gPGA：GPU加速群体遗传学分析
gPGA: GPU Accelerated Population Genetics Analyses.
作者信息
Zhou Chunbao, Lang Xianyu, Wang Yangang, Zhu Chaodong
机构信息
Supercomputing Center, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China.
Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
出版信息
PLoS One. 2015 Aug 6;10(8):e0135028. doi: 10.1371/journal.pone.0135028. eCollection 2015.
BACKGROUND
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.
METHODOLOGY
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.
CONCLUSIONS
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.
背景
隔离迁移（IM）模型在群体遗传学和系统地理学研究中具有重要意义。IM程序基于基因谱系的马尔可夫链蒙特卡罗（MCMC）模拟，将IM模型应用于从一对密切相关的群体或物种中获取的遗传数据。然而，IM程序的计算负担限制了其应用。
方法
凭借强大的计算能力，图形处理单元（GPU）已在许多领域得到广泛应用。在本文中，我们基于统一计算设备架构（CUDA），在一个GPU上实现了IM程序的有效版本，我们将其称为gPGA。
结论
与IM程序相比，gPGA在一个GPU上可实现高达52.30倍的加速。评估结果表明，它能够有效地、快速地分析数据集，用于分歧群体遗传学研究。该软件可在https://github.com/chunbaozhou/gPGA上免费获取源代码。
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