Accelerating K-mer Frequency Counting with GPU and Non-Volatile Memory

TL;DR

This paper enhances k-mer frequency counting efficiency by redesigning a bioinformatics method to leverage GPU and non-volatile memory, significantly reducing CPU time and energy consumption compared to traditional multi-node setups.

Contribution

It introduces novel techniques to adapt and scale a reference-free variant calling method using GPU and NVM, enabling single-machine performance comparable to multi-node clusters.

Findings

CPU time reduced by 7.5x compared to 16-node setup

Energy consumption decreased by 5.5x

Single machine achieves similar performance to multi-node system

Abstract

The emergence of Next Generation Sequencing (NGS) platforms has increased the throughput of genomic sequencing and in turn the amount of data that needs to be processed, requiring highly efficient computation for its analysis. In this context, modern architectures including accelerators and non-volatile memory are essential to enable the mass exploitation of these bioinformatics workloads. This paper presents a redesign of the main component of a state-of-the-art reference-free method for variant calling, SMUFIN, which has been adapted to make the most of GPUs and NVM devices. SMUFIN relies on counting the frequency of \textit{k-mers} (substrings of length $k$) in DNA sequences, which also constitutes a well-known problem for many bioinformatics workloads, such as genome assembly. We propose techniques to improve the efficiency of k-mer counting and to scale-up workloads like \sm that used to require 16 nodes of \mn to a single machine with a GPU and NVM drives. Results show that although the single machine is not able to improve the time to solution of 16 nodes, its CPU time is 7.5x shorter than the aggregate CPU time of the 16 nodes, with a reduction in energy consumption of 5.5x.