The gputools package enables GPU computing in R.
basic_science · Level V
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- Record sourced from PubMed, PMID 19850754.
- Also identified by DOI 10.1093/bioinformatics/btp608 and PMC identifier 2796814.
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Abstract
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. R users can take advantage of the better performance provided by an Nvidia GPU. 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
Medical subject headings
- Algorithms
- Gene Expression Profiling
- Oligonucleotide Array Sequence Analysis
- Programming Languages
- Software