{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101639"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101639","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"GPU acceleration of advanced K-mer counting for computational genomics","abstract":"k-mer counting is a popular pre-processing step in many bioinformatic algorithms. KMC2 is one of the most popular tools for k-mer counting. In this work, we leverage the computational power of the GPU to accelerate KMC2. Our goal is to reduce the overall runtime of many genome analysis tasks that use k-mer counting as an essential step. We achieved 4.03x speedup using one GTX 1080 Ti with one CPU (Xeon E5-2603) thread and 5.88x speedup using one GPU with four CPU threads over KMC2 running on a single CPU thread. This speedup is significant because accelerating k-mer counting is challenging due to reasons like serialized portions of code and overhead of disk operations.","abstract_html":"k-mer counting is a popular pre-processing step in many bioinformatic algorithms. KMC2 is one of the most popular tools for k-mer counting. In this work, we leverage the computational power of the GPU to accelerate KMC2. Our goal is to reduce the overall runtime of many genome analysis tasks that use k-mer counting as an essential step. We achieved 4.03x speedup using one GTX 1080 Ti with one CPU (Xeon E5-2603) thread and 5.88x speedup using one GPU with four CPU threads over KMC2 running on a single CPU thread. This speedup is significant because accelerating k-mer counting is challenging due to reasons like serialized portions of code and overhead of disk operations.","abstract_has_math":false,"creators":["Li, Huiren"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Chen, Deming"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:28:01Z","date_published":"2018-09-27T16:28:01Z","updated_at":"2026-07-22T22:24:40Z","subjects":["k-mer counting","GPU acceleration"],"languages":["en"],"rights":["Copyright 2018 Huiren Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101639","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chen, Deming"]},{"key":"dc:creator","label":"Author","values":["Li, Huiren"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:28:01Z","2020-09-28T09:15:24Z","2018-05-15","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["k-mer counting","GPU acceleration"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Huiren Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101639"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["k-mer counting is a popular pre-processing step in many bioinformatic algorithms. KMC2 is one of the most popular tools for k-mer counting. In this work, we leverage the computational power of the GPU to accelerate KMC2. Our goal is to reduce the overall runtime of many genome analysis tasks that use k-mer counting as an essential step. We achieved 4.03x speedup using one GTX 1080 Ti with one CPU (Xeon E5-2603) thread and 5.88x speedup using one GPU with four CPU threads over KMC2 running on a single CPU thread. This speedup is significant because accelerating k-mer counting is challenging due to reasons like serialized portions of code and overhead of disk operations.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Huiren Li, accepted the attached license on 2018-05-14 at 19:09.","The student, Huiren Li, submitted this Thesis for approval on 2018-05-14 at 19:13.","This Thesis was approved for publication on 2018-05-15 at 09:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12571 on 2018-09-27 at 11:15:40","Made available in DSpace on 2018-09-27T16:28:01Z (GMT). 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KMC2 is one of the most popular tools for k-mer counting. In this work, we leverage the computational power of the GPU to accelerate KMC2. Our goal is to reduce the overall runtime of many genome analysis tasks that use k-mer counting as an essential step. We achieved 4.03x speedup using one GTX 1080 Ti with one CPU (Xeon E5-2603) thread and 5.88x speedup using one GPU with four CPU threads over KMC2 running on a single CPU thread. This speedup is significant because accelerating k-mer counting is challenging due to reasons like serialized portions of code and overhead of disk operations.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Huiren Li, accepted the attached license on 2018-05-14 at 19:09.","The student, Huiren Li, submitted this Thesis for approval on 2018-05-14 at 19:13.","This Thesis was approved for publication on 2018-05-15 at 09:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12571 on 2018-09-27 at 11:15:40","Made available in DSpace on 2018-09-27T16:28:01Z (GMT). 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