Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.
Bioinformatics. 2010 Jan 1;26(1):134-5. doi: 10.1093/bioinformatics/btp608. Epub 2009 Oct 22.
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
默认情况下，R 统计环境不利用并行性。研究人员可能会求助于昂贵的解决方案，如集群硬件，用于大型分析任务。图形处理单元（GPU）提供了一种廉价且计算能力强大的替代方案。我们使用 R 和 Nvidia 的 CUDA 工具包，为配备 GPU 的计算机实现了几种在微阵列基因表达分析中常用的功能。
R 用户可以利用 Nvidia GPU 提供的更好的性能。
该软件包可从 CRAN（R 项目的软件包存储库）获得，网址为 http://cran.r-project.org/web/packages/gputools。更多关于我们的 gputools R 包的信息可在 http://brainarray.mbni.med.umich.edu/brainarray/Rgpgpu 上获得。
