SimdSketch

A SIMD-accelerated library to compute two types of sketches:

• Classic bottom $s$ sketch, containing the $s$ smallest distinct k-mer hashes.
• Bucket sketch, which partitions the hashes into $s$ parts and returns the smallest hash in each part. (Introduced as one permutation hashing in Li, Owen, Zhang 2012.)

See the corresponding blog post for background and evaluation.

Sketching takes 2 seconds for a 3Gbp human genome. This library returns 32-bit u32 hashes. This means that currently it may not be very suitable for sequences that are too close to 1Gbp in length, since the bottom hash values will be relatively dense.

Algorithm. For the bottom $s$ sketch, we first collect all ``sufficiently small'' hashes into a vector. Then, that vector is sorted and deduplicated, and the smallest $s$ values are returned. This ensures that the runtime is $O(n + s \log s)$ when the number of duplicate k-mers is limited.

For the bucket sketch, the classic method is to partition hashes linearly, e.g., for $s=2$ into the bottom and top half. Then, a single value is kept per part, and each hash is compared against the rolling minimum of its bucket.

Instead, here we make buckets by the remainder modulo $s$. This way, we can again pre-filter for ``sufficiently small'' values, and then only scan those for the minimum.

In both variants, we double the ``smallness'' threshold until either $s$ distinct values are found or all $s$ buckets have a value in them.

Formulas For the bottom sketch, the Jaccard similarity j is computed as follows:

1. Find the smallest s distinct k-mer hashes in the union of two sketches.
2. Return the fraction of these k-mers that occurs in both sketches.

For the bucket sketch, we first identify all buckets that are not left empty by both sketches. Then, we take the fraction j0 of the remaining buckets where they are equal. We use b-bit sketches, where only the bottom b bits of each bucket-minimum are stored. This gives a 1/2^b probability of hash collisions. To fix this, we compute j = (j0 - 1/2^b) / (1 - 1/2^b) as the similarity corrected for these collisions.

The Mash distance as reported by the CLI is computed as -ln(2*j / (1+j))/k. This is always >=0, but can be as large as inf for disjoint sets, that have j=0.

Implementation notes. Good performance is mostly achieved by using a branch-free implementation: all hashes are computed using 8 parallel streams using SIMD, and appended to a vector when they are sufficiently small to likely be part of the sketch.

The underlying streaming and hashing algorithms are described in the following preprint:

• SimdMinimizers: Computing random minimizers, fast. Ragnar Groot Koerkamp, Igor Martayan bioRxiv 2025.01.27 doi.org/10.1101/2025.01.27.634998

Usage

Please see lib.rs and docs.rs for full docs.

use packed_seq::SeqVec;

// Bottom h=10000 sketch of k=31-mers.
let k = 31;
let h = 10_000;

// Use `new_rc` for a canonical version instead.
let sketch = simd_sketch::Sketcher::new(k, h);

// Generate two random sequences of 2M characters.
let n = 2_000_000;
let seq1 = packed_seq::PackedSeqVec::random(n);
let seq2 = packed_seq::PackedSeqVec::random(n);

let sketch1: simd_sketch::BottomSketch = sketcher.bottom_sketch(seq1.as_slice());
let sketch2: simd_sketch::BottomSketch = sketcher.bottom_sketch(seq2.as_slice());

// Value between 0 and 1, estimating the fraction of shared k-mers.
let similarity = sketch1.similarity(&sketch2);

// Bucket sketch variant

let sketch1: simd_sketch::BinSketch = sketcher.sketch(seq1.as_slice());
let sketch2: simd_sketch::BinSketch = sketcher.sketch(seq2.as_slice());

// Value between 0 and 1, estimating the fraction of shared k-mers.
let similarity = sketch1.similarity(&sketch2);

Command line tool

The crate comes with a simple command line tool for computing all-to-all Mash distances matrices:

> simd-sketch triangle --help
Takes paths to fasta files, and outputs a Phylip distance matrix to stdout

Usage: simd-sketch triangle [OPTIONS] [PATHS]...

Arguments:
  [PATHS]...  Paths to (directories of) (gzipped) fasta files

Options:
      --alg <ALG>        Sketch algorithm to use. Defaults to bucket because of its much faster comparisons [default: bucket] [possible values: bottom, bucket]
      --fwd              When set, use forward instead of canonical k-mer hashes
  -k <K>                 k-mer size [default: 31]
  -s <S>                 Bottom-s sketch, or number of buckets [default: 10000]
  -b <B>                 For bucket-sketch, store only the lower b bits [default: 8]
      --output <OUTPUT>  Write phylip distance matrix here, or default to stdout
  -h, --help             Print help

Minimal example usage, printing the matrix to stdout:

simd-sketch triangle inputs/*.fa

Typical example usage, for specific k and using reverse-complement-aware hashes:

simd-sketch triangle --rc -k 21 inputs/*.fa --output matrix.phylip

Maximal usage with default parameters:

simd-sketch triangle --alg bucket -k 31 -s 10000 -b 8 inputs/*.fna.gz --output matrix.phylip