Paper Info
Title
Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS.
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 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.
Year
DOI
Venue
2011
10.1093/bioinformatics/btq638
BIOINFORMATICS
Keywords
Field
DocType
genome wide association study,programming languages,algorithms,interaction network,computer graphics,network analysis,programming language,nucleotides,computational biology
MATLAB,Computer science,CUDA,Parallel computing,Implementation,Web application,Bioinformatics,Graphics processing unit,Java,Computer graphics,Python (programming language)
Journal
Volume
Issue
ISSN
27
2
1367-4803
Citations 
PageRank 
References 
1
0.61
1
Authors
3
Name
Order
Citations
PageRank
Nicholas A. Davis110.61
Ahwan Pandey210.61
Brett A. McKinney3747.36