{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/78431"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/78431","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"PICS - a Performance-analysis-based Introspective Control System to steer parallel applications","abstract":"Parallel programming has always been difficult due to the complexity of hardware and the diversity of applications. Although significant progress has been achieved over the years, attaining high parallel efficiency on large supercomputers for various applications is still quite challenging. As we go beyond the current scale of computers to those with peak capacities of an ExaFLOP/s, it is clear that an introspective and adaptive runtime system (RTS) will be critical to reduce programmers' tuning efforts by automatically handling the complexities of applications and machines. This is the motivation for my research on a Performance-analysis-based Introspective Control System - PICS. PICS intelligently steers parallel applications and runtime system configurations to achieve desired goals by utilizing expert knowledge to analyze performance data and adaptively reconfiguring applications. This thesis designs a holistic introspective control system for automatic performance tuning that combines the real-time performance analysis and performance steering to effectively automate the optimization. A few techniques are explored to make the parallel runtime system and applications more adaptive and controllable. Control points are defined for applications to interact with PICS. Decision tree based automatic performance analysis is implemented to significantly reduce the search space of multiple configurations. Parallel evaluation and sampling techniques are exploited to reduce the overhead of the system and to improve its scalability. In addition, the result of automatic performance analysis can be visualized to help developers manually tune their applications. The utility of PICS is demonstrated with several benchmarks and real- world applications.","abstract_html":"Parallel programming has always been difficult due to the complexity of hardware and the diversity of applications. Although significant progress has been achieved over the years, attaining high parallel efficiency on large supercomputers for various applications is still quite challenging. As we go beyond the current scale of computers to those with peak capacities of an ExaFLOP/s, it is clear that an introspective and adaptive runtime system (RTS) will be critical to reduce programmers' tuning efforts by automatically handling the complexities of applications and machines. This is the motivation for my research on a Performance-analysis-based Introspective Control System - PICS. PICS intelligently steers parallel applications and runtime system configurations to achieve desired goals by utilizing expert knowledge to analyze performance data and adaptively reconfiguring applications. This thesis designs a holistic introspective control system for automatic performance tuning that combines the real-time performance analysis and performance steering to effectively automate the optimization. A few techniques are explored to make the parallel runtime system and applications more adaptive and controllable. Control points are defined for applications to interact with PICS. Decision tree based automatic performance analysis is implemented to significantly reduce the search space of multiple configurations. Parallel evaluation and sampling techniques are exploited to reduce the overhead of the system and to improve its scalability. In addition, the result of automatic performance analysis can be visualized to help developers manually tune their applications. The utility of PICS is demonstrated with several benchmarks and real- world applications.","abstract_has_math":false,"creators":["Sun, Yanhua"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Kale, Laxmikant V.","Gropp, William D.","Kloeckner, Andreas","Balaji, Pavan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-07-22T22:17:12Z","date_published":"2015-07-22T22:17:12Z","updated_at":"2026-07-22T22:26:11Z","subjects":["parallel computing","control points","runtime system","application reconfiguration","automatic performance analysis","large scale analysis"],"languages":["en"],"rights":["Copyright 2015 Yanhua Sun"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/78431","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kale, Laxmikant V.","Gropp, William D.","Kloeckner, Andreas","Balaji, Pavan"]},{"key":"dc:creator","label":"Author","values":["Sun, Yanhua"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-07-22T22:17:12Z","2015-05","2015-04-23","2015-5"]},{"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":["parallel computing","control points","runtime system","application reconfiguration","automatic performance analysis","large scale analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Yanhua Sun"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/78431"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Parallel programming has always been difficult due to the complexity of hardware and the diversity of applications. Although significant progress has been achieved over the years, attaining high parallel efficiency on large supercomputers for various applications is still quite challenging. As we go beyond the current scale of computers to those with peak capacities of an ExaFLOP/s, it is clear that an introspective and adaptive runtime system (RTS) will be critical to reduce programmers' tuning efforts by automatically handling the complexities of applications and machines. This is the motivation for my research on a Performance-analysis-based Introspective Control System - PICS. PICS intelligently steers parallel applications and runtime system configurations to achieve desired goals by utilizing expert knowledge to analyze performance data and adaptively reconfiguring applications. This thesis designs a holistic introspective control system for automatic performance tuning that combines the real-time performance analysis and performance steering to effectively automate the optimization. A few techniques are explored to make the parallel runtime system and applications more adaptive and controllable. Control points are defined for applications to interact with PICS. Decision tree based automatic performance analysis is implemented to significantly reduce the search space of multiple configurations. Parallel evaluation and sampling techniques are exploited to reduce the overhead of the system and to improve its scalability. In addition, the result of automatic performance analysis can be visualized to help developers manually tune their applications. The utility of PICS is demonstrated with several benchmarks and real- world applications.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-07-22 without embargo terms","The student, Yanhua Sun, accepted the attached license on 2015-04-21 at 16:43.","The student, Yanhua Sun, submitted this Dissertation for approval on 2015-04-21 at 16:49.","This Dissertation was approved for publication on 2015-04-23 at 07:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #7966 on 2015-07-22 at 10:32:59","Made available in DSpace on 2015-07-22T22:17:12Z (GMT). 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As we go beyond the current scale of computers to those with peak capacities of an ExaFLOP/s, it is clear that an introspective and adaptive runtime system (RTS) will be critical to reduce programmers' tuning efforts by automatically handling the complexities of applications and machines. This is the motivation for my research on a Performance-analysis-based Introspective Control System - PICS. PICS intelligently steers parallel applications and runtime system configurations to achieve desired goals by utilizing expert knowledge to analyze performance data and adaptively reconfiguring applications. This thesis designs a holistic introspective control system for automatic performance tuning that combines the real-time performance analysis and performance steering to effectively automate the optimization. A few techniques are explored to make the parallel runtime system and applications more adaptive and controllable. Control points are defined for applications to interact with PICS. Decision tree based automatic performance analysis is implemented to significantly reduce the search space of multiple configurations. Parallel evaluation and sampling techniques are exploited to reduce the overhead of the system and to improve its scalability. In addition, the result of automatic performance analysis can be visualized to help developers manually tune their applications. The utility of PICS is demonstrated with several benchmarks and real- world applications.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-07-22 without embargo terms","The student, Yanhua Sun, accepted the attached license on 2015-04-21 at 16:43.","The student, Yanhua Sun, submitted this Dissertation for approval on 2015-04-21 at 16:49.","This Dissertation was approved for publication on 2015-04-23 at 07:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #7966 on 2015-07-22 at 10:32:59","Made available in DSpace on 2015-07-22T22:17:12Z (GMT). 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