{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/34387"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/34387","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Towards Algorithm Transformation for Temporal Data Mining on GPU","abstract":"Data Mining allows one to analyze large amounts of data. With increasing amounts of data being collected, more computing power is needed to mine these larger and larger sums of data. The GPU is an excellent piece of hardware with a compelling price to performance ratio and has rapidly risen in popularity. However, this increase in speed comes at a cost. The GPU's architecture executes non-data parallel code with either marginal speedup or even slowdown. The type of data mining we examine, temporal data mining, uses a ¯nite state machine (FSM), which is non-data parallel. We contribute the concept of algorithm transformation for increasing the data parallelism of an algorithm. We apply the algorithm transformation process to the problem of temporal data mining which solves the same problem as the FSM-based algorithm, but is data parallel. The new GPU implementation shows a 6x speedup over the best CPU implementation and 11x speedup over a previous GPU implementation.","abstract_html":"Data Mining allows one to analyze large amounts of data. With increasing amounts of data being collected, more computing power is needed to mine these larger and larger sums of data. The GPU is an excellent piece of hardware with a compelling price to performance ratio and has rapidly risen in popularity. However, this increase in speed comes at a cost. The GPU's architecture executes non-data parallel code with either marginal speedup or even slowdown. The type of data mining we examine, temporal data mining, uses a ¯nite state machine (FSM), which is non-data parallel. We contribute the concept of algorithm transformation for increasing the data parallelism of an algorithm. We apply the algorithm transformation process to the problem of temporal data mining which solves the same problem as the FSM-based algorithm, but is data parallel. The new GPU implementation shows a 6x speedup over the best CPU implementation and 11x speedup over a previous GPU implementation.","abstract_has_math":false,"creators":["Ponce, Sean Philip"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science","degree_department":"Computer Science","school":null,"contributors":[],"advisors":[],"committee_chairs":["Cao, Yong"],"committee_members":["Feng, Wu-chun","Ramakrishnan, Naren"],"year":2009,"date_issued":"2009-07-07","date_published":"2009-07-07","updated_at":"2026-07-22T22:19:12Z","subjects":["CUDA","GPGPU","temporal data mining"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-08062009-133358"],"render_values":[{"text":"etd-08062009-133358","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/34387","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Cao, Yong"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Feng, Wu-chun","Ramakrishnan, Naren"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science"]},{"key":"dc:creator","label":"Author","values":["Ponce, Sean Philip"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:42:53Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:42:53Z","2009-08-18"]},{"key":"dc:date.issued","label":"Date","values":["2009-07-07"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["CUDA","GPGPU","temporal data mining"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-08062009-133358"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/34387"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Data Mining allows one to analyze large amounts of data. With increasing amounts of data being collected, more computing power is needed to mine these larger and larger sums of data. The GPU is an excellent piece of hardware with a compelling price to performance ratio and has rapidly risen in popularity. However, this increase in speed comes at a cost. The GPU's architecture executes non-data parallel code with either marginal speedup or even slowdown. The type of data mining we examine, temporal data mining, uses a ¯nite state machine (FSM), which is non-data parallel. We contribute the concept of algorithm transformation for increasing the data parallelism of an algorithm. We apply the algorithm transformation process to the problem of temporal data mining which solves the same problem as the FSM-based algorithm, but is data parallel. The new GPU implementation shows a 6x speedup over the best CPU implementation and 11x speedup over a previous GPU implementation."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Towards Algorithm Transformation for Temporal Data Mining on GPU"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Cao, Yong"],"dc:contributor.committeemember":["Feng, Wu-chun","Ramakrishnan, Naren"],"dc:contributor.department":["Computer Science"],"dc:creator":["Ponce, Sean Philip"],"dc:date.accessioned":["2014-03-14T20:42:53Z"],"dc:date.available":["2014-03-14T20:42:53Z","2009-08-18"],"dc:date.issued":["2009-07-07"],"dc:description.abstract":["Data Mining allows one to analyze large amounts of data. With increasing amounts of data being collected, more computing power is needed to mine these larger and larger sums of data. 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