title: permGPU: Using graphics processing units in RNA microarray association studies.
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.
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA.
authors.forename: Ivo D
authors.initials: ID
authors.lastname: Shterev
authors.forename: Sin-Ho
authors.initials: SH
authors.lastname: Jung
authors.forename: Stephen L
authors.initials: SL
authors.lastname: George
authors.forename: Kouros
authors.initials: K
authors.lastname: Owzar
mesh_terms: D020869:Gene Expression Profiling; D056726:Genetic Association Studies; D006801:Humans; D046228:Microarray Analysis; D009369:Neoplasms / Q000235:genetics*; D012313:RNA; D012984:Software
chemical_list: D012313:RNA