{"title":"基于fpga的心脏收缩阵列加速BWA-MEM基因组图谱算法","authors":"Ernst Houtgast, V. Sima, K. Bertels, Z. Al-Ars","doi":"10.1109/SAMOS.2015.7363679","DOIUrl":null,"url":null,"abstract":"We present the first accelerated implementation of BWA-MEM, a popular genome sequence alignment algorithm widely used in next generation sequencing genomics pipelines. The Smith-Waterman-like sequence alignment kernel requires a significant portion of overall execution time. We propose and evaluate a number of FPGA-based systolic array architectures, presenting optimizations generally applicable to variable length Smith-Waterman execution. Our kernel implementation is up to 3× faster, compared to software-only execution. This translates into an overall application speedup of up to 45%, which is 96% of the theoretically maximum achievable speedup when accelerating only this kernel.","PeriodicalId":346802,"journal":{"name":"2015 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"58","resultStr":"{\"title\":\"An FPGA-based systolic array to accelerate the BWA-MEM genomic mapping algorithm\",\"authors\":\"Ernst Houtgast, V. Sima, K. Bertels, Z. Al-Ars\",\"doi\":\"10.1109/SAMOS.2015.7363679\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present the first accelerated implementation of BWA-MEM, a popular genome sequence alignment algorithm widely used in next generation sequencing genomics pipelines. The Smith-Waterman-like sequence alignment kernel requires a significant portion of overall execution time. We propose and evaluate a number of FPGA-based systolic array architectures, presenting optimizations generally applicable to variable length Smith-Waterman execution. Our kernel implementation is up to 3× faster, compared to software-only execution. This translates into an overall application speedup of up to 45%, which is 96% of the theoretically maximum achievable speedup when accelerating only this kernel.\",\"PeriodicalId\":346802,\"journal\":{\"name\":\"2015 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"58\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAMOS.2015.7363679\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAMOS.2015.7363679","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An FPGA-based systolic array to accelerate the BWA-MEM genomic mapping algorithm
We present the first accelerated implementation of BWA-MEM, a popular genome sequence alignment algorithm widely used in next generation sequencing genomics pipelines. The Smith-Waterman-like sequence alignment kernel requires a significant portion of overall execution time. We propose and evaluate a number of FPGA-based systolic array architectures, presenting optimizations generally applicable to variable length Smith-Waterman execution. Our kernel implementation is up to 3× faster, compared to software-only execution. This translates into an overall application speedup of up to 45%, which is 96% of the theoretically maximum achievable speedup when accelerating only this kernel.