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dc.contributor.authorGoli, Mehdi
dc.contributor.authorMcCall, John
dc.contributor.authorBrown, Christopher Mark
dc.contributor.authorJanjic, Vladimir
dc.contributor.authorHammond, Kevin
dc.date.accessioned2015-03-03T11:31:05Z
dc.date.available2015-03-03T11:31:05Z
dc.date.issued2013-06-20
dc.identifier.citationGoli , M , McCall , J , Brown , C M , Janjic , V & Hammond , K 2013 , Mapping parallel programs to heterogeneous CPU/GPU architectures using a Monte Carlo Tree Search . in 2013 IEEE Congress on Evolutionary Computation, CEC 2013 . IEEE , pp. 2932-2939 . https://doi.org/10.1109/CEC.2013.6557926en
dc.identifier.isbn9781479904532
dc.identifier.isbn9781479904525
dc.identifier.otherPURE: 51070558
dc.identifier.otherPURE UUID: 8ee5d875-a11f-462c-b0f7-672d81284a62
dc.identifier.otherScopus: 84881588847
dc.identifier.otherORCID: /0000-0002-4326-4562/work/33080456
dc.identifier.otherORCID: /0000-0001-6030-2885/work/70619184
dc.identifier.urihttps://hdl.handle.net/10023/6157
dc.description.abstractThe single core processor, which has dominated for over 30 years, is now obsolete with recent trends increasing towards parallel systems, demanding a huge shift in programming techniques and practices. Moreover, we are rapidly moving towards an age where almost all programming will be targeting parallel systems. Parallel hardware is rapidly evolving, with large heterogeneous systems, typically comprising a mixture of CPUs and GPUs, becoming the mainstream. Additionally, with this increasing heterogeneity comes increasing complexity: not only does the programmer have to worry about where and how to express the parallelism, they must also express an efficient mapping of resources to the available system. This generally requires in-depth expert knowledge that most application programmers do not have. In this paper we describe a new technique that derives, automatically, optimal mappings for an application onto a heterogeneous architecture, using a Monte Carlo Tree Search algorithm. Our technique exploits high-level design patterns, targeting a set of well-specified parallel skeletons. We demonstrate that our MCTS on a convolution example obtained speedups that are within 5% of the speedups achieved by a hand-tuned version of the same application.
dc.format.extent8
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartof2013 IEEE Congress on Evolutionary Computation, CEC 2013en
dc.rights© © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectMonte Carlo Tree Searchen
dc.subjectHeuristic algorithmen
dc.subjectStatic mappingen
dc.subjectParallel programmingen
dc.subjectHeterogenous architectureen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccQA75en
dc.titleMapping parallel programs to heterogeneous CPU/GPU architectures using a Monte Carlo Tree Searchen
dc.typeConference itemen
dc.contributor.sponsorEuropean Commissionen
dc.contributor.sponsorEuropean Commissionen
dc.contributor.sponsorEPSRCen
dc.contributor.sponsorEPSRCen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen
dc.identifier.doihttps://doi.org/10.1109/CEC.2013.6557926
dc.identifier.grantnumbern/aen
dc.identifier.grantnumberFP&-ICT-2011-7en
dc.identifier.grantnumberEP/G055181/1en
dc.identifier.grantnumberEP/F030657/1en


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