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dc.contributor.authorAktemur, Tankut Barış
dc.contributor.authorKameyama, Y.
dc.contributor.authorKiselyov, O.
dc.contributor.authorShan, C.-C.
dc.date.accessioned2016-02-15T07:33:17Z
dc.date.available2016-02-15T07:33:17Z
dc.date.issued2013
dc.identifier.isbn978-1-4503-1842-6
dc.identifier.urihttp://hdl.handle.net/10679/2172
dc.identifier.urihttp://dl.acm.org/citation.cfm?id=2426917
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractThe appeal of generative programming is "abstraction without guilt": eliminating the vexing trade-off between writing high-level code and highly-performant code. Generative programming also promises to formally capture the domain-specific knowledge and heuristics used by high-performance computing (HPC)experts. How far along are we in fulfilling these promises? To gauge our progress, a recent Shonan Meeting on "bridging the theory of staged programming languages and the practice of high-performance computing" proposed to use a set of benchmarks, dubbed "Shonan Challenge". Shonan Challenge is a collection of crisp problems posed by HPC and domain experts, for which efficient implementations are known but were tedious to write and modify. The challenge is to generate a similar efficient implementation from the high-level specification of a problem, performing the same optimizations, but automatically. It should be easy to adjust optimizations and the specification, maintaining confidence in the generated code. We describe our initial set of benchmarks and provide three solutions to two of the problems. We hope that the Shonan Challenge will clarify the state of the art and stimulate the theory and technology of staging just as the POPLmark challenge did for meta-theory mechanization. Since each Shonan Challenge problem is a kernel of a significant HPC application, each solution has an immediate practical application.
dc.description.sponsorshipTÜBİTAK
dc.language.isoengen_US
dc.publisherACM
dc.relationinfo:turkey/grantAgreement/TUBITAK/110E028
dc.relation.ispartofPEPM 2013: ACM SIGPLAN 2013 Workshop on Partial Evaluation and Program Manipulation
dc.rightsrestrictedAccess
dc.titleShonan challenge for generative programming: short position paperen_US
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID124803
dc.contributor.ozuauthorAktemur, Tankut Barış
dc.identifier.startpage147
dc.identifier.endpage154
dc.identifier.doi10.1145/2426890.2426917
dc.subject.keywordsHigh-performance computing
dc.subject.keywordsGenerative programming
dc.subject.keywordsStaging
dc.subject.keywordsCode generation
dc.subject.keywordsDomain-specific languages
dc.identifier.scopusSCOPUS:2-s2.0-84873436251
dc.contributor.authorMale1


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