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马尔科夫链蒙特卡洛算法并行化及其应用 被引量:2

Parallelization and application of Markov chain Monte Carlo
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摘要 为使高性能计算助力群体遗传学和系统地理学研究,提出一种基于MPI(message passing interface)的群体遗传学分析软件,利用集群中多个CPU核心的计算能力加速群体遗传学分析。进行正确性验证,对并行加速比和并行效率进行评估,在保证计算结果正确性前提下,利用256个CPU核心时可以得到最好的并行加速比(185.16),在利用128个CPU核心时可以得到最好的并行效率(93.68%)。实验结果表明,利用高性能计算能够进行快速有效的群体遗传学分析。 To accelerate the research of population genetics and phylogeography based on high performance computing (HPC),a parallel implementation of the IM program using the message passing interface (MPI)on multiple CPU cores was proposed.The validity,parallel speed and parallel efficiency were evaluated.Best parallel speedup (185.16)when using 256 CPU cores and best parallel efficiency (93.68%)when using 128 CPU cores were obtained.The results demonstrate that the divergence popula-tion genetics analysis can be done effectively and rapidly based on HPC.
出处 《计算机工程与设计》 北大核心 2016年第7期1811-1816,1826,共7页 Computer Engineering and Design
基金 国家自然科学基金项目(61303050) 国家863高技术研究发展计划基金项目(2015AA01A303) 中国科学院青年创新促进会基金项目(2015375) 中国科学院信息化专项基金项目(XXH2503-02)
关键词 群体遗传学 系统地理学 IM模型 高性能计算 消息传递接口 population genetics phylogeography isolation with migration model high performance computing message passing interface
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