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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, MC3 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,已广泛用于系统发育分析。 Mrbayes的核心算法是都会耦合马尔可夫链蒙特卡洛(MC3)。但是,在处理大型数据集时,MC3算法太慢,无法满足研究人员的要求。尽管已针对MrBayes提出了几种并行化方法,例如基于MPI(消息传递接口)的MrBayes,基于GPU(图形处理单元)的MrBayes,但仍然没有有效的并行算法来充分利用现代CPU和计算机体系结构的计算能力。本文(a)提出了一种新的三级混合并行算法,其中包括数据级并行(DLP),线程级并行(TLP)和进程级并行(PLP),它们可用于大多数现代的多级并行算法。核心计算机; (b)比较了实际蛋白质数据集上并行策略不同组合的性能。实验结果表明,这种混合并行算法的确将更多的计算能力转化为更高的速度。此外,在相同数据集下,所提算法的提速接近一个GPU的提速。该算法适合系统发育推论的实际应用。

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