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Bioinformatics. 2011 Jan 15;27(2):284-5. doi: 10.1093/bioinformatics/btq638. Epub 2010 Nov 25.

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

Author information

  • 1Department of Mathematical and Computer Sciences, University of Tulsa, Tulsa, OK 74104, USA.

Abstract

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 versus GPU implementations. When compared with naïve, 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.

AVAILABILITY:

The SNPrank code is open source and available at http://insilico.utulsa.edu/snprank.

PMID:
21115438
[PubMed - indexed for MEDLINE]
PMCID:
PMC3018810
Free PMC Article

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