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Bioinformatics Advance Access published online on October 22, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp608
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

The gputools package enables GPU computing in R

Joshua Buckner 1,*, Justin Wilson 1, Mark Seligman 3, Brian Athey 2, Stanley Watson 1 and Fan Meng 1,2

1 Molecular & Behavioral Neuroscience Institute and Psychiatry Department
2 National Center for Integrative Biomedical Informatics University of Michigan, Ann Arbor, MI 48109, US
3 Rapid Biologics, P.O. Box 31989, Seattle, WA 98103, info{at}rapidbiologics.com

*To whom correspondence should be addressed. Dr. Joshua Buckner, E-mail: bucknerj{at}umich.edu


  Abstract

Motivation: By default, the R statistical environment does not make use of parallelism. Researchers may resort to expensive solutions such as cluster hardware for large analysis tasks. Graphics processing units (GPUs) provide an inexpensive and computationally powerful alternative. Using R and the CUDA toolkit from Nvidia, we have implemented several functions commonly used in microarray gene expression analysis for GPU equipped computers.

Results: R users can take advantage of the better performance provided by an Nvidia GPU.

Availability: The package is available from CRAN, the R project's repository of packages, at http://cran.r-project.org/web/packages/gputools

More information about our gputools R package is available at http://brainarray.mbni.med.umich.edu/brainarray/Rgpgpu

Contact: bucknerj{at}umich.edu

Associate Editor: Prof. John Quackenbush

Received on July 8, 2009; revised on October 7, 2009; accepted on October 17, 2009

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