Will solid-state drives accelerate your bioinformatics? In-depth profiling, performance analysis, and beyond

TL;DR

This paper investigates how solid-state drives (SSDs) can accelerate bioinformatics workflows by profiling 23 key programs, analyzing performance gains, and discussing optimization strategies for integrating SSDs with parallel computing.

Contribution

It provides an in-depth profiling and analysis of bioinformatics programs on SSDs, offering insights and recommendations for optimizing bioinformatics pipelines with new storage technologies.

Findings

SSDs can significantly speed up bioinformatics programs.

Parallelization combined with SSDs enhances performance.

Profiling reveals specific bottlenecks and optimization opportunities.

Abstract

A wide variety of large-scale data has been produced in bioinformatics. In response, the need for efficient handling of biomedical big data has been partly met by parallel computing. However, the time demand of many bioinformatics programs still remains high for large-scale practical uses due to factors that hinder acceleration by parallelization. Recently, new generations of storage devices have emerged, such as NAND flash-based solid-state drives (SSDs), and with the renewed interest in near-data processing, they are increasingly becoming acceleration methods that can accompany parallel processing. In certain cases, a simple drop-in replacement of hard disk drives (HDDs) by SSDs results in dramatic speedup. Despite the various advantages and continuous cost reduction of SSDs, there has been little review of SSD-based profiling and performance exploration of important but time-consuming bioinformatics programs. For an informative review, we perform in-depth profiling and analysis of 23 key bioinformatics programs using multiple types of devices. Based on the insight we obtain from this research, we further discuss issues related to design and optimize bioinformatics algorithms and pipelines to fully exploit SSDs. The programs we profile cover traditional and emerging areas of importance, such as alignment, assembly, mapping, expression analysis, variant calling, and metagenomics. We explain how acceleration by parallelization can be combined with SSDs for improved performance and also how using SSDs can expedite important bioinformatics pipelines, such as variant calling by the Genome Analysis Toolkit (GATK) and transcriptome analysis using RNA sequencing (RNA-seq). We hope that this review can provide useful directions and tips to accompany future bioinformatics algorithm design procedures that properly consider new generations of powerful storage devices.