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dc.contributor.authorDing, Yufei
dc.contributor.authorAnsel, Jason
dc.contributor.authorVeeramachaneni, Kalyan
dc.contributor.authorShen, Xipeng
dc.contributor.authorO'Reilly, Una-May
dc.contributor.authorAmarasinghe, Saman P.
dc.date.accessioned2015-11-04T17:53:23Z
dc.date.available2015-11-04T17:53:23Z
dc.date.issued2015-06
dc.identifier.isbn9781450334686
dc.identifier.urihttp://hdl.handle.net/1721.1/99720
dc.description.abstractA daunting challenge faced by program performance autotuning is input sensitivity, where the best autotuned configuration may vary with different input sets. This paper presents a novel two-level input learning algorithm to tackle the challenge for an important class of autotuning problems, algorithmic autotuning. The new approach uses a two-level input clustering method to automatically refine input grouping, feature selection, and classifier construction. Its design solves a series of open issues that are particularly essential to algorithmic autotuning, including the enormous optimization space, complex influence by deep input features, high cost in feature extraction, and variable accuracy of algorithmic choices. Experimental results show that the new solution yields up to a 3x speedup over using a single configuration for all inputs, and a 34x speedup over a traditional one-level method for addressing input sensitivity in program optimizations.en_US
dc.description.sponsorshipUnited States. Dept. of Energy (Award DE-SC0005288)en_US
dc.description.sponsorshipUnited States. Dept. of Energy (Award DE-SC0008923)en_US
dc.description.sponsorshipUnited States. Dept. of Energy (Early Career Award)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant 1464216)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant 1320796)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (CAREER Award)en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/2737924.2737969en_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.titleAutotuning algorithmic choice for input sensitivityen_US
dc.typeArticleen_US
dc.identifier.citationYufei Ding, Jason Ansel, Kalyan Veeramachaneni, Xipeng Shen, Una-May O’Reilly, and Saman Amarasinghe. 2015. Autotuning algorithmic choice for input sensitivity. In Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2015). ACM, New York, NY, USA, 379-390.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorAnsel, Jasonen_US
dc.contributor.mitauthorVeeramachaneni, Kalyanen_US
dc.contributor.mitauthorO'Reilly, Una-Mayen_US
dc.contributor.mitauthorAmarasinghe, Saman P.en_US
dc.relation.journalProceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2015)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsDing, Yufei; Ansel, Jason; Veeramachaneni, Kalyan; Shen, Xipeng; O’Reilly, Una-May; Amarasinghe, Samanen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-7231-7643
mit.licenseOPEN_ACCESS_POLICYen_US


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