Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS

Nicholas A. Davis, Ahwan Pandey, B. A. McKinney
Department of Mathematical and Computer Sciences, University of Tulsa, Tulsa, OK, USA


   title={Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS},

   author={Davis, N.A. and Pandey, A. and McKinney, BA},




   publisher={Oxford Univ Press}


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MOTIVATION: Bioinformatics researchers have a variety of programming languages and architectures at their disposal, and recent advances in graphics processing unit (GPU) computing have added a promising new option. However, many performance comparisons inflate the actual advantages of GPU technology. In this study, we carry out a realistic performance evaluation of SNPrank, a network centrality algorithm that ranks single nucleotide polymorhisms (SNPs) based on their importance in the context of a phenotype-specific interaction network. Our goal is to identify the best computational engine for the SNPrank web application and to provide a variety of well-tested implementations of SNPrank for Bioinformaticists to integrate into their research. RESULTS: Using SNP data from the Wellcome Trust Case Control Consortium genome-wide association study of Bipolar Disorder, we compare multiple SNPrank implementations, including Python, Matlab, and Java as well as CPU vs GPU implementations. When compared with naive, single-threaded CPU implementations, the GPU yields a large improvement in the execution time. However, with comparable effort, multi-threaded CPU implementations negate the apparent advantage of GPU implementations.
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