{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81081"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81081","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Program Optimization Strategies for Data-Parallel Many-Core Processors","abstract":"My work discusses various strategies for optimizing programs on a highly data-parallel architecture with fine-grained sharing of resources. I first investigate useful strategies in optimizing a suite of applications. I then introduce program optimization carving, an approach that discovers high-performance application configurations for data-parallel, many-core architectures. Instead of applying a particular phase ordering of optimizations, it starts with an optimization space of major transformations and then reduces the space by examining the static code and pruning configurations that do not maximize desirable qualities in isolation or combination. Careful selection of pruning criteria for applications running on the NVIDIA GeForce 8800 GTX reduces the optimization space by as much as 98% while finding configurations within 1% of the best performance. Random sampling, in contrast, can require nearly five times as many configurations to find performance within 10% of the best. I also examine the technique's effectiveness when varying pruning criteria.","abstract_html":"My work discusses various strategies for optimizing programs on a highly data-parallel architecture with fine-grained sharing of resources. I first investigate useful strategies in optimizing a suite of applications. I then introduce program optimization carving, an approach that discovers high-performance application configurations for data-parallel, many-core architectures. Instead of applying a particular phase ordering of optimizations, it starts with an optimization space of major transformations and then reduces the space by examining the static code and pruning configurations that do not maximize desirable qualities in isolation or combination. Careful selection of pruning criteria for applications running on the NVIDIA GeForce 8800 GTX reduces the optimization space by as much as 98% while finding configurations within 1% of the best performance. Random sampling, in contrast, can require nearly five times as many configurations to find performance within 10% of the best. I also examine the technique's effectiveness when varying pruning criteria.","abstract_has_math":false,"creators":["Ryoo, Shane"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Hwu, Wen-Mei W."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:09:31Z","date_published":"2015-09-25T20:09:31Z","updated_at":"2026-07-22T22:26:15Z","subjects":["Engineering, Electronics and Electrical"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3314878"],"render_values":[{"text":"(MiAaPQ)AAI3314878","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81081","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hwu, Wen-Mei W."]},{"key":"dc:creator","label":"Author","values":["Ryoo, Shane"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:09:31Z","10000-01-01","2008"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Engineering, Electronics and Electrical"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81081","(MiAaPQ)AAI3314878"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["My work discusses various strategies for optimizing programs on a highly data-parallel architecture with fine-grained sharing of resources. I first investigate useful strategies in optimizing a suite of applications. I then introduce program optimization carving, an approach that discovers high-performance application configurations for data-parallel, many-core architectures. Instead of applying a particular phase ordering of optimizations, it starts with an optimization space of major transformations and then reduces the space by examining the static code and pruning configurations that do not maximize desirable qualities in isolation or combination. Careful selection of pruning criteria for applications running on the NVIDIA GeForce 8800 GTX reduces the optimization space by as much as 98% while finding configurations within 1% of the best performance. Random sampling, in contrast, can require nearly five times as many configurations to find performance within 10% of the best. I also examine the technique's effectiveness when varying pruning criteria.","Made available in DSpace on 2015-09-25T20:09:31Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3314878.pdf: 2140924 bytes, checksum: 297ec0089b8213ba48d9cf5e8afdc612 (MD5) Previous issue date: 2008","Embargo set by: Seth Robbins for item 82363 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","146 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008."]},{"key":"dc:title","label":"Title","values":["Program Optimization Strategies for Data-Parallel Many-Core Processors"]}]}],"canonical_facts":{"dc:contributor":["Hwu, Wen-Mei W."],"dc:creator":["Ryoo, Shane"],"dc:date":["2015-09-25T20:09:31Z","10000-01-01","2008"],"dc:description":["My work discusses various strategies for optimizing programs on a highly data-parallel architecture with fine-grained sharing of resources. I first investigate useful strategies in optimizing a suite of applications. I then introduce program optimization carving, an approach that discovers high-performance application configurations for data-parallel, many-core architectures. Instead of applying a particular phase ordering of optimizations, it starts with an optimization space of major transformations and then reduces the space by examining the static code and pruning configurations that do not maximize desirable qualities in isolation or combination. Careful selection of pruning criteria for applications running on the NVIDIA GeForce 8800 GTX reduces the optimization space by as much as 98% while finding configurations within 1% of the best performance. Random sampling, in contrast, can require nearly five times as many configurations to find performance within 10% of the best. I also examine the technique's effectiveness when varying pruning criteria.","Made available in DSpace on 2015-09-25T20:09:31Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3314878.pdf: 2140924 bytes, checksum: 297ec0089b8213ba48d9cf5e8afdc612 (MD5) Previous issue date: 2008","Embargo set by: Seth Robbins for item 82363 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","146 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008."],"dc:identifier":["http://hdl.handle.net/2142/81081","(MiAaPQ)AAI3314878"],"dc:language":["eng"],"dc:subject":["Engineering, Electronics and Electrical"],"dc:title":["Program Optimization Strategies for Data-Parallel Many-Core Processors"],"dc:type":["text"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:15Z"}