Algorithmic Improvement and GPU Acceleration of the GenASM Algorithm

TL;DR

This paper presents algorithmic enhancements and GPU acceleration for GenASM, significantly reducing memory usage and increasing speed, thereby improving genomic sequence alignment performance on GPUs.

Contribution

The paper introduces novel algorithmic optimizations and GPU parallelization techniques that substantially improve memory efficiency and computational speed for GenASM.

Findings

24× reduction in memory footprint

4.1× speedup over CPU implementation

62× speedup over minimap2's CPU-based KSW2

Abstract

We improve on GenASM, a recent algorithm for genomic sequence alignment, by significantly reducing its memory footprint and bandwidth requirement. Our algorithmic improvements reduce the memory footprint by 24$\times$ and the number of memory accesses by 12$\times$. We efficiently parallelize the algorithm for GPUs, achieving a 4.1$\times$ speedup over a CPU implementation of the same algorithm, a 62$\times$ speedup over minimap2's CPU-based KSW2 and a 7.2$\times$ speedup over the CPU-based Edlib for long reads.