{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/65967"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/65967","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Smart data structures : an online machine learning approach to multicore data structures","abstract":"As multicores become prevalent, the complexity of programming is skyrocketing. One major difficulty is eciently orchestrating collaboration among threads through shared data structures. Unfortunately, choosing and hand-tuning data structure algorithms to get good performance across a variety of machines and inputs is a herculean task to add to the fundamental difficulty of getting a parallel program correct. To help mitigate these complexities, this work develops a new class of parallel data structures called Smart Data Structures that leverage online machine learning to adapt themselves automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate signicant improvements over the best existing algorithms under a variety of conditions. Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available.","abstract_html":"As multicores become prevalent, the complexity of programming is skyrocketing. One major difficulty is eciently orchestrating collaboration among threads through shared data structures. Unfortunately, choosing and hand-tuning data structure algorithms to get good performance across a variety of machines and inputs is a herculean task to add to the fundamental difficulty of getting a parallel program correct. To help mitigate these complexities, this work develops a new class of parallel data structures called Smart Data Structures that leverage online machine learning to adapt themselves automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate signicant improvements over the best existing algorithms under a variety of conditions. Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available.","abstract_has_math":false,"creators":["Eastep, Jonathan M. (Jonathan Michael)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate signicant improvements over the best existing algorithms under a variety of conditions. Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Smart data structures : an online machine learning approach to multicore data structures"]}]}],"canonical_facts":{"dc:contributor.advisor":["Anant Agarwal."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Eastep, Jonathan M. 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To help mitigate these complexities, this work develops a new class of parallel data structures called Smart Data Structures that leverage online machine learning to adapt themselves automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate signicant improvements over the best existing algorithms under a variety of conditions. Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/65967"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc: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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