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

  • Authors:

  • Rui Jiang;Feng Zeng;Wangshu Zhang;Xuebing Wu;Zhihong Yu

  • Affiliations:

  • -;-;-;-;-

  • Venue:
  • IJCBS '09 Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
  • Year:
  • 2009

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Abstract

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 Parkinson’s disease data set.