{"title":"MSA-CUDA: Multiple Sequence Alignment on Graphics Processing Units with CUDA","authors":"Yongchao Liu, B. Schmidt, D. Maskell","doi":"10.1109/ASAP.2009.14","DOIUrl":null,"url":null,"abstract":"Progressive alignment is a widely used approach for computing multiple sequence alignments (MSAs). However, aligning several hundred or thousand sequences with popular progressive alignment tools such as ClustalW requires hours or even days on state-of-the-art workstations. This paper presents MSA-CUDA, a parallel MSA program, which parallelizes all three stages of the ClustalW processing pipeline using CUDA and achieves significant speedups compared to the sequential ClustalW for a variety of large protein sequence datasets. Our tests on a GeForce GTX 280 GPU demonstrate average speedups of 36.91 (for long protein sequences), 18.74 (for average-length protein sequences), and 11.27 (for short protein sequences) compared to the sequential ClustalW running on a Pentium 4 3.0 GHz processor. Our MSA-CUDA outperforms ClustalW-MPI running on 32 cores of a high performance workstation cluster.","PeriodicalId":202421,"journal":{"name":"2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"93","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASAP.2009.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 93
Abstract
Progressive alignment is a widely used approach for computing multiple sequence alignments (MSAs). However, aligning several hundred or thousand sequences with popular progressive alignment tools such as ClustalW requires hours or even days on state-of-the-art workstations. This paper presents MSA-CUDA, a parallel MSA program, which parallelizes all three stages of the ClustalW processing pipeline using CUDA and achieves significant speedups compared to the sequential ClustalW for a variety of large protein sequence datasets. Our tests on a GeForce GTX 280 GPU demonstrate average speedups of 36.91 (for long protein sequences), 18.74 (for average-length protein sequences), and 11.27 (for short protein sequences) compared to the sequential ClustalW running on a Pentium 4 3.0 GHz processor. Our MSA-CUDA outperforms ClustalW-MPI running on 32 cores of a high performance workstation cluster.