ArticleBioinformatics (Oxford, England)2019
Scaling read aligners to hundreds of threads on general-purpose processors.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 573 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
573 citing papers in PubMed.
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- Comparative analysis of precursor supply and pathway reinforcement strategies for shikimic acid biosynthesis in Pseudomonas putida.Microbial cell factories · 2026Article
513 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.