ASTRAL-MP: scaling ASTRAL to very large datasets using randomization and parallelization
ASTRAL-MP: scaling ASTRAL to very large datasets using randomization and parallelization
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DOI：
10.1093/bioinformatics/btz211
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发表时间：
2019-10-15
期刊：
影响因子：
5.8
通讯作者：
Mirarab, Siavash
中科院分区：
文献类型：
--
作者：
Yin, John;Zhang, Chao;Mirarab, Siavash
动机：进化历史可以从基因组的一部分改变到另一部分。潜在的基因树之间的不一致性，激发了总结方法的发展，重建一个物种树从一个输入的基因树集合。ASTRAL是一种广泛使用的汇总方法，能够扩展到相对较大的数据集。然而，基因组数据集的规模正在迅速增长。尽管它的相对效率，目前的单线程实现ASTRAL落后于数据增长的趋势是无法分析最大的可用datasets在合理的time.Results：ASTRAL使用动态编程，是不是平凡的并行。在本文中，我们介绍ASTRAL-MP，ASTRAL的第一个版本，可以利用并行性，也使用随机化技术来加快其一些步骤。重要的是，ASTRAL-MP不仅可以利用多个CPU核心，还可以利用一个或多个图形处理单元（GPU）。ASTRAL-MP代码随着CPU内核的增加而扩展得非常好，其GPU版本在OpenCL中实现，与ASTRAL-III相比，可以有高达158倍的加速。使用GPU和多个核心，ASTRAL-MP能够分析10000个物种的数据集或超过100000个基因的数据集，
Motivation: Evolutionary histories can change from one part of the genome to another. The potential for discordance between the gene trees has motivated the development of summary methods that reconstruct a species tree from an input collection of gene trees. ASTRAL is a widely used summary method and has been able to scale to relatively large datasets. However, the size of genomic datasets is quickly growing. Despite its relative efficiency, the current single-threaded implementation of ASTRAL is falling behind the data growth trends is not able to analyze the largest available datasets in a reasonable time.Results: ASTRAL uses dynamic programing and is not trivially parallel. In this paper, we introduce ASTRAL-MP, the first version of ASTRAL that can exploit parallelism and also uses randomization techniques to speed up some of its steps. Importantly, ASTRAL-MP can take advantage of not just multiple CPU cores but also one or several graphics processing units (GPUs). The ASTRAL-MP code scales very well with increasing CPU cores, and its GPU version, implemented in OpenCL, can have up to 158x speedups compared to ASTRAL-III. Using GPUs and multiple cores, ASTRAL-MP is able to analyze datasets with 10 000 species or datasets with more than 100 000 genes in