Scaling read aligners to hundreds of threads on general-purpose processors

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ID: 306838
2018
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Abstract
Abstract Motivation General-purpose processors can now contain many dozens of processor cores and support hundreds of simultaneous threads of execution. To make best use of these threads, genomics software must contend with new and subtle computer architecture issues. We discuss some of these and propose methods for improving thread scaling in tools that analyze each read independently, such as read aligners. Results We implement these methods in new versions of Bowtie, Bowtie 2 and HISAT. We greatly improve thread scaling in many scenarios, including on the recent Intel Xeon Phi architecture. We also highlight how bottlenecks are exacerbated by variable-record-length file formats like FASTQ and suggest changes that enable superior scaling. Availability and implementation Experiments for this study: https://github.com/BenLangmead/bowtie-scaling. Bowtie http://bowtie-bio.sourceforge.net . Bowtie 2 http://bowtie-bio.sourceforge.net/bowtie2 . HISAT http://www.ccb.jhu.edu/software/hisat Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W2952597168 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ben Langmead, Christopher Wilks, Valentin Antonescu, Rone Charles
Journal BMC Bioinformatics
Year 2018
DOI
10.1093/bioinformatics/bty648
URL
https://academic.oup.com/bioinformatics/article-pdf/35/3/421/27700323/bty648.pdf
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