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Computing the Phylogenetic Likelihood Function Out-of-Core

机译:计算系统发育似然函数外核心

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The computation of the phylogenetic likelihood function for reconstructing evolutionary trees from molecular sequence data is both memory- and compute-intensive. Based on our experience with the user community of RAxML, memory-shortages (as opposed to CPU time limitations) are currently the prevalent problem regarding resource availability, that is, lack of memory hinders large-scale biological analyses. To this end, we study the performance of an out-of-core execution of the phylogenetic likelihood function by means of a proof-of-concept implementation in RAxML. We find that RAM miss rates are below 10%, even if only 5% of the required data structures are held in RAM. Moreover, we show that our proof-of-concept implementation runs more than 5 times faster than the respective standard implementation when paging is used. The concepts presented here can be applied to all programs that rely on the phylogenetic likelihood function and can contribute significantly to enabling the computation of whole-genome phylogenies.
机译:从分子序列数据重建进化树的系统发育似然函数的计算是记忆和计算密集的。基于我们对raxml的用户社区的经验,内存短缺(与CPU时间限制相反)是目前有关资源可用性的普遍存在问题,即缺乏内存阻碍大规模生物分析。为此,我们通过RAXML中的概念证据实现研究了系统发育似然函数的缺失执行的性能。我们发现RAM Miss率低于10%,即使只有5%的所需数据结构持有RAM。此外,我们表明我们的概念证据实现速度比使用分页时的各个标准实现速度快5倍。这里呈现的概念可以应用于依赖于系统发育似然功能的所有程序,并且可以显着贡献以实现全基因组的计算。

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