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

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2011-01-15 Epub Date: 2010-11-25 DOI:10.1093/bioinformatics/btq638

Nicholas A Davis, Ahwan Pandey, B A McKinney

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引用次数: 20

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.

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GWAS的网络分析工具sn恶作剧的CPU和GPU实现的真实世界比较。

动机:生物信息学研究人员有各种各样的编程语言和体系结构可供他们使用，并且图形处理单元(GPU)计算的最新进展增加了一个有前途的新选择。然而，许多性能比较夸大了GPU技术的实际优势。在这项研究中，我们对SNPrank进行了实际的性能评估，SNPrank是一种网络中心性算法，根据单核苷酸多态性(snp)在表型特异性相互作用网络中的重要性对其进行排名。我们的目标是为sn恶作剧网络应用程序确定最好的计算引擎，并为生物信息学家提供各种经过良好测试的sn恶作剧实现，以便将其集成到他们的研究中。结果:使用来自Wellcome Trust病例控制联盟双相情感障碍全基因组关联研究的SNP数据，我们比较了多种SNPrank实现，包括Python, Matlab和Java，以及CPU与GPU实现。与naïve单线程CPU实现相比，GPU在执行时间上有很大的改进。然而，通过相当的努力，多线程CPU实现否定了GPU实现的明显优势。可用性:sn恶作剧代码是开源的，可以在http://insilico.utulsa.edu/snprank上获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.