一种FPGA硬件加速的树长度间接计算方法,用于系统发育树重建

Henry Block, T. Maruyama
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引用次数: 7

摘要

在这项工作中,我们提出了一个FPGA硬件实现,用于最大简约算法的系统发育树重建。我们的方法基于一种特殊的随机局部搜索算法,该算法使用树长度的间接计算方法和渐进邻域。在我们的实现中,我们定义了一个树形结构,并通过并行和管道处理来加速搜索。我们展示了六个真实世界生物数据集的结果。我们将执行时间与之前的硬件方法和TNT(可用的最快的省钱程序)进行比较。与之前的方法相比,每次重排的加速率在34到45之间,整个搜索的加速率在2到6之间。在TNT作用下,每次重排的加速率为2 ~ 4,整个搜索的加速率为18 ~ 112。我们估计,对于更大的数据集,这些加速率可能会增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An FPGA hardware acceleration of the indirect calculation of tree lengths method for phylogenetic tree reconstruction
In this work, we present an FPGA hardware implementation for a phylogenetic tree reconstruction with maximum parsimony algorithm. We base our approach on a particular stochastic local search algorithm that uses the Indirect Calculation of Tree Lengths method and the Progressive Neighborhood. In our implementation, we define a tree structure, and accelerate the search by parallel and pipeline processing. We show results for six real-world biological datasets. We compare execution times against our previous hardware approach, and TNT, the fastest available parsimony program. Acceleration rates between 34 to 45 per rearrangement, and 2 to 6, for the whole search, are obtained against our previous approach. Acceleration rates between 2 to 4 per rearrangement, and 18 to 112, for the whole search, are obtained against TNT. We estimate that these acceleration rates could increase for even larger datasets.
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