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

Title:
Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS.
Authors:
Davis NA; Department of Mathematical and Computer Sciences, University of Tulsa, Tulsa, OK 74104, USA., Pandey A, McKinney BA
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2011 Jan 15; Vol. 27 (2), pp. 284-5. Date of Electronic Publication: 2010 Nov 25.
Publication Type:
Evaluation Study; Journal Article; Research Support, N.I.H., Extramural
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Genome-Wide Association Study* , Polymorphism, Single Nucleotide* , Software*, Algorithms ; Computational Biology ; Computer Graphics ; Computers ; Programming Languages
References:
BMC Res Notes. 2009 Jul 24;2:149. (PMID: 19630950)
PLoS Genet. 2009 Mar;5(3):e1000432. (PMID: 19300503)
Genes Immun. 2010 Dec;11(8):630-6. (PMID: 20613780)
Nature. 2007 Jun 7;447(7145):661-78. (PMID: 17554300)
Am J Hum Genet. 2007 Sep;81(3):559-75. (PMID: 17701901)
Grant Information:
K25 AI064625 United States AI NIAID NIH HHS; R56 AI080932 United States AI NIAID NIH HHS; K25 AI-64625 United States AI NIAID NIH HHS; R56 AI-80932 United States AI NIAID NIH HHS
Entry Date(s):
Date Created: 20101201 Date Completed: 20110406 Latest Revision: 20211020
Update Code:
20250114
PubMed Central ID:
PMC3018810
DOI:
10.1093/bioinformatics/btq638
PMID:
21115438
Database:
MEDLINE

Weitere Informationen

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.