{"id":{"repo_id":"texas-state","oai_identifier":"oai:digital.library.txst.edu:10877/7738"},"canonical_url":"https://search.dev.ndltd.org/etd/texas-state/oai:digital.library.txst.edu:10877/7738","repository":{"repo_id":"texas-state","name":"Texas State University","base_url":"https://digital.library.txst.edu/server/oai/request"},"display":{"title":"GPU Execution Tracing and Compression","abstract":"Program tracing is widely used for debugging and performance optimization. Whenever a program is traced, the overhead in terms of extra runtime and in terms of storage for the generated trace information are a concern. These concerns are greatly exacerbated on GPUs due to the large amount of parallelism. In fact, GPUs provide such massive parallelism that conventional tracing approaches either fail or only manage to trace very few events per thread. Hence, we need not only a low-overhead but also a space-efficient approach to make detailed tracing possible on GPUs. To the best of my knowledge, none of the existing GPU tracing tools support both. Thus, in this thesis, I developed an execution tracing tool for GPUs called ECL-Tracer that is light-weight and immediately compresses the generated trace data before they are stored.","abstract_html":"Program tracing is widely used for debugging and performance optimization. Whenever a program is traced, the overhead in terms of extra runtime and in terms of storage for the generated trace information are a concern. These concerns are greatly exacerbated on GPUs due to the large amount of parallelism. In fact, GPUs provide such massive parallelism that conventional tracing approaches either fail or only manage to trace very few events per thread. Hence, we need not only a low-overhead but also a space-efficient approach to make detailed tracing possible on GPUs. To the best of my knowledge, none of the existing GPU tracing tools support both. Thus, in this thesis, I developed an execution tracing tool for GPUs called ECL-Tracer that is light-weight and immediately compresses the generated trace data before they are stored.","abstract_has_math":false,"creators":["Azimi Moghaddam, Sahar"],"institution":"Texas State University","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Burtscher, Martin"],"committee_chairs":[],"committee_members":["Qasem, Apan","Zong, Ziliang"],"year":2017,"date_issued":"2017-05","date_published":"2017-05","updated_at":"2026-07-27T21:22:32Z","subjects":["GPU execution tracing","GPU trace compression","trace compression","parallel programming","data compression"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10877/7738","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Burtscher, Martin"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Qasem, Apan","Zong, Ziliang"]},{"key":"dc:creator","label":"Author","values":["Azimi Moghaddam, Sahar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-09-19T21:09:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-09-19T21:09:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-05"]},{"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":["Texas State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["GPU execution tracing","GPU trace compression","trace compression","parallel programming","data compression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10877/7738"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Program tracing is widely used for debugging and performance optimization. Whenever a program is traced, the overhead in terms of extra runtime and in terms of storage for the generated trace information are a concern. These concerns are greatly exacerbated on GPUs due to the large amount of parallelism. In fact, GPUs provide such massive parallelism that conventional tracing approaches either fail or only manage to trace very few events per thread. Hence, we need not only a low-overhead but also a space-efficient approach to make detailed tracing possible on GPUs. To the best of my knowledge, none of the existing GPU tracing tools support both. Thus, in this thesis, I developed an execution tracing tool for GPUs called ECL-Tracer that is light-weight and immediately compresses the generated trace data before they are stored."]},{"key":"dc:format","label":"Dc Format","values":["Text"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["1 file (.pdf)"]},{"key":"dc:title","label":"Title","values":["GPU Execution Tracing and Compression"]}]}],"canonical_facts":{"dc:contributor.advisor":["Burtscher, Martin"],"dc:contributor.committeemember":["Qasem, Apan","Zong, Ziliang"],"dc:creator":["Azimi Moghaddam, Sahar"],"dc:date.accessioned":["2018-09-19T21:09:21Z"],"dc:date.available":["2018-09-19T21:09:21Z"],"dc:date.issued":["2017-05"],"dc:description.abstract":["Program tracing is widely used for debugging and performance optimization. Whenever a program is traced, the overhead in terms of extra runtime and in terms of storage for the generated trace information are a concern. These concerns are greatly exacerbated on GPUs due to the large amount of parallelism. In fact, GPUs provide such massive parallelism that conventional tracing approaches either fail or only manage to trace very few events per thread. Hence, we need not only a low-overhead but also a space-efficient approach to make detailed tracing possible on GPUs. To the best of my knowledge, none of the existing GPU tracing tools support both. 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