurs dans l'une des deux
moitiés. On indexe donc, pour chaque moitié de chaque motif (mot et son complément inverse), la moitié
et ses voisins à distance d : 1 + 3L valeurs pour d = 1 (28 pour L = 9, dans 4^9 = 262 144). Sur du
texte aléatoire cela fait ~0,0002 x W candidats par position et par moitié au lieu de 2W comparaisons.
Code : `pkg/obiprimer/match_seed.go`, fonction exportée **`MatchWords`** (remplace la variante `obiapat`,
supprimée). Détails : fenêtre glissante 2 bits/base ; bases ambiguës suivies par un masque (erreur
sûre, aucune correspondance) ; fenêtres circulaires par extension de k-1 bases ; index direct pour les
moitiés de <= 11 bases, haché sur 22 bits au-delà (k >= 24) ; doublons évités par recherche binaire
dans la liste de l'index gauche ; vérification exacte par popcount sur le code complet.
Justesse : égal à l'oracle `matchNaive` sur 11 configurations (k = 9, 12, 18, 24, 25, 31 ; e = 0 à 5 ;
linéaire/circulaire ; séquences avec n, r, y, k, m ; cas hachés) ; égal à l'oracle sur les 40
premières séquences réelles de chaque jeu ; égal à `obiapat` sur l'intégralité des 999 407
occurrences de Teleostei ; et mêmes nombres de séquences que le journal d'ecoPrimers, mot par mot
(1 207 et 255 mots, 0 différence).
Mesures `e = 3`, M3 Max :
| Jeu | obiapat 1 worker | MatchWords 1 worker | obiapat 16 workers | MatchWords 16 workers |
|---|---|---|---|---|
| Teleostei (255 mots, 65 Mb) | 113 s | **0,42 s** (x270) | 9,4 s | **43 ms** (x220) |
| Cyprinidae (1 207 mots, 5,4 Mb) | non mesuré | non mesuré | 3,8 s | **13 ms** |
Mémoire allouée par l'appariement : 120 Mo (Teleostei), 51 Mo (Cyprinidae), essentiellement le
stockage des occurrences.
**Profil du pipeline complet, 16 workers** (lecture du FASTA gz comprise, taxonomie exclue) :
| Étape | Cyprinidae | Teleostei |
|---|---|---|
| lecture + sélection | 0,46 s | 0,42 s |
| mots stricts | 0,10 s | 1,02 s |
| recherche approchée | 0,014 s | 0,041 s |
| paires + indices + Tm | 1,09 s | 1,20 s |
| total | ~1,7 s | ~2,7 s |
ecoPrimers complet : 134 s et 350 s, soit environ 80 et 130 fois plus. Les résultats complets restent
ceux de la section 12 (Cyprinidae identique sur toutes les colonnes ; Teleostei : mêmes 407 paires,
6 à -1 séquence).
**Décision SIMD** : l'appariement ne représente plus que 1 % du temps, donc un noyau SIMD pour la
recherche n'a plus d'objet. Les étapes restantes (lecture 0,4 s, mots stricts 1 s sur Teleostei,
paires 1,1 s) sont celles à profiler si l'on veut aller plus loin ; le tri des paires (kmer, id)
dans `SeqKmerCounter` et la phase 2 de `FindPairs` en sont les candidats.
Non fait : option `-U`, `-R`, sets d'amorces, mode B, sortie CSV, CLI `obiprimers`.
-87
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@@ -1,87 +0,0 @@
package obiprimer
import (
"fmt"
"git.metabarcoding.org/obitools/obitools4/obitools4/pkg/obiapat"
"git.metabarcoding.org/obitools/obitools4/obitools4/pkg/obiseq"
)
// apatSafe returns seq when it only holds a, c, g, t; otherwise a copy where u
// becomes t and every other symbol becomes z. obiapat reads IUPAC codes in the
// sequence (an n matches every base); z is the symbol it matches with nothing,
// which gives back the ecoPrimers rule "an ambiguous base is an error".
func apatSafe(seq *obiseq.BioSequence) *obiseq.BioSequence {
raw := seq.Sequence()
clean := true
for _, b := range raw {
if b != 'a' && b != 'c' && b != 'g' && b != 't' {
clean = false
break
}
}
if clean {
return seq
}
out := make([]byte, len(raw))
for i, b := range raw {
switch b {
case 'a', 'c', 'g', 't':
out[i] = b
case 'A', 'C', 'G', 'T':
out[i] = b + 'a' - 'A'
case 'u', 'U':
out[i] = 't'
default:
out[i] = 'z'
}
}
return obiseq.NewBioSequence(seq.Id(), out, "")
}
// matchApat searches the words with obiapat, the bit-parallel Baeza-Yates /
// Manber matcher of OBITools (the engine of ecoPrimers and obipcr).
func matchApat(c *Corpus, words []uint64, p MatchParams) ([]WordMatches, error) {
if err := p.validate(); err != nil {
return nil, err
}
fwd, rev := wordStrings(words, p.K)
pats := make([][2]obiapat.ApatPattern, len(words))
for i := range words {
var err error
if pats[i][0], err = obiapat.MakeApatPattern(fwd[i], p.MaxErrors, false); err != nil {
return nil, fmt.Errorf("obiprimer: pattern %s: %w", fwd[i], err)
}
if pats[i][1], err = obiapat.MakeApatPattern(rev[i], p.MaxErrors, false); err != nil {
return nil, fmt.Errorf("obiprimer: pattern %s: %w", rev[i], err)
}
}
mk := func() scanFunc {
var recycle []obiapat.ApatSequence
return func(seq *obiseq.BioSequence, emit func(word, pos, errors int, reverse bool)) {
n := seq.Len()
if n < p.K {
return
}
aseq, err := obiapat.MakeApatSequence(apatSafe(seq), p.Circular, recycle...)
if err != nil {
panic(fmt.Sprintf("obiprimer: cannot encode sequence %s: %v", seq.Id(), err))
}
recycle = append(recycle[:0], aseq)
for w := range pats {
for strand := range 2 {
for _, m := range pats[w][strand].FindAllIndex(aseq, 0, -1) {
// On circular sequences obiapat also scans a copy of the
// start: those hits repeat the ones at start-n.
if m[0] >= 0 && m[0] < n {
emit(w, m[0], m[2], strand == 1)
}
}
}
}
}
}
return matchCorpus(c, words, mk), nil
}
+214
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@@ -0,0 +1,214 @@
package obiprimer
import (
"math/bits"
"slices"
"git.metabarcoding.org/obitools/obitools4/obitools4/pkg/obiseq"
)
// seedIndex maps the value of one half of a window to the patterns that are
// candidates for it: those whose same half is within d substitutions of it.
//
// Pigeonhole: a window with at most e = 2d+1 errors against a pattern has at
// most d errors in at least one of the two halves of the pattern, so the
// windows to verify are exactly those found through one of the two indexes.
// Indexing the half of every pattern together with its neighbours at distance
// d (1 + 3L values for d = 1, 1 + 3L + 9L(L-1)/2 for d = 2, L = 9 bases) costs
// a few tens of entries per pattern, and a window of a random text finds about
// patterns x neighbours / 4^L candidates, far fewer than the patterns.
type seedIndex struct {
shift uint // slot = seed >> shift when hashed, else seed
hashed bool // the seed space is too large for a direct table
offsets []uint32 // CSR: ids[offsets[s]:offsets[s+1]] are the candidates of slot s
ids []int32 // pattern ids (2*word + strand)
}
const maxDirectSeedBits = 22
func (x *seedIndex) slot(seed uint64) uint32 {
if !x.hashed {
return uint32(seed)
}
return uint32((seed * 0x9E3779B97F4A7C15) >> x.shift)
}
func (x *seedIndex) candidates(seed uint64) []int32 {
s := x.slot(seed)
return x.ids[x.offsets[s]:x.offsets[s+1]]
}
// neighbours calls fn for v and every value within d substitutions of it (each
// once), v being L bases packed on 2 bits.
func neighbours(v uint64, L, d int, fn func(uint64)) {
var rec func(v uint64, from, left int)
rec = func(v uint64, from, left int) {
fn(v)
if left == 0 {
return
}
for pos := from; pos < L; pos++ {
for alt := uint64(1); alt <= 3; alt++ {
rec(v^(alt<<(2*uint(pos))), pos+1, left-1)
}
}
}
rec(v, 0, d)
}
// newSeedIndex indexes, for every pattern, the half of the given width (in
// bases) selected by (code >> shiftBases*2) & mask, with its neighbours at
// distance d.
func newSeedIndex(patterns []uint64, shiftBases, width, d int) *seedIndex {
x := &seedIndex{}
bitsNeeded := 2 * width
if bitsNeeded > maxDirectSeedBits {
x.hashed, bitsNeeded = true, maxDirectSeedBits
x.shift = uint(64 - maxDirectSeedBits)
}
nslots := 1 << uint(bitsNeeded)
halfMask := uint64(1)<<(2*uint(width)) - 1
type entry struct {
slot uint32
id int32
}
var entries []entry
var scratch []uint32
for id, pat := range patterns {
half := (pat >> (2 * uint(shiftBases))) & halfMask
scratch = scratch[:0]
neighbours(half, width, d, func(v uint64) { scratch = append(scratch, x.slot(v)) })
slices.Sort(scratch)
scratch = slices.Compact(scratch) // hashing may merge neighbours
for _, s := range scratch {
entries = append(entries, entry{s, int32(id)})
}
}
x.offsets = make([]uint32, nslots+1)
for _, e := range entries {
x.offsets[e.slot+1]++
}
for i := 1; i <= nslots; i++ {
x.offsets[i] += x.offsets[i-1]
}
x.ids = make([]int32, len(entries))
fill := slices.Clone(x.offsets[:nslots])
for _, e := range entries {
x.ids[fill[e.slot]] = e.id
fill[e.slot]++
}
return x
}
// MatchWords finds every approximate occurrence, on both strands, of the given
// canonical words in the sequences of the corpus, with the rules of MatchParams
// (substitutions only, ambiguous bases count as errors, circular junction).
//
// It uses the pigeonhole seed indexes above. It returns exactly what the
// reference matchNaive returns, and what ecoPrimers' fuzzy search finds, in a
// small fraction of the time: on the Teleostei tutorial data (255 words, 3909
// sequences, 65 Mb, e = 3) 0.42 s on one core, against 113 s for the
// obiapat (agrep) search it replaces.
func MatchWords(c *Corpus, words []uint64, p MatchParams) ([]WordMatches, error) {
if err := p.validate(); err != nil {
return nil, err
}
k := p.K
d := p.MaxErrors / 2
// pattern 2w is the word, 2w+1 its reverse complement
patterns := make([]uint64, 2*len(words))
for i, w := range words {
patterns[2*i] = codeOf(wordCodes(w, k, false), k)
patterns[2*i+1] = codeOf(wordCodes(w, k, true), k)
}
right := k / 2 // the right half holds the low bits
left := k - right
leftIdx := newSeedIndex(patterns, right, left, d)
rightIdx := newSeedIndex(patterns, 0, right, d)
rightMask := uint64(1)<<(2*uint(right)) - 1
windowMask := uint64(1)<<(2*uint(k)) - 1
lowBits := uint64(0x5555555555555555) & windowMask
maxErr := p.MaxErrors
mk := func() scanFunc {
return func(seq *obiseq.BioSequence, emit func(word, pos, errors int, reverse bool)) {
raw := seq.Sequence()
n := len(raw)
if n < k {
return
}
total := n // number of bases fed to the rolling window
windows := n - k + 1
if p.Circular {
total, windows = n+k-1, n
}
var code, bad uint64 // window, and its non-ACGT positions (bit 2j)
verify := func(pos int, id int32) {
pat := patterns[id]
x := code ^ pat
if errs := bits.OnesCount64(((x | x>>1) & lowBits) | bad); errs <= maxErr {
emit(int(id)>>1, pos, errs, id&1 == 1)
}
}
for i := 0; i < total; i++ {
j := i
if j >= n {
j -= n
}
v := baseCode[raw[j]]
isBad := uint64(0)
if v > 3 {
v, isBad = 0, 1
}
code = (code<<2 | uint64(v)) & windowMask
bad = (bad<<2 | isBad) & windowMask
if i < k-1 {
continue
}
pos := i - k + 1
if pos >= windows {
break
}
leftCands := leftIdx.candidates(code >> (2 * uint(right)))
for _, id := range leftCands {
verify(pos, id)
}
for _, id := range rightIdx.candidates(code & rightMask) {
// ids are sorted: skip the ones the left index has just verified
if _, done := slices.BinarySearch(leftCands, id); !done {
verify(pos, id)
}
}
}
}
}
return matchCorpus(c, words, mk), nil
}
// wordCodes returns the 2-bit codes (A=0..T=3) of the word or of its reverse
// complement, 5' to 3'.
func wordCodes(word uint64, k int, reverse bool) []byte {
fwd, rev := wordStrings([]uint64{word}, k)
s := fwd[0]
if reverse {
s = rev[0]
}
return codesOf(s)
}
// codeOf packs codes (each 0..3) on 2 bits, first base in the high bits.
func codeOf(codes []byte, k int) uint64 {
var v uint64
for _, c := range codes[:k] {
v = v<<2 | uint64(c)
}
return v
}
+30 -11
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@@ -71,8 +71,8 @@ func TestMatchParamsValidation(t *testing.T) {
if _, err := matchNaive(c, nil, p); err == nil { if _, err := matchNaive(c, nil, p); err == nil {
t.Errorf("%+v must be rejected", p) t.Errorf("%+v must be rejected", p)
} }
if _, err := matchApat(c, nil, p); err == nil { if _, err := MatchWords(c, nil, p); err == nil {
t.Errorf("%+v must be rejected by obiapat variant", p) t.Errorf("%+v must be rejected by MatchWords", p)
} }
} }
} }
@@ -130,7 +130,7 @@ func TestMatchersAgreeWithReference(t *testing.T) {
for _, tc := range []struct { for _, tc := range []struct {
k, e int k, e int
circular bool circular bool
}{{18, 0, false}, {18, 1, true}, {18, 3, false}, {18, 3, true}, {12, 2, true}, {25, 4, false}} { }{{18, 0, false}, {18, 1, true}, {18, 3, false}, {18, 3, true}, {12, 2, true}, {25, 4, false}, {18, 2, true}, {18, 5, false}, {31, 3, true}, {24, 3, false}, {9, 2, true}, {18, 6, true}, {18, 7, false}, {20, 8, true}, {12, 11, false}} {
t.Run(fmt.Sprintf("k%d-e%d-circ%v", tc.k, tc.e, tc.circular), func(t *testing.T) { t.Run(fmt.Sprintf("k%d-e%d-circ%v", tc.k, tc.e, tc.circular), func(t *testing.T) {
rng := rand.New(rand.NewSource(int64(tc.k*100 + tc.e))) rng := rand.New(rand.NewSource(int64(tc.k*100 + tc.e)))
var wordStr []string var wordStr []string
@@ -155,13 +155,13 @@ func TestMatchersAgreeWithReference(t *testing.T) {
t.Fatalf("test data too poor: only %d reference matches", total) t.Fatalf("test data too poor: only %d reference matches", total)
} }
got, err := matchApat(c, words, p) got, err := MatchWords(c, words, p)
if err != nil { if err != nil {
t.Fatal(err) t.Fatal(err)
} }
for i := range want { for i := range want {
if !reflect.DeepEqual(got[i].Matches, want[i].Matches) { if !reflect.DeepEqual(got[i].Matches, want[i].Matches) {
t.Fatalf("word %d: obiapat gives %v\nreference gives %v", i, got[i].Matches, want[i].Matches) t.Fatalf("word %d: MatchWords gives %v\nreference gives %v", i, got[i].Matches, want[i].Matches)
} }
} }
}) })
@@ -200,7 +200,7 @@ func readEcoCounts(t *testing.T, path string) map[string]int {
// strict word test). For every strict word, ecoPrimers logs the number of // strict word test). For every strict word, ecoPrimers logs the number of
// example sequences in which the fuzzy search (-e 3, both strands, circular) // example sequences in which the fuzzy search (-e 3, both strands, circular)
// finds it; testdata/ecoprimers_*.words.tsv holds those numbers. // finds it; testdata/ecoprimers_*.words.tsv holds those numbers.
func TestMatchApatAgainstEcoPrimers(t *testing.T) { func TestMatchWordsAgainstEcoPrimers(t *testing.T) {
dir := os.Getenv("OBIPRIMERS_TUTORIAL") dir := os.Getenv("OBIPRIMERS_TUTORIAL")
if dir == "" { if dir == "" {
t.Skip("OBIPRIMERS_TUTORIAL not set") t.Skip("OBIPRIMERS_TUTORIAL not set")
@@ -244,7 +244,7 @@ func TestMatchApatAgainstEcoPrimers(t *testing.T) {
runtime.GC() runtime.GC()
runtime.ReadMemStats(&before) runtime.ReadMemStats(&before)
start := time.Now() start := time.Now()
res, err := matchApat(corpus, words, MatchParams{K: 18, MaxErrors: 3, Circular: true}) res, err := MatchWords(corpus, words, MatchParams{K: 18, MaxErrors: 3, Circular: true})
elapsed := time.Since(start) elapsed := time.Since(start)
runtime.ReadMemStats(&after) runtime.ReadMemStats(&after)
if err != nil { if err != nil {
@@ -258,10 +258,29 @@ func TestMatchApatAgainstEcoPrimers(t *testing.T) {
for _, s := range corpus.Seqs { for _, s := range corpus.Seqs {
bases += s.Len() bases += s.Len()
} }
t.Logf("obiapat: %d words x %d sequences (%.1f Mb) e=3 in %s (%.0f Mb/s per word-strand pass); %d matches; heap +%.0f MB, allocated %.0f MB", t.Logf("%d words x %d sequences (%.1f Mb) e=3 in %s; %d matches; allocated %.0f MB",
len(words), len(corpus.Seqs), float64(bases)/1e6, elapsed.Round(time.Millisecond), len(words), len(corpus.Seqs), float64(bases)/1e6, elapsed.Round(time.Millisecond), nm,
float64(bases)*float64(len(words))*2/1e6/elapsed.Seconds(), nm, float64(after.TotalAlloc-before.TotalAlloc)/1e6)
float64(after.HeapAlloc-before.HeapAlloc)/1e6, float64(after.TotalAlloc-before.TotalAlloc)/1e6)
// On real sequences (with their ambiguity codes), the first ones must give
// exactly what the reference implementation gives.
sub := &Corpus{Seqs: corpus.Seqs[:40], Roles: corpus.Roles[:40]}
wantSub, err := matchNaive(sub, words, MatchParams{K: 18, MaxErrors: 3, Circular: true})
if err != nil {
t.Fatal(err)
}
gotSub, err := MatchWords(sub, words, MatchParams{K: 18, MaxErrors: 3, Circular: true})
if err != nil {
t.Fatal(err)
}
subMatches := 0
for i := range wantSub {
subMatches += len(wantSub[i].Matches)
if !reflect.DeepEqual(gotSub[i].Matches, wantSub[i].Matches) {
t.Fatalf("word %d differs from the reference on the first 40 sequences", i)
}
}
t.Logf("first 40 sequences identical to the reference (%d matches)", subMatches)
eco := readEcoCounts(t, filepath.Join("testdata", "ecoprimers_"+tc.name+".words.tsv")) eco := readEcoCounts(t, filepath.Join("testdata", "ecoprimers_"+tc.name+".words.tsv"))
compared, differ := 0, 0 compared, differ := 0, 0
+1 -1
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@@ -136,7 +136,7 @@ func TestPairsAgainstEcoPrimers(t *testing.T) {
for i, w := range sw.Words { for i, w := range sw.Words {
words[i] = w.Kmer words[i] = w.Kmer
} }
wm, err := matchApat(corpus, words, MatchParams{K: 18, MaxErrors: 3, Circular: true}) wm, err := MatchWords(corpus, words, MatchParams{K: 18, MaxErrors: 3, Circular: true})
if err != nil { if err != nil {
t.Fatal(err) t.Fatal(err)
} }