The gputools package enables GPU computing in R

被引：26

作者：

Buckner, Joshua ^{[1
,2
]}

Wilson, Justin ^{[1
,2
]}

Seligman, Mark ^{[4
]}

Athey, Brian ^{[3
]}

Watson, Stanley ^{[1
,2
]}

Meng, Fan ^{[1
,2
,3
]}

机构：

[1] Univ Michigan, Dept Psychiat, Ann Arbor, MI 48109 USA

[2] Univ Michigan, Mol & Behav Neurosci Inst, Ann Arbor, MI 48109 USA

[3] Univ Michigan, Natl Ctr Integrat Biomed Informat, Ann Arbor, MI 48109 USA

[4] Rapid Biol, Seattle, WA 98103 USA

来源：

BIOINFORMATICS | 2010年 / 26卷 / 01期

基金：

美国国家卫生研究院;

关键词：

D O I：

10.1093/bioinformatics/btp608

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：134 / 135

页数：2

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