基于AxML/PAxML的PC架构系统发育树推断

A. Stamatakis, T. Ludwig
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引用次数: 15

摘要

基于最大似然方法的包含数百甚至数千个生物体的系统发育树的推断在计算上是非常昂贵的。在之前的工作中,我们引入了子树相等向量(SEV)来显著减少拓扑计算过程中所需的浮点运算次数,并在(P)AxML中实现了该方法,AxML是(并行)fastDNAml的导数。实验结果表明(P)AxML在廉价的pc处理器架构上扩展得特别好,对于大型数据集,它比(并行)fastDNAml获得51%到65%的全局运行时加速,同时呈现完全相同的输出。在本文中,我们提出了一个额外的基于sev的算法优化,它可以在PC处理器上很好地扩展,并且与初始版本的AxML相比,全局执行时间进一步提高了14%到19%。此外,我们提出了新的基于距离的启发式方法来减少分析树拓扑的数量,这进一步使程序的速度提高了4%到8%。最后,我们讨论了一种新的实验性树构建算法和推断大型高质量树的潜在启发式解决方案,该算法在一些初始测试中呈现出更好的树,同时将程序执行速度提高了6倍以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Phylogenetic tree inference on PC architectures with AxML/PAxML
Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the maximum likelihood method is computationally extremely expensive. In previous work, we have introduced subtree equality vectors (SEV) to significantly reduce the number of required floating point operations during topology evaluation and implemented this method in (P)AxML, which is a derivative of (parallel) fastDNAml. Experimental results show that (P)AxML scales particularly well on inexpensive PC-processor architectures obtaining global run time accelerations between 51% and 65% over (parallel) fastDNAml for large data sets, yet rendering exactly the same output. In this paper, we present an additional SEV-based algorithmic optimization which scales well on PC processors and leads to a further improvement of global execution times of 14% to 19% compared to the initial version of AxML. Furthermore, we present novel distance-based heuristics for reducing the number of analyzed tree topologies, which further accelerate the program by 4% up to 8%. Finally, we discuss a novel experimental tree-building algorithm and potential heuristic solutions for inferring large high quality trees, which for some initial tests rendered better trees and accelerated program execution at the same time by a factor greater than 6.
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