{"title":"An FPGA hardware acceleration of the indirect calculation of tree lengths method for phylogenetic tree reconstruction","authors":"Henry Block, T. Maruyama","doi":"10.1109/FPL.2014.6927430","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":172795,"journal":{"name":"2014 24th International Conference on Field Programmable Logic and Applications (FPL)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 24th International Conference on Field Programmable Logic and Applications (FPL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FPL.2014.6927430","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
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.