permGPU: Using graphics processing units in RNA microarray association studies

Background: Many analyses of microarray association studies involve permutation, bootstrap resampling and crossvalidation, 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 incre...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Ivo D. Shterev, Sin-Ho Jung, Stephen L. George, Kouros Owzar
• Keywords: BMCBI 2010, Microarray Association Studies, Resampling, Test Statistics