{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81624"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81624","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"General -Purpose Processors for Multimedia Applications: Predictability and Energy Efficiency","abstract":"We next apply the above findings towards improving the energy efficiency of general-purpose processors for real-time multimedia applications. Recently, researchers have proposed two forms of hardware adaptation to improve energy efficiency of these processors: architecture adaptation and dynamic voltage/frequency scaling (DVS). A key to effective adaptation is the control algorithm, which determines when and what to adapt. We develop algorithms based on two opportunities for saving energy in modern processors: (1) they often run faster than necessary for the application's real-time constraint, and (2) often resources stay active, consuming energy, but contribute little to performance. Our algorithms are the first to use both architecture adaptation and DVS and exploit both opportunities for saving energy for multimedia applications. Our final algorithm is based on formal optimization theory, which lends a key advantage over previously proposed algorithms: it requires little tuning of its design parameters for an actual implementation. Our final algorithm is predictive---its decisions are made using predictions about processor behavior obtained from a small profiling phase, rather than from continuous measurement. Thus, the algorithm can predict its behavior for the rest of an application after this phase. The results show the algorithm is effective at saving energy in a variety of scenarios, architecture adaptation is effective with and without DVS, and exploiting both opportunities for saving energy gives significant gains.","abstract_html":"We next apply the above findings towards improving the energy efficiency of general-purpose processors for real-time multimedia applications. Recently, researchers have proposed two forms of hardware adaptation to improve energy efficiency of these processors: architecture adaptation and dynamic voltage/frequency scaling (DVS). A key to effective adaptation is the control algorithm, which determines when and what to adapt. We develop algorithms based on two opportunities for saving energy in modern processors: (1) they often run faster than necessary for the application's real-time constraint, and (2) often resources stay active, consuming energy, but contribute little to performance. Our algorithms are the first to use both architecture adaptation and DVS and exploit both opportunities for saving energy for multimedia applications. Our final algorithm is based on formal optimization theory, which lends a key advantage over previously proposed algorithms: it requires little tuning of its design parameters for an actual implementation. Our final algorithm is predictive---its decisions are made using predictions about processor behavior obtained from a small profiling phase, rather than from continuous measurement. Thus, the algorithm can predict its behavior for the rest of an application after this phase. The results show the algorithm is effective at saving energy in a variety of scenarios, architecture adaptation is effective with and without DVS, and exploiting both opportunities for saving energy gives significant gains.","abstract_has_math":false,"creators":["Hughes, Christopher J."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Adve, Sarita V."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:19:35Z","date_published":"2015-09-25T20:19:35Z","updated_at":"2026-07-22T22:26:16Z","subjects":["Computer Science"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3101869"],"render_values":[{"text":"(MiAaPQ)AAI3101869","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81624","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Adve, Sarita V."]},{"key":"dc:creator","label":"Author","values":["Hughes, Christopher J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:19:35Z","10000-01-01","2003"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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":["Computer Science"]}]},{"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/2142/81624","(MiAaPQ)AAI3101869"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We next apply the above findings towards improving the energy efficiency of general-purpose processors for real-time multimedia applications. Recently, researchers have proposed two forms of hardware adaptation to improve energy efficiency of these processors: architecture adaptation and dynamic voltage/frequency scaling (DVS). A key to effective adaptation is the control algorithm, which determines when and what to adapt. We develop algorithms based on two opportunities for saving energy in modern processors: (1) they often run faster than necessary for the application's real-time constraint, and (2) often resources stay active, consuming energy, but contribute little to performance. Our algorithms are the first to use both architecture adaptation and DVS and exploit both opportunities for saving energy for multimedia applications. Our final algorithm is based on formal optimization theory, which lends a key advantage over previously proposed algorithms: it requires little tuning of its design parameters for an actual implementation. Our final algorithm is predictive---its decisions are made using predictions about processor behavior obtained from a small profiling phase, rather than from continuous measurement. Thus, the algorithm can predict its behavior for the rest of an application after this phase. The results show the algorithm is effective at saving energy in a variety of scenarios, architecture adaptation is effective with and without DVS, and exploiting both opportunities for saving energy gives significant gains.","Made available in DSpace on 2015-09-25T20:19:35Z (GMT). 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Recently, researchers have proposed two forms of hardware adaptation to improve energy efficiency of these processors: architecture adaptation and dynamic voltage/frequency scaling (DVS). A key to effective adaptation is the control algorithm, which determines when and what to adapt. We develop algorithms based on two opportunities for saving energy in modern processors: (1) they often run faster than necessary for the application's real-time constraint, and (2) often resources stay active, consuming energy, but contribute little to performance. Our algorithms are the first to use both architecture adaptation and DVS and exploit both opportunities for saving energy for multimedia applications. Our final algorithm is based on formal optimization theory, which lends a key advantage over previously proposed algorithms: it requires little tuning of its design parameters for an actual implementation. Our final algorithm is predictive---its decisions are made using predictions about processor behavior obtained from a small profiling phase, rather than from continuous measurement. Thus, the algorithm can predict its behavior for the rest of an application after this phase. The results show the algorithm is effective at saving energy in a variety of scenarios, architecture adaptation is effective with and without DVS, and exploiting both opportunities for saving energy gives significant gains.","Made available in DSpace on 2015-09-25T20:19:35Z (GMT). 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