SimdMinimizers: Computing Random Minimizers, fast

Abstract

Motivation. Because of the rapidly-growing amount of sequencing data, computing sketches of large textual datasets has become an essential preprocessing task. These sketches are typically much smaller than the input sequences, but preserve sufficient information for downstream analysis. Minimizers are an especially popular sketching technique and used in a wide variety of applications. They sample at least one out of every w consecutive k-mers. As DNA sequencers are getting more accurate, some applications can afford to use a larger w and hence sparser and smaller sketches. And as sketches get smaller, their analysis becomes faster, so the time spent sketching the full-sized input becomes more of a bottleneck.
Methods. Our library simd-minimizers implements a random minimizer algorithm using SIMD instructions. It supports both AVX2 and NEON architectures. Its main novelty is two-fold. First, it splits the input into 8 chunks that are streamed over in parallel through all steps of the algorithm. This is enabled by using the completely deterministic two-stacks sliding window minimum algorithm, which seems not to have been used before for finding minimizers.
Results. Our library is up to 6.8× faster than a scalar implementation of the rescan method when w = 5 is small, and 3.4× faster for larger w = 19. Computing canonical minimizers is less than 50% slower than computing forward minimizers, and over 15× faster than the existing implementation in the minimizer-iter crate. Our library finds all (canonical) minimizers of a 3.2 Gbp human genome in 5.2 (resp. 6.7) seconds.