gPGA: GPU Accelerated Population Genetics Analyses

• Published in: PloS one Vol. 10; no. 8; p. e0135028
• Main Authors: Zhou, Chunbao, Lang, Xianyu, Wang, Yangang, Zhu, Chaodong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.08.2015; Public Library of Science (PLoS)
• Subjects: Analysis; Computation; Computer applications; Computer Graphics; Computer simulation; Data processing; Divergence; Genetics; Genetics, Population - statistics & numerical data; Graphics processing units; Humans; Markov analysis; Markov Chains; Markov processes; Migration; Monte Carlo Method; Monte Carlo methods; Monte Carlo simulation; Mutation; Phylogenetics; Population genetics; Population studies; Software; United States; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0135028

The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application. With strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA. Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA.