The gputools package enables GPU computing in R.
BACKGROUND
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.


BACKGROUND
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
Buckner
Joshua
J
Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA. bucknerj@umich.edu
Wilson
Justin
J
Seligman
Mark
M
Athey
Brian
B
Watson
Stanley
S
Meng
Fan
F
D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011381:Programming Languages; D012984:Software