{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/17377"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/17377","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Accelerating genomic sequence alignment using high performance reconfigurable computers","abstract":"Reconfigurable computing technology has progressed to a stage where it is now possible to achieve orders of magnitude performance and power efficiency gains over conventional computer architectures for a subset of high performance computing applications. In this thesis, we investigate the potential of reconfigurable computers to accelerate genomic sequence alignment specifically for genome sequencing applications. We present a highly optimized implementation of a parallel sequence alignment algorithm for the Berkeley Emulation Engine (BEE2) reconfigurable computer, allowing a single BEE2 to align simultaneously hundreds of sequences. For each reconfigurable processor (FPGA), we demonstrate a 61X speedup versus a state-of-the-art implementation on a modern conventional CPU core, and a 56X improvement in performance-per-Watt. We also show that our implementation is highly scalable and we provide performance results from a cluster implementation using 32 FPGAs. We conclude that reconfigurable computers provide an excellent platform on which to run sequence alignment, and that clusters of reconfigurable computers will be able to cope far more easily with the vast quantities of data produced by new ultra-high-throughput sequencers.","abstract_html":"Reconfigurable computing technology has progressed to a stage where it is now possible to achieve orders of magnitude performance and power efficiency gains over conventional computer architectures for a subset of high performance computing applications. In this thesis, we investigate the potential of reconfigurable computers to accelerate genomic sequence alignment specifically for genome sequencing applications. We present a highly optimized implementation of a parallel sequence alignment algorithm for the Berkeley Emulation Engine (BEE2) reconfigurable computer, allowing a single BEE2 to align simultaneously hundreds of sequences. For each reconfigurable processor (FPGA), we demonstrate a 61X speedup versus a state-of-the-art implementation on a modern conventional CPU core, and a 56X improvement in performance-per-Watt. We also show that our implementation is highly scalable and we provide performance results from a cluster implementation using 32 FPGAs. We conclude that reconfigurable computers provide an excellent platform on which to run sequence alignment, and that clusters of reconfigurable computers will be able to cope far more easily with the vast quantities of data produced by new ultra-high-throughput sequencers.","abstract_has_math":false,"creators":["McMahon, Peter Leonard"],"institution":"Department of Computer Science","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Kuttel, Michelle Mary"],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008","date_published":"2008","updated_at":"2026-07-22T22:23:26Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/17377","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kuttel, Michelle Mary"]},{"key":"dc:creator","label":"Author","values":["McMahon, Peter Leonard"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-02-29T12:07:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-02-29T12:07:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2008"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Computer Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/17377"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Includes bibliographical references (pages 65-70)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Reconfigurable computing technology has progressed to a stage where it is now possible to achieve orders of magnitude performance and power efficiency gains over conventional computer architectures for a subset of high performance computing applications. In this thesis, we investigate the potential of reconfigurable computers to accelerate genomic sequence alignment specifically for genome sequencing applications. We present a highly optimized implementation of a parallel sequence alignment algorithm for the Berkeley Emulation Engine (BEE2) reconfigurable computer, allowing a single BEE2 to align simultaneously hundreds of sequences. For each reconfigurable processor (FPGA), we demonstrate a 61X speedup versus a state-of-the-art implementation on a modern conventional CPU core, and a 56X improvement in performance-per-Watt. We also show that our implementation is highly scalable and we provide performance results from a cluster implementation using 32 FPGAs. We conclude that reconfigurable computers provide an excellent platform on which to run sequence alignment, and that clusters of reconfigurable computers will be able to cope far more easily with the vast quantities of data produced by new ultra-high-throughput sequencers."]},{"key":"dc:title","label":"Title","values":["Accelerating genomic sequence alignment using high performance reconfigurable computers"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kuttel, Michelle Mary"],"dc:creator":["McMahon, Peter Leonard"],"dc:date.accessioned":["2016-02-29T12:07:18Z"],"dc:date.available":["2016-02-29T12:07:18Z"],"dc:date.issued":["2008"],"dc:description":["Includes bibliographical references (pages 65-70)."],"dc:description.abstract":["Reconfigurable computing technology has progressed to a stage where it is now possible to achieve orders of magnitude performance and power efficiency gains over conventional computer architectures for a subset of high performance computing applications. In this thesis, we investigate the potential of reconfigurable computers to accelerate genomic sequence alignment specifically for genome sequencing applications. We present a highly optimized implementation of a parallel sequence alignment algorithm for the Berkeley Emulation Engine (BEE2) reconfigurable computer, allowing a single BEE2 to align simultaneously hundreds of sequences. For each reconfigurable processor (FPGA), we demonstrate a 61X speedup versus a state-of-the-art implementation on a modern conventional CPU core, and a 56X improvement in performance-per-Watt. We also show that our implementation is highly scalable and we provide performance results from a cluster implementation using 32 FPGAs. 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