{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/66313"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/66313","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Evolutionary algorithms for compiler-enabled program autotuning","abstract":"PetaBricks [4, 21, 7, 3, 5] is an implicitly parallel programming language which, through the process of autotuning, can automatically optimize programs for fast QoS-aware execution on any hardware. In this thesis we develop and evaluate two PetaBricks autotuners: INCREA and SiblingRivalry. INCREA, based on a novel bottom-up evolutionary algorithm, optimizes programs offline at compile time. SiblingRivalry improves on INCREA by optimizing online during a program's execution, dynamically adapting to changes in hardware and the operating system. Continuous adaptation is achieved through racing, where half of available resources are devoted to always-on learning. We evaluate INCREA and SiblingRivalry on a large number of real-world benchmarks, and show that our autotuners can significantly speed up PetaBricks programs with respect to many non-tuned and mis-tuned baselines. Our results indicate the need for a continuous learning loop that can optimize efficiently by exploiting online knowledge of a program's performance. The results leave open the question of how to solve the online optimization problem on all cores, i.e. without racing.","abstract_html":"PetaBricks [4, 21, 7, 3, 5] is an implicitly parallel programming language which, through the process of autotuning, can automatically optimize programs for fast QoS-aware execution on any hardware. In this thesis we develop and evaluate two PetaBricks autotuners: INCREA and SiblingRivalry. INCREA, based on a novel bottom-up evolutionary algorithm, optimizes programs offline at compile time. SiblingRivalry improves on INCREA by optimizing online during a program's execution, dynamically adapting to changes in hardware and the operating system. Continuous adaptation is achieved through racing, where half of available resources are devoted to always-on learning. We evaluate INCREA and SiblingRivalry on a large number of real-world benchmarks, and show that our autotuners can significantly speed up PetaBricks programs with respect to many non-tuned and mis-tuned baselines. Our results indicate the need for a continuous learning loop that can optimize efficiently by exploiting online knowledge of a program's performance. The results leave open the question of how to solve the online optimization problem on all cores, i.e. without racing.","abstract_has_math":false,"creators":["Pacula, Maciej"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Una-May O'Reilly."],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-22T22:20:46Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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We evaluate INCREA and SiblingRivalry on a large number of real-world benchmarks, and show that our autotuners can significantly speed up PetaBricks programs with respect to many non-tuned and mis-tuned baselines. Our results indicate the need for a continuous learning loop that can optimize efficiently by exploiting online knowledge of a program's performance. The results leave open the question of how to solve the online optimization problem on all cores, i.e. without racing."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Evolutionary algorithms for compiler-enabled program autotuning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Una-May O'Reilly."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. 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