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permGPU: Using graphics processing units in RNA microarray association studies

Ivo Shterev, Sin H. Jung, Stephen George, Kouros Owzar
Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Road, Durham, NC 27705, USA
BMC Bioinformatics, Vol. 11, No. 1. (2010), 329.

@article{shterev2010permgpu,

   title={permGPU: Using graphics processing units in RNA microarray association studies},

   author={Shterev, I.D. and Jung, S.H. and George, S.L. and Owzar, K.},

   journal={BMC bioinformatics},

   volume={11},

   number={1},

   pages={329},

   issn={1471-2105},

   year={2010},

   publisher={BioMed Central Ltd}

}

BACKGROUND:Many analyses of microarray association studies involve permutation and 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 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.
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