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Accelerating parallel maximum likelihood-based phylogenetic tree calculations using subtree equality vectors

机译:使用子树相等向量加快基于并行最大似然的系统树的计算

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Heuristics for calculating phylogenetic trees for a large sets of aligned rRNA sequences based on the maximum likelihood method are computationally expensive. The core of most parallel algorithms, which accounts for the greatest part of computation time, is the tree evaluation function, that calculates the likelihood value for each tree topology. This paper describes and uses Subtree Equality Vectors (SEVs) to reduce the number of required floating point operations during topology evaluation.We integrated our optimizations into various sequential programs and into parallel fastDNAml, one of the most common and efficient parallel programs for calculating large phylogenetic trees.Experimental results for our parallel program, which renders exactly the same output as parallel fastDNAml show global run time improvements of 26% to 65%. The optimization scales best on clusters of PCs, which also implies a substantial cost saving factor for the determination of large trees.
机译:基于最大似然法来计算大量比对rRNA序列的系统树的启发式方法在计算上是昂贵的。大多数并行算法的核心是最大的计算时间,它是树评估函数,该函数计算每种树拓扑的似然值。本文介绍并使用子树等式向量(SEV)来减少拓扑评估期间所需的浮点运算次数。我们将优化技术集成到各种顺序程序和 parallel fastDNAml 中,这是最常见的方法之一。用于计算大型系统树的高效并行程序。我们的并行程序的实验结果使完全 parallel fastDNAml 具有相同的输出,显示全局运行时间提高了26%至65% 。该优化在PC群集上的扩展规模最佳,这也意味着在确定大型树木时可节省大量成本。

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