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The gputools package enables GPU computing in R

Joshua Buckner, Justin Wilson, Mark Seligman, Brian Athey, Stanley Watson, Fan Meng
Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109 and 3 Rapid Biologics, P.O. Box 31989, Seattle, WA 98103, USA
Bioinformatics, Vol. 26, No. 1. (1 January 2010), pp. 134-135.

@article{buckner2010gputools,

   title={The gputools package enables GPU computing in R},

   author={Buckner, J. and Wilson, J. and Seligman, M. and Athey, B. and Watson, S. and Meng, F.},

   journal={Bioinformatics},

   volume={26},

   number={1},

   pages={134},

   year={2010},

   publisher={Oxford Univ Press}

}

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@umich.edu 10.1093/bioinformatics/btp608
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