Accelerating Genome-Wide Association Studies Using CUDA Compatible Graphics Processing Units
MOE Key Laboratory of Bioinformatics, Bioinformatics Division, TNLIST/Dept. of Automation, Tsinghua University, Beijing, China
International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 2009. IJCBS ’09
Recent advances in highly parallel, multithreaded, manycore Graphics Processing Units (GPUs) have been enabling massive parallel implementations of many applications in bioinformatics. In this paper, we describe a parallel implementation of genome-wide association studies (GWAS) using Compute Unified Device Architecture (CUDA). Using a single NVIDIA GTX 280 graphics card, we achieve speedups of about 15 times over Intel Xeon E5420. We also implement a highly scalable, massive parallel, GWAS system using the message passing interface (MPI) and show that a single GTX 280 can have similar performance as a 16-node cluster. We further apply the GPU program to two real genome-wide case-control data sets. The results show that the GPU program is 17.7 times as fast as the CPU version for an age-related macular degeneration (AMD) data set and 25.7 times as fast as the CPU version for a Parkinsonpsilas disease data set.
June 15, 2011 by hgpu
