Scaling read aligners to hundreds of threads on general-purpose processors
By Ben Langmead, C. Wilks, Valentin Antonescu, Rone Charles. bioRxiv, 2017
797 citations recorded by the source corpus.
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. 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.
Subfields: Hardware and Architecture, Information Systems and Management, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/3e8eb090aa01772bd2aaba70b1e34484f717ef21
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In Hardware and Architecture
In Information Systems and Management
In Molecular Biology
- Jennifer Lu
- Ben Langmead
- Derrick E. Wood
- Steven L. Salzberg
- Florian P. Breitwieser
- Heng Li
- Daehwan Kim
- Richa Bharti
Citations in the corpus
- Improved metagenomic analysis with Kraken 2 (2019, methodology): “Previous work by Langmead et al. [17] has shown the importance of removing parsing work from critical sections, i.e., portions of the program that can be executed by only 1 thread at a time.”