dc.contributor.author | Ansel, Jason | |
dc.contributor.author | Kamil, Shoaib | |
dc.contributor.author | Veeramachaneni, Kalyan | |
dc.contributor.author | Ragan-Kelley, Jonathan | |
dc.contributor.author | Bosboom, Jeffrey | |
dc.contributor.author | O'Reilly, Una-May | |
dc.contributor.author | Amarasinghe, Saman | |
dc.date.accessioned | 2021-11-04T19:07:20Z | |
dc.date.available | 2021-11-04T19:07:20Z | |
dc.date.issued | 2014-08-24 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/137397 | |
dc.description.abstract | Program 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.iso | en | |
dc.publisher | ACM | en_US |
dc.relation.isversionof | 10.1145/2628071.2628092 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | MIT web domain | en_US |
dc.title | OpenTuner: an extensible framework for program autotuning | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Ansel, Jason, Kamil, Shoaib, Veeramachaneni, Kalyan, Ragan-Kelley, Jonathan, Bosboom, Jeffrey et al. 2014. "OpenTuner: an extensible framework for program autotuning." | |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2019-05-02T17:01:28Z | |
dspace.date.submission | 2019-05-02T17:01:29Z | |
mit.license | OPEN_ACCESS_POLICY | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |