{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/23810"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/23810","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Performance analysis of object-based and message-driven programs","abstract":"The significant gap between peak and realized performance of parallel machines motivates the need for performance analysis. Most existing performance analysis tools provide generic measurement and displays. It is the responsibility of the users to analyze the performance of their programs using the displayed information. This is a non-trivial task, because not only does one need to identify the information that is needed for such analysis, sometimes that information may not even be displayed by the tool. The task of analysis is even more difficult for massively parallel machines, where voluminous amounts of information can be generated. Therefore, a good performance analysis tool should be able to provide intelligent analysis about the performance of a parallel program. Such automatic performance analysis is feasible for programming paradigms that provide the system sufficient information about the behavior of its programs. We have built a framework for automatic analysis for one such paradigm called Charm, a portable, object-based, and message-driven parallel programming language. In this thesis, we describe the process of design and implementation of this framework, and show its utility with sample case studies.","abstract_html":"The significant gap between peak and realized performance of parallel machines motivates the need for performance analysis. Most existing performance analysis tools provide generic measurement and displays. It is the responsibility of the users to analyze the performance of their programs using the displayed information. This is a non-trivial task, because not only does one need to identify the information that is needed for such analysis, sometimes that information may not even be displayed by the tool. The task of analysis is even more difficult for massively parallel machines, where voluminous amounts of information can be generated. Therefore, a good performance analysis tool should be able to provide intelligent analysis about the performance of a parallel program. Such automatic performance analysis is feasible for programming paradigms that provide the system sufficient information about the behavior of its programs. We have built a framework for automatic analysis for one such paradigm called Charm, a portable, object-based, and message-driven parallel programming language. In this thesis, we describe the process of design and implementation of this framework, and show its utility with sample case studies.","abstract_has_math":false,"creators":["Sinha, Amitabh Bhuvangyan"],"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."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T14:27:56Z","date_published":"2011-05-07T14:27:56Z","updated_at":"2026-07-22T22:25:22Z","subjects":["Computer Science"],"languages":["eng"],"rights":["Copyright 1995 Sinha, Amitabh Bhuvangyan"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9522177","(UMI)AAI9522177"],"render_values":[{"text":"AAI9522177","href":null,"code":true},{"text":"(UMI)AAI9522177","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/23810","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kale, Laxmikant V."]},{"key":"dc:creator","label":"Author","values":["Sinha, Amitabh Bhuvangyan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T14:27:56Z","10000-01-01","1995"]},{"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"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1995 Sinha, Amitabh Bhuvangyan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9522177","(UMI)AAI9522177","http://hdl.handle.net/2142/23810"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The significant gap between peak and realized performance of parallel machines motivates the need for performance analysis. 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Most existing performance analysis tools provide generic measurement and displays. It is the responsibility of the users to analyze the performance of their programs using the displayed information. This is a non-trivial task, because not only does one need to identify the information that is needed for such analysis, sometimes that information may not even be displayed by the tool. The task of analysis is even more difficult for massively parallel machines, where voluminous amounts of information can be generated. Therefore, a good performance analysis tool should be able to provide intelligent analysis about the performance of a parallel program. Such automatic performance analysis is feasible for programming paradigms that provide the system sufficient information about the behavior of its programs. We have built a framework for automatic analysis for one such paradigm called Charm, a portable, object-based, and message-driven parallel programming language. 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