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dc.contributor.authorAnsel, Jason
dc.contributor.authorKamil, Shoaib
dc.contributor.authorVeeramachaneni, Kalyan
dc.contributor.authorRagan-Kelley, Jonathan
dc.contributor.authorBosboom, Jeffrey
dc.contributor.authorO'Reilly, Una-May
dc.contributor.authorAmarasinghe, Saman
dc.date.accessioned2021-11-04T19:07:20Z
dc.date.available2021-11-04T19:07:20Z
dc.date.issued2014-08-24
dc.identifier.urihttps://hdl.handle.net/1721.1/137397
dc.description.abstractProgram autotuning has been shown to achieve better or more portable performance in a number of domains. However, autotuners themselves are rarely portable between projects, for a number of reasons: using a domain-informed search space representation is critical to achieving good results; search spaces can be intractably large and require advanced machine learning techniques; and the landscape of search spaces can vary greatly between different problems, sometimes requiring domain specific search techniques to explore efficiently. This paper introduces OpenTuner, a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests. We demonstrate the efficacy and generality of OpenTuner by building autotuners for 7 distinct projects and 16 total benchmarks, showing speedups over prior techniques of these projects of up to 2.8x with little programmer effort. © 2014 ACM.en_US
dc.language.isoen
dc.publisherACMen_US
dc.relation.isversionof10.1145/2628071.2628092en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleOpenTuner: an extensible framework for program autotuningen_US
dc.typeArticleen_US
dc.identifier.citationAnsel, Jason, Kamil, Shoaib, Veeramachaneni, Kalyan, Ragan-Kelley, Jonathan, Bosboom, Jeffrey et al. 2014. "OpenTuner: an extensible framework for program autotuning."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-05-02T17:01:28Z
dspace.date.submission2019-05-02T17:01:29Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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