{"title":"The fast Viterbi algorithm caching Profile Hidden Markov Models on graphic processing units","authors":"Jun Li, Yanhui Li, Shuangping Chen","doi":"10.1109/CSAE.2011.5952535","DOIUrl":null,"url":null,"abstract":"Profile Hidden Markov Models are used as a popular tool in bioinformatics research and a regular task is to compare a set of protein sequences with a database of models according to sequences' score on these models. However, it suffers from long runtimes on PC platforms, and the runtimes are increasing rapidly due to the rapid growth in size of both sequences and models. In this paper, we present a Viterbi algorithm running on graphic processing units to score sequences, a method padding HMMs and a memory hierarchy are also introduced, these strategies can promote running efficiency in parallel and reduce impact of the bottleneck from buses. Experimental results show the runtimes are saved by the method dramatically.","PeriodicalId":138215,"journal":{"name":"2011 IEEE International Conference on Computer Science and Automation Engineering","volume":"98 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Computer Science and Automation Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSAE.2011.5952535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Profile Hidden Markov Models are used as a popular tool in bioinformatics research and a regular task is to compare a set of protein sequences with a database of models according to sequences' score on these models. However, it suffers from long runtimes on PC platforms, and the runtimes are increasing rapidly due to the rapid growth in size of both sequences and models. In this paper, we present a Viterbi algorithm running on graphic processing units to score sequences, a method padding HMMs and a memory hierarchy are also introduced, these strategies can promote running efficiency in parallel and reduce impact of the bottleneck from buses. Experimental results show the runtimes are saved by the method dramatically.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
