{"title":"FPGA Acceleration of the Phylogenetic Parsimony Kernel?","authors":"Nikolaos S. Alachiotis, A. Stamatakis","doi":"10.1109/FPL.2011.83","DOIUrl":null,"url":null,"abstract":"The phylogenetic parsimony function is a popular, discrete criterion for reconstructing evolutionary trees based on molecular sequence data. Parsimony strives to find the phylogenetic tree that explains the evolutionary history of organisms by the least number of mutations. Because parsimony is a discrete function, it should fit well to FPGAs. We present a versatile FPGA implementation of the parsimony function and compare its performance to a highly optimized SSE3- and AVX-vectorized software implementation. We find that, because of a particular constellation in our lab, the speedups that can be achieved by using an FPGA, are substantially less impressive, than usually reported in papers on FPGA acceleration of bioinformatics kernels. We conclude that, a competitive spirit between SW and HW application developers can contribute toward obtaining more objective performance comparisons.","PeriodicalId":311066,"journal":{"name":"2011 21st International Conference on Field Programmable Logic and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 21st International Conference on Field Programmable Logic and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FPL.2011.83","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
Abstract
The phylogenetic parsimony function is a popular, discrete criterion for reconstructing evolutionary trees based on molecular sequence data. Parsimony strives to find the phylogenetic tree that explains the evolutionary history of organisms by the least number of mutations. Because parsimony is a discrete function, it should fit well to FPGAs. We present a versatile FPGA implementation of the parsimony function and compare its performance to a highly optimized SSE3- and AVX-vectorized software implementation. We find that, because of a particular constellation in our lab, the speedups that can be achieved by using an FPGA, are substantially less impressive, than usually reported in papers on FPGA acceleration of bioinformatics kernels. We conclude that, a competitive spirit between SW and HW application developers can contribute toward obtaining more objective performance comparisons.