stride-align 0.4.0

SIMD-accelerated fuzzy string matching, sequence alignment (Smith-Waterman / Needleman-Wunsch), edit distance (Levenshtein / OSA / Indel / Jaro-Winkler), phonetic encoders (Soundex / Metaphone / NYSIIS / Caverphone), and Dynamic Time Warping — x86 / ARM / LoongArch / POWER kernels with first-class CJK.

stride-align

Languages: English · 简体中文

stride-align is a SIMD-accelerated Python library for fuzzy string matching, sequence alignment, phonetic encoding, and time-series distance. It implements Smith-Waterman and Needleman-Wunsch (local and global alignment), Levenshtein and Damerau-Levenshtein edit distance, Jaro and Jaro-Winkler similarity, Hamming and Indel distance, Dynamic Time Warping for int16/float32/float64 numeric sequences, and the standard phonetic-encoder family — Soundex, Metaphone (Philips and jellyfish variants), Double Metaphone (Apache Commons and Python-package variants), NYSIIS, the Match Rating Approach codex and comparator, and Caverphone 2. All scoring kernels are hand-vectorised behind a runtime CPU dispatcher: x86 SSE4.1, AVX2, AVX-512BW+VL, AVX10/256 and AVX10/512; ARM NEON and SVE/SVE2; LoongArch LSX and LASX; PowerPC VSX; with a portable scalar fallback. The library binds to Python through nanobind (vectorcall on every entry point), targets Python 3.12+, and accepts bytes, str (UCS-1/UCS-2/UCS-4 via zero-copy), and NumPy ndarray inputs.

The library is built for high-throughput fuzzy-match workloads — record linkage, deduplication, search-as-you-type, NLP token similarity, and bioinformatics-style local alignment — with first- class support for CJK text: UCS-2 inputs route to a 16-bit-token Farrar kernel rather than being downconverted to bytes, and Chinese, Japanese and Korean strings exercise the same SIMD path as ASCII. The all-pairs surface (cdist, cdist_above_threshold, cdist_top_k, cdist_top_k_per_query) combines per-target SIMD scoring with closed-form length-difference pruning that skips scoring work when a target's max possible similarity provably can't clear a running heap minimum or threshold. Substitution matrices (BLOSUM, PAM) and affine gap penalties are supported on the alignment path. Cross-architecture correctness is validated on real hardware (no emulators) — Intel/AMD x86, Apple Silicon and Graviton ARM, Loongson LoongArch, and IBM POWER8 — with benchmarks tracked at stride-align.com/BENCHMARK.html and the project home at stride-align.com.

Instead of giving you a lecture, we're going to learn by doing. Let's dive right into how it works.

Installation

pip install stride-align

On Loongson systems, install NumPy from your Linux distribution before installing stride-align, and grab the LoongArch64 wheel from one of the mirrors below — PyPI does not index the linux_loongarch64 platform tag, so a direct download is the only binary path.

sudo apt install python3-numpy
PY=$(python3 -c 'import sys; print(f"cp{sys.version_info.major}{sys.version_info.minor}")')

Pick old-world or new-world

LoongArch has two incompatible binary worlds. They differ in which program loader the dynamic linker uses and which glibc ABI is in play, and a wheel from one world cannot run on the other.

• Old-world — stock Kylin and the original Loongson distributions. Loader at /lib64/ld.so.1, glibc 2.28-era.
• New-world — recent LoongArch distros and any box where the new loader /lib64/ld-linux-loongarch-lp64d.so.1 has been installed. Targets glibc 2.36+.

One-liner check on your machine:

test -e /lib64/ld-linux-loongarch-lp64d.so.1 && echo new-world || echo old-world

We ship one wheel per world. Each is statically linked against libstdc++ / libgcc, so the only ABI difference users see is the loader / glibc world. Pick the matching URL below.

Old-world wheel

pip install \
  https://github.com/adamdeprince/stride-align/releases/download/v0.3.0/stride_align-0.3.0-1.oldworld-${PY}-${PY}-linux_loongarch64.whl

Mirror:

pip install \
  https://stride-align.com/wheels/v0.3.0/stride_align-0.3.0-1.oldworld-${PY}-${PY}-linux_loongarch64.whl

New-world wheel

The new-world wheel needs the new loader symlinked into place. One sudo step, once per box, leaves old-world binaries unaffected:

sudo ln -sf /opt/loongson-gcc-16.1.0/sysroot/lib64/ld-linux-loongarch-lp64d.so.1 \
            /lib64/ld-linux-loongarch-lp64d.so.1

(Distro packagers usually drop an equivalent symlink as part of the new-world transition, in which case you can skip this.)

Then:

pip install \
  https://github.com/adamdeprince/stride-align/releases/download/v0.3.0/stride_align-0.3.0-1.newworld-${PY}-${PY}-linux_loongarch64.whl

Mirror:

pip install \
  https://stride-align.com/wheels/v0.3.0/stride_align-0.3.0-1.newworld-${PY}-${PY}-linux_loongarch64.whl

Other notes

Prebuilt LoongArch64 wheels are available for Python 3.12, 3.13, and 3.14 — in both worlds — on both mirrors. The build details (toolchains, RPATH wrapper, static C++ runtime) live in docs/loongson-build.md. If you are on a different Python (or just want to build from source), pip install stride-align falls back to the source distribution on PyPI, which compiles the LSX/LASX kernels locally.

First, just a disclaimer: I'm not using religious texts here to push an agenda - for this demo I need multiple largish public domain documents that have the same meaning but are phrased differently. The Bible just happens to fit that demo requirement freakishly well.

Imagine we have two sentences - let's use the first sentence in Genesis for this:

In the American Standard Version we have: "In the beginning God created the heavens and the earth."

In the King James Version we have: "In the beginning God created the heaven and the earth."

We can see with our eyes there's a difference - heavens vs heaven. But how do we quantify this difference? We'd use this little bit of code:

import stride_align as sa

print(sa.smith_waterman_normalized_score(
      "In the beginning God created the heavens and the earth.",
      "In the beginning God created the heaven and the earth."))

When we run this it prints:

0.9907407407407407

Normalized scores are between 0 and 1. A score of 1 means the inputs are an exact match under the default scoring model. Scores near 0 mean the inputs have little in common, though Smith-Waterman may still find small local matches inside otherwise unrelated strings.

Now let's change the text and see what happens to the score.

import stride_align as sa

print(sa.smith_waterman_normalized_score(
      "In the beginning God created the heavens and the earth.",
      "The quick brown fox jumped over the lazy dog."))

and Python prints

0.12222222222222222

Starting to get the idea? The more similar the strings, the higher the score.

Let's build a bigger example, something that gives us a feel for the library's performance. You'll probably notice that we switch between Smith-Waterman and Needleman-Wunsch and may be wondering which to use when. Use Needleman-Wunsch when you want to compare the whole input against the whole input. Use Smith-Waterman when you want to find the best matching region inside larger inputs.

Okay, let's move on to the demo code. You need requests for this part of the demo:

pip install requests

import os, time, requests
import stride_align as sa

if not os.path.exists("kjv.txt"):
    response = requests.get("https://openbible.com/textfiles/kjv.txt")
    response.raise_for_status()
    response.encoding = "utf-8-sig"
    open("kjv.txt", "w", encoding="utf-8").write(response.text)

lines = [line.strip().lower() for line in open("kjv.txt")][2:]

while True:
    if not (query := input("Enter a snippet to match.  Press enter to end.\n")):
        break
    t = time.perf_counter()
    scores = sa.needleman_wunsch_normalized_scores(query.lower(), lines)
    best = int(scores.argmax())
    print()
    print("Score:", float(scores[best]))
    print(lines[best])
    print("Search time: %0.2fms" % ((time.perf_counter() - t) * 1000))
    print()
    print()

Now how can we use this? Suppose we have a random Bible verse and want to know what chapter and verse it comes from. grep you say? Oh, heavens, no, we made a mistake. The verse we have is from a different translation, say the Catholic Public Domain, and what we have on our computer is the King James Bible. grep's exact string matching won't work here. How do we find the chapter and verse? We search for the "closest" or "most similar" string using stride-align, of course.

In our demo the first part concerns itself with downloading and caching. The good folks at Open Bible put this text where it's HTTP-reachable, but we want to be respectful of their IT budget so we cache what we download. It's just good citizenship.

In the next part we load all of the lines into a list. We remove newlines and make everything lower case because we don't want to get all fiddly about whether we're holding the shift key.

Lastly that while True: loop collects a line of text, presumably the Bible verse from the Catholic version of the Bible we want to look up the chapter and verse for, and matches it against all of the lines in the King James Bible using the batch form of Needleman-Wunsch. It returns an array of scores. We use argmax() to find the best-scoring line and then print the line associated with that index. Let's try it.

I'm going to use Jeremiah 4:28 from the Catholic Bible - it's actually quite different from the same verse in the King James Bible. Let's see what happens ...

$ python3 demo2.py
Enter a snippet to match.  Press enter to end.
The earth will mourn, and the heavens will lament from above. For I have spoken, I have decided, and I have not regretted. Neither will I be turned away from it.

Score: 0.3598901098901099
jeremiah 4:28	for this shall the earth mourn, and the heavens above be black: because i have spoken [it], i have purposed [it], and will not repent, neither will i turn back from it.
Search time: 206.51ms

... and we found it! And pretty quickly too.

Now let's do another demo: spell checking.

This is a toy spell checker, not a production one. It ignores punctuation, capitalization, word frequency, proper nouns, and context. The point is to show the same one-query-against-many-candidates pattern on a familiar task.

import os, sys
import stride_align as sa

paths = ['/usr/share/dict/words',
         '/usr/dict/words',
         '/var/lib/dict/words',
         '/etc/dictionaries-common/words']

for path in paths:
    if os.path.exists(path):
        break
else:
    print("Sorry, I can't find your dictionary", file=sys.stderr)
    exit(1)


words = [line.strip().lower() for line in open(path)]


for line in sys.stdin:
    new_line = []
    for word in line.split():
        scores = sa.needleman_wunsch_normalized_scores(word.lower(), words)
        word = words[int(scores.argmax())]
        new_line.append(word)
    print(' '.join(new_line), flush=True)

The first thing this script does is try to find our operating system's list of correctly spelled words. Its location can vary from distribution to distribution. Once we've found it, we load it, strip off newlines and start the act of spell checking.

The spell checking looks a lot like the matching we did before. For each candidate word, we match it against all of the words in our list of correctly spelled words, use argmax() to find the highest-scoring candidate, and replace the word with that candidate. We could speed things up with some optimizations, like not searching for a match for correctly spelled words, but this is a demo and that optimization is left as an exercise for the reader.

Let's see how it works!

$ cat - | python3 demo3.py
this is a demonstrtion of a spel checker
it doesn't matter that I can't spell corectly

this is a demonstration of a spell checker
it doesn't matter that i can't spell correctly

Details

The current scaffold provides:

• Needleman-Wunsch score-only alignment
• Needleman-Wunsch alignment with traceback
• Smith-Waterman score-only alignment
• Smith-Waterman alignment with traceback
• A backend layout that matches the specialization pattern used in massive-speedup
• CPU/backend detection and Python-side backend dispatch

The native boundary accepts:

• bytes against bytes
• str against str
• sequences of immutable hashable Python objects
• mixed sequence/object inputs where a str or bytes side is treated as a sequence

Direct bytes versus str pairs raise TypeError.

The current implementations are generic dynamic-programming kernels with preprocessing that serializes Python inputs into 8, 16, 32, or 64-bit token streams. SIMD-specialized backends can replace the backend translation units later without changing the Python API.

Score-only functions return numeric scores. The normalized variants return scores between 0 and 1. Path functions return alignment result objects containing the score, aligned values, operations, and CIGAR-style summaries where available.

API

import stride_align

score = stride_align.needleman_wunsch_score("ACGT", "ACCT")
scores = stride_align.Scores("ACGT", variant="needleman_wunsch").compare(["ACCT", "AGGT"])
result = stride_align.smith_waterman_path("ACCGT", "CCG")
wide_result = stride_align.smith_waterman_path("ACCGT", "CCG", width=64)
object_result = stride_align.needleman_wunsch_path(
    [frozenset({1}), frozenset({2})],
    [frozenset({1}), frozenset({3})],
)

print(score)
print(scores)
print(result.score, result.aligned_query, result.aligned_target, result.operations)
print(wide_result.score)
print(object_result.aligned_query, object_result.aligned_target)

Use Scores(...).compare([...]) or the *_scores() functions for one-query against many-target score workloads. That path prepares the query/profile once and is the preferred performance API for repeated English/Chinese text comparisons.

Traceback outputs preserve the paired fast-path type:

• str inputs return aligned str
• bytes inputs return aligned bytes
• sequence/object inputs return aligned tuple values with None gaps

Pass width=8, 16, 32, or 64 to force the internal token/scoring width instead of using automatic selection.

Some functions expose CIGAR strings, short for "Concise Idiosyncratic Gapped Alignment Report". CIGAR is the compact alignment-operation notation used by SAM/BAM tooling. If you want the full formal version, see the SAM specification.

Substitution matrices (BLOSUM, PAM)

For protein alignment, stride_align.matrices ships the canonical BLOSUM and PAM substitution matrices. Pass any of them via the matrix= kwarg on smith_waterman_score, needleman_wunsch_score, or their _scores batch counterparts:

import stride_align
from stride_align.matrices import blosum62, pam250

# Local alignment, NCBI standard BLOSUM62 with affine gaps (open=-11,
# extend=-1). matrix= is mutually exclusive with match_score / mismatch_score.
stride_align.smith_waterman_score(
    "HEAGAWGHEE", "PAWHEAE",
    matrix=blosum62,
    gap_open_score=-11, gap_extend_score=-1,
)

# Batch (1 query × N targets) with profile reuse — the recommended
# path for "score one query against a library".
stride_align.smith_waterman_scores(
    "HEAGAWGHEE",
    ["PAWHEAE", "HEAGAWGHEE", "MEEPS"],
    matrix=pam250, gap_open_score=-14, gap_extend_score=-2,
)

# Custom matrices: parse any NCBI-format text file
custom = stride_align.matrices.SubstitutionMatrix.from_ncbi_text(
    open("/path/to/BLOSUM62").read(),
    name="BLOSUM62",
    gap_open=-11, gap_extend=-1,
)

Each built-in SubstitutionMatrix exposes its alphabet, matrix data (int8 ndarray), and recommended gap defaults via .gap_score (linear), .gap_open, and .gap_extend. Both linear gaps (gap_score=) and affine gaps (gap_open_score= + gap_extend_score=) are supported on the AVX-512 backend; other SIMD backends currently fall back to the scalar generic kernel for matrix-mode.

The shipped matrix values come from the NCBI BLAST distribution ftp.ncbi.nih.gov/blast/matrices/, which carries the canonical reference scores. The original publications are:

• BLOSUM45 / 50 / 62 / 80 / 90 — Henikoff S., Henikoff J.G. (1992). Amino acid substitution matrices from protein blocks. PNAS 89(22):10915–10919. doi:10.1073/pnas.89.22.10915  ·  PDF (open access)
• PAM30 / 70 / 250 — Dayhoff M.O., Schwartz R.M., Orcutt B.C. (1978). A model of evolutionary change in proteins. In Atlas of Protein Sequence and Structure, vol. 5, supplement 3, pages 345–352. National Biomedical Research Foundation, Washington, D.C. (Book chapter; not available online as an open PDF. A widely cited follow-on derivation appears in Schwartz R.M., Dayhoff M.O. (1978), Matrices for detecting distant relationships, same volume, pages 353–358.)

Edit-distance scorers

Beyond Smith-Waterman and Needleman-Wunsch, stride-align exposes six unit-cost edit-distance and similarity metrics — each with its own SIMD-batched code path:

import stride_align

# Levenshtein (Myers 1999 bit-parallel) — inserts, deletes, substitutes
stride_align.levenshtein_score("kitten", "sitting")               # -> 3
stride_align.levenshtein_normalized_score("kitten", "sitting")    # -> 0.571...
stride_align.levenshtein_scores("kitten", ["kit", "sitting"])     # -> ndarray[int64]

# Optional `score_cutoff` (rapidfuzz convention): bail early per-target,
# results that exceed the cutoff come back as `cutoff + 1`.
stride_align.levenshtein_scores(query, targets, score_cutoff=3)

# Damerau-Levenshtein (OSA-restricted, Hyyrö 2002) — adds adjacent
# transposition at unit cost. This is what rapidfuzz exposes as
# OSA.distance and is what most callers asking for
# "Damerau-Levenshtein" actually want.
stride_align.damerau_levenshtein_score("ab", "ba")                # -> 1

# True Damerau-Levenshtein — the unrestricted form, where one
# character may participate in more than one edit. Slower (no
# bit-parallel kernel yet) but matches rapidfuzz.distance.DamerauLevenshtein
# exactly. Diverges from OSA on overlapping transpositions, e.g.
# "ca" -> "abc": OSA=3, true-DL=2.
stride_align.true_damerau_levenshtein_score("ca", "abc")          # -> 2

# Indel — Levenshtein restricted to insertions and deletions, no
# substitutions. Equivalent to |a| + |b| - 2 * LCS(a, b). Bit-
# parallel Allison-Dix (1986) inner loop.
stride_align.indel_score("kitten", "sitting")                     # -> 5

# Hamming — count of positions where two equal-length strings differ.
# Cutoff variant bails the byte loop once mismatches exceed the cap.
stride_align.hamming_score("100", "110")                          # -> 1

# Jaro / Jaro-Winkler — similarities in [0, 1]; Winkler adds a
# capped prefix bonus.
stride_align.jaro_similarity("martha", "marhta")                  # -> 0.944...
stride_align.jaro_winkler_similarity("martha", "marhta")          # -> 0.961...

The batch variants (*_scores, *_similarities) pack one target per SIMD lane on every supported backend:

• x86: SSE4.1 / AVX2 / AVX-512 / AVX10-256 / AVX10-512
• ARM: NEON (Linux + macOS), SVE / SVE2
• LoongArch: LSX / LASX
• PowerPC: VSX

For Lev / OSA, patterns up to 64 chars run a single-word Myers; 65–256 chars use the multi-word kernel (W=2/3/4). Indel and OSA fall back to scalar bit-parallel for patterns >64 (multi-word generalization deferred); true-DL is scalar DP only.

Phonetic encoders

For name matching, deduplication, and search-as-you-type, stride-align ships the full standard phonetic-encoder family. Each encoder maps a string to a short code such that names that sound similar share a code, regardless of spelling:

import stride_align as sa

# American Soundex (Russell & Odell, 1918). 4-character code.
sa.soundex("Robert")                                     # -> "R163"
sa.soundex("Rupert")                                     # -> "R163"
sa.soundex_equal("Robert", "Rupert")                     # -> True

# Metaphone (Lawrence Philips, 1990) — two-letter and longer
# spec-correct variants. The published 1990 spec and the popular
# jellyfish library disagree on a handful of edge cases; the variant
# kwarg picks the rule family.
sa.metaphone("Schmidt")                                  # -> "SKMTT"  (PHILIPS, spec)
sa.metaphone("Schmidt", variant=sa.MetaphoneVariant.JELLYFISH)  # -> "SXMTT"
sa.metaphone_equal("Schmidt", "Smith")                   # -> False

# Double Metaphone (Lawrence Philips, 2000) — primary and alternate
# codes; the alternate captures plausible non-English pronunciations.
# COMMONS is the faithful Apache Commons Codec port; PYTHON is bug-
# compat with the metaphone PyPI package.
sa.double_metaphone("Schwartz")                          # -> ("XRTS", "XFRTS")
sa.double_metaphone("Hugh")                              # -> ("H", "")
sa.double_metaphone("Hugh",
    variant=sa.DoubleMetaphoneVariant.PYTHON)            # -> ("HH", "")

# NYSIIS (Taft, 1970). More discriminative than Soundex for English
# names — "Watkins" / "Wilkins" / "Wilkinson" don't collide.
sa.nysiis("Watkins"), sa.nysiis("Wilkins")               # -> ("WATCAN", "WALCAN")

# Match Rating Approach (Moore, Western Airlines, 1977). A codex plus
# a pairwise comparator with length-difference + rating-threshold rules.
sa.match_rating_codex("Christopher")                     # -> "CHRPHR"
sa.match_rating_compare("Robert", "Rupert")              # -> True

# Caverphone 2.0 (Hood, 2004). Fixed-length 10-character code,
# right-padded with '1'. Designed for late-19th-century New Zealand
# electoral rolls but widely applied to general English-language
# name matching.
sa.caverphone("Stevenson")                               # -> "STFNSN1111"

All seven encoders are dispatched through the same byte-extraction helper, accept str and bytes inputs interchangeably, and skip non-letter / non-ASCII codepoints before encoding — pre-normalise with unicodedata.normalize("NFKD", s) if you want accent folding. Cross-checked against the canonical Apache Commons Codec reference data and the jellyfish, metaphone, and doublemetaphone PyPI packages.

Dynamic Time Warping

For aligning numeric sequences whose timing or speed varies — audio signals, gesture / sensor traces, financial time series — stride-align exposes Dynamic Time Warping with optional Sakoe-Chiba band:

import numpy as np
import stride_align as sa

q = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
t = np.array([1.0, 2.0, 2.5, 4.0, 5.0])

# Default distance follows the dtype:
#   float32 / float64 -> L2-squared, (x - y)^2
#   int16             -> L1, |x - y|  (audio convention)
sa.dtw(q, t)                                              # -> 0.25

# Sakoe-Chiba band: int radius or fraction of max(|q|, |t|).
sa.dtw(q, t, window=2)
sa.dtw(q, t, window=0.2)

# Explicit distance.
sa.dtw(q.astype(np.int16), t.astype(np.int16), distance="l1")

# Batch.
sa.dtw_distances(q, [t, t * 2, t + 0.5], window=2)

Inputs must be NumPy ndarray with matching dtype (float32, float64, or int16 — the natural audio dtype). Other dtypes and non-ndarray inputs raise TypeError.

cdist, cdist_above_threshold, cdist_top_k, cdist_top_k_per_query

For all-pairs scoring across two lists of strings, stride-align ships three matrix-style entry points:

qs = ["kitten", "sitting", "kit"]
ts = ["kitten", "kit", "sitting", "biting"]

# Full N×M similarity matrix — ndarray[float64] (similarity scorers)
# or ndarray[int64] (distance scorers).
sa.cdist(qs, ts, scorer=sa.Scorer.JARO)

# Streaming filter — yields only pairs whose similarity exceeds the
# threshold. Workers feed a bounded queue; the caller drains it.
# Length pruning + per-pair cutoff push-down into the kernel skip
# most of the work at high thresholds.
for score, q, t in sa.cdist_above_threshold(
    qs, ts, scorer=sa.Scorer.LEVENSHTEIN_NORMALIZED, threshold=0.7,
):
    ...

# Top-k by score — returns at most k highest-scoring (or lowest, for
# distance scorers) (score, query, target) tuples. Heaps are
# per-thread; a shared atomic global-min bound lets the per-pair
# cutoff push-down lift the prune threshold as work progresses.
sa.cdist_top_k(qs, ts, scorer=sa.Scorer.JARO, k=10)

# Top-k targets PER QUERY, yielded as a generator. Differs from
# cdist_top_k (which returns the k highest pairs globally) by keeping
# a separate top-k heap per query. With pruning=True, the worst-in-
# heap score adapts as scoring progresses and targets whose closed-
# form length-difference upper bound on similarity can't beat it
# are skipped before the kernel runs — a big win on workloads with
# wide length variation.
for query, top in sa.cdist_top_k_per_query(
    qs, ts, scorer=sa.Scorer.LEVENSHTEIN_NORMALIZED, k=5, pruning=True,
):
    # top is [(score, target), ...] sorted descending
    ...

At high thresholds the pruning is dramatic — see the cross-arch table in BENCHMARK.md (the cdist pruning rows). Loongson LASX in particular flips the expected ranking against Tiger Lake AVX-512 at T=0.99; the comparison report lives at docs/loongson-vs-tiger-lake-cdist-2026-05-24.md.

See BENCHMARK.md for full cross-architecture numbers.

Optimizations and Benchmarks

Careful attention has been, and continues to be, paid to stride-align's performance story. The library includes SIMD optimization for a variety of common targets, including x86, Arm, and LoongArch.

LoongArch / Loongson. The Loongson optimization story is especially telling: for the checked benchmark case -- English text, 16-bit score width, score-only Smith-Waterman -- the LASX backend is 16x faster than the generic backend and 22.4x faster than Parasail.

If you are a researcher using Loongson servers and benefiting from this speedup, citations, bug reports, benchmark cases, and tiny inexpensive Chinese souvenirs are appreciated. Tea, calligraphy bookmarks, paper-cut ornaments, Chinese knot charms, panda keychains, and small dragon desk objects are all welcome. Please do not send anything expensive or anything that requires customs paperwork.

See complete benchmarks.

Native Microbench

For perf profiling without Python frames or benchmark orchestration, configure a native x86 microbench build:

nanobind_dir="$(.venv/bin/python -m nanobind --cmake_dir)"
cmake -S . -B build/perf \
  -DCMAKE_BUILD_TYPE=RelWithDebInfo \
  -DSTRIDE_ALIGN_BUILD_MICROBENCH=ON \
  -DSTRIDE_ALIGN_PERF_SYMBOLS=ON \
  -DPython_EXECUTABLE=.venv/bin/python \
  -Dnanobind_DIR="$nanobind_dir"
cmake --build build/perf --target stride_align_x86_microbench
build/perf/stride_align_x86_microbench --backend avx2 --shape 1:many --pass english --width 16
python tools/x86_microbench_regression.py \
  --binary build/perf/stride_align_x86_microbench \
  --cpu 2 \
  --backends avx2,avx512bwvl \
  --shapes 1:1,1:many \
  --passes english,chinese \
  --widths 16,32 \
  --write-json /tmp/stride-align-x86-microbench.json
.venv/bin/python tools/pinned_benchmark_sweep.py \
  --output-dir /tmp/stride-align-pinned \
  --cpu 2 \
  --iterations 15 \
  --warmups 3

STRIDE_ALIGN_PERF_SYMBOLS=ON keeps nanobind modules unstripped and adds debug symbols plus frame pointers while preserving -O3.

The checked-in native microbench baseline lives at benchmarks/x86_microbench_baseline.json. Treat it as a local guardrail with a loose threshold, not as a cross-machine SLA.

Citations

If you use my software in your research, please cite me.

@software{deprince_stride_align,
  author       = {DePrince, Adam},
  title        = {stride-align: Fast Smith-Waterman and Needleman-Wunsch alignment for Python},
  year         = {2026},
  publisher    = {GitHub},
  url          = {https://github.com/adamdeprince/stride-align},
  note         = {Python/C++ library for sequence and string alignment}
}