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A Three-Level Parallel Algorithm For MrBayes 3.2

机译:MRBAYES 3.2的三级并行算法

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摘要

MrBayes is a popular bioinformatics software that is widely used in phylogenetic analysis. The core algorithm of Mrbayes is Metropolis Coupled Markov Chain Monte Carlo (MC3). However, when dealing with large data sets, MC~3 algorithm is too slow to meet researcher's requirements. Although several parallelizations have been proposed for MrBayes, such as MPI (Message Passing Interface) based MrBayes, GPU (Graphics Processing Unit) based MrBayes, there is still no efficient parallel algorithm to fully utilize computing power of modern CPU and computer architecture. This paper (a) presents a new three-level hybrid parallel algorithm, include data-level parallelism (DLP), thread-level parallelism (TLP), and process-level parallelism (PLP), which can be used on most modern multi-core computers; (b) compares the performance of different combinations of parallel strategies on real-world protein data sets. The experimental results show that, this hybrid parallel algorithm does convert more computing powers into higher speedup. Furthermore, the proposed algorithm's speedup is near the speedup on one GPU at the same data sets. This algorithm is fit for practical use in phylogenetic inferences.
机译:MRBAYES是一种流行的生物信息学软件,广泛用于系统发育分析。 MRBAYES的核心算法是大都会耦合马尔可夫链蒙特卡罗(MC3)。但是,在处理大数据集时,MC〜3算法太慢,以满足研究人员的要求。虽然已经为MRBAYES提出了几种并行化,例如基于MPI(消息传递接口)的MRBAYES,GPU(图形处理单元)的MRBAATES,但仍然没有有效的并行算法可以充分利用现代CPU和计算机架构的计算能力。本文(a)呈现了一种新的三级混合并行算法,包括数据级并行性(DLP),线程平行度(TLP)和过程级并行性(PLP),其可以在大多数现代的多型中使用核心计算机; (b)比较了不同组合对现实蛋白质数据集的不同组合的性能。实验结果表明,这种混合并行算法确实将更多计算能力转换为更高的加速。此外,所提出的算法的加速在同一数据集的一个GPU上附近。该算法适用于系统发育推断的实际应用。

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