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Accelerating Genome-Wide Association Studies Using CUDA Compatible Graphics Processing Units

Rui Jiang, Feng Zeng, Wangshu Zhang, Xuebing Wu, Zhihong Yu
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

@inproceedings{jiang2009accelerating,

   title={Accelerating genome-wide association studies using CUDA compatible graphics processing units},

   author={Jiang, R. and Zeng, F. and Zhang, W. and Wu, X. and Yu, Z.},

   booktitle={Bioinformatics, Systems Biology and Intelligent Computing, 2009. IJCBS’09. International Joint Conference on},

   pages={70–76},

   year={2009},

   organization={IEEE}

}

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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.
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