Name
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 |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1093/bioinformatics/btp608 | Bioinformatics |
Keywords | DocType | Volume |
gpu computing,microarray gene expression analysis,rgpgpu contact,cuda toolkit,large analysis task,gputools r package,gputools package,nvidia gpu,r statistical environment,r user,gpu-equipped computer,r project,gene expression profiling,programming languages,algorithms | Journal | 26 |
Issue | ISSN | Citations |
1 | 1367-4811 | 12 |
PageRank | References | Authors |
1.00 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joshua Buckner | 1 | 12 | 1.00 |
| Justin Wilson | 2 | 111 | 4.80 |
| Mark Seligman | 3 | 12 | 1.00 |
| Brian Athey | 4 | 82 | 5.64 |
| Stanley J. Watson | 5 | 41 | 2.40 |
| Fan Meng | 6 | 114 | 10.82 |