{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:38:22Z","timestamp":1772725102428,"version":"3.50.1"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2017,1,11]],"date-time":"2017-01-11T00:00:00Z","timestamp":1484092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGARCH Comput. Archit. News"],"published-print":{"date-parts":[[2017,1,11]]},"abstract":"<jats:p>Next Generation Sequencing techniques have resulted in an exponential growth in the generation of genetics data, the amount of which will soon rival, if not overtake, other Big Data fields, such as astronomy and streaming video services. To become useful, this data requires processing by a complex pipeline of algorithms, taking multiple days even on large clusters. The mapping stage of such genomics pipelines, which maps the short reads onto a reference genome, takes up a significant portion of execution time. BWA-MEM is the de-facto industry-standard for the mapping stage.<\/jats:p>\n          <jats:p>Here, a GPU-accelerated implementation of BWA-MEM is proposed. The Seed Extension phase, one of the three main BWA-MEM algorithm phases that requires between 30%-50% of overall processing time, is offloaded onto the GPU. A thorough design space analysis is presented for an optimized mapping of this phase onto the GPU. The re- sulting systolic-array based implementation obtains a two- fold overall application-level speedup, which is the maximum theoretically achievable speedup. Moreover, this speedup is sustained for systems with up to twenty-two logical cores. Based on the findings, a number of suggestions are made to improve GPU architecture, resulting in potentially greatly increased performance for bioinformatics-class algorithms.<\/jats:p>","DOI":"10.1145\/3039902.3039910","type":"journal-article","created":{"date-parts":[[2017,1,17]],"date-time":"2017-01-17T13:42:08Z","timestamp":1484660528000},"page":"38-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["An Efficient GPUAccelerated Implementation of Genomic Short Read Mapping with BWAMEM"],"prefix":"10.1145","volume":"44","author":[{"given":"Ernst Joachim","family":"Houtgast","sequence":"first","affiliation":[{"name":"Computer Engineering Lab, TU Delft, Delft, The Netherlands and Bluebee, The Netherlands"}]},{"given":"VladMihai","family":"Sima","sequence":"additional","affiliation":[{"name":"Bluebee, The Netherlands"}]},{"given":"Koen","family":"Bertels","sequence":"additional","affiliation":[{"name":"Computer Engineering Lab, TU Delft, Delft, The Netherlands"}]},{"given":"Zaid","family":"AlArs","sequence":"additional","affiliation":[{"name":"Computer Engineering Lab, TU Delft, Delft, The Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2017,1,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2840819.2840854"},{"key":"e_1_2_1_2_1","volume-title":"Next Generation Genomics: World Map of High-throughput Sequencers","author":"Hadfield James","unstructured":"James Hadfield and Nick Loman . Next Generation Genomics: World Map of High-throughput Sequencers . http:\/\/omicsmaps.com, 2016. Accessed: 2016-01-13. James Hadfield and Nick Loman. Next Generation Genomics: World Map of High-throughput Sequencers. http:\/\/omicsmaps.com, 2016. Accessed: 2016-01-13."},{"key":"e_1_2_1_3_1","volume-title":"An Analytical Framework for Optimizing Variant Discovery from Personal Genomes. Nature comm., 6","author":"Highnam Gareth","year":"2015","unstructured":"Gareth Highnam , Jason J. Wang , Dean Kusler , Justin Zook , Vinaya Vijayan , Nir Leibovich , and David Mittelman . An Analytical Framework for Optimizing Variant Discovery from Personal Genomes. Nature comm., 6 , 2015 . Gareth Highnam, Jason J. Wang, Dean Kusler, Justin Zook, Vinaya Vijayan, Nir Leibovich, and David Mittelman. An Analytical Framework for Optimizing Variant Discovery from Personal Genomes. Nature comm., 6, 2015."},{"key":"e_1_2_1_4_1","volume-title":"Conf. on Embedded Computer Systems: Architectures, Modeling, and Simulation","author":"Houtgast E.J.","year":"2015","unstructured":"E.J. Houtgast , V. Sima , K.L.M. Bertels , and Z. Al-Ars . An FPGA-Based Systolic Array to Accelerate the BWA-MEM Genomic Mapping Algorithm. In Int'l . Conf. on Embedded Computer Systems: Architectures, Modeling, and Simulation , 2015 . E.J. Houtgast, V. Sima, K.L.M. Bertels, and Z. Al-Ars. An FPGA-Based Systolic Array to Accelerate the BWA-MEM Genomic Mapping Algorithm. In Int'l. Conf. on Embedded Computer Systems: Architectures, Modeling, and Simulation, 2015."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-30695-7_10"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM.2016.17"},{"key":"e_1_2_1_7_1","volume-title":"Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM. arXiv preprint arXiv:1303.3997","author":"Li Heng","year":"2013","unstructured":"Heng Li . Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM. arXiv preprint arXiv:1303.3997 , 2013 . Heng Li. Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM. arXiv preprint arXiv:1303.3997, 2013."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2009.5160931"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts061"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts276"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-117"},{"key":"e_1_2_1_12_1","volume-title":"Identification of Common Molecular Subsequences. Journal of molecular biology, 147(1):195--197","author":"Smith T.F.","year":"1981","unstructured":"T.F. Smith and MS Waterman . Identification of Common Molecular Subsequences. Journal of molecular biology, 147(1):195--197 , 1981 . T.F. Smith and MS Waterman. Identification of Common Molecular Subsequences. Journal of molecular biology, 147(1):195--197, 1981."},{"key":"e_1_2_1_13_1","volume-title":"Big Data: Astronomical or Genomical? PLoS Biology, 13(7)","author":"Stephens Z.D.","year":"2015","unstructured":"Z.D. Stephens , S.Y. Lee , F. Faghri , R.H. Campbell , C. Zhai , M.J. Efron , R. Iyer , M.C. Schatz , S. Sinha , and G.E. Robinson . Big Data: Astronomical or Genomical? PLoS Biology, 13(7) , 2015 . Z.D. Stephens, S.Y. Lee, F. Faghri, R.H. Campbell, C. Zhai, M.J. Efron, R. Iyer, M.C. Schatz, S. Sinha, and G.E. Robinson. Big Data: Astronomical or Genomical? PLoS Biology, 13(7), 2015."}],"container-title":["ACM SIGARCH Computer Architecture News"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3039902.3039910","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3039902.3039910","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:36:31Z","timestamp":1750217791000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3039902.3039910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,11]]},"references-count":13,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,1,11]]}},"alternative-id":["10.1145\/3039902.3039910"],"URL":"https:\/\/doi.org\/10.1145\/3039902.3039910","relation":{},"ISSN":["0163-5964"],"issn-type":[{"value":"0163-5964","type":"print"}],"subject":[],"published":{"date-parts":[[2017,1,11]]},"assertion":[{"value":"2017-01-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}