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Open Access Highly Accessed Software

permGPU: Using graphics processing units in RNA microarray association studies

Ivo D Shterev1, Sin-Ho Jung12, Stephen L George12 and Kouros Owzar12*

Author Affiliations

1 Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Road, Durham, NC 27705, USA

2 CALGB Statistical Center, Duke University, 2424 Erwin Road, Durham, NC 27705, USA

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BMC Bioinformatics 2010, 11:329  doi:10.1186/1471-2105-11-329

Published: 16 June 2010

Abstract

Background

Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed.

Results

We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server.

Conclusions

permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.