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A Comparative study and evaluation of parallel programming models for shared-memory parallel architectures

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.contributor.authorSánchez García, Luis Miguel
dc.contributor.authorFernández Muñoz, Javier
dc.contributor.authorSotomayor Fernández, Rafael
dc.contributor.authorEscolar Díaz, María Soledad
dc.contributor.authorGarcía Sánchez, José Daniel
dc.date.accessioned2017-11-07T12:37:24Z
dc.date.available2017-11-07T12:37:24Z
dc.date.issued2013-07
dc.description.abstractNowadays, shared-memory parallel architectures have evolved and new programming frameworks have appeared that exploit these architectures: OpenMP, TBB, Cilk Plus, ArBB and OpenCL. This article focuses on the most extended of these frameworks in commercial and scientific areas. This paper shows a comparative study of these frameworks and an evaluation. The study covers several capacities, such as task deployment, scheduling techniques, or programming language abstractions. The evaluation measures three dimensions: code development complexity, performance and efficiency, measure as speedup per watt. For this evaluation, several parallel benchmarks have been implemented with each framework. These benchmarks are created to cover certain scenarios, like regular memory access or irregular computation. The conclusions show some highlights, like the fact that some frameworks (OpenMP, Cilk Plus) are better for transforming quickly a sequential code, others (TBB) have a small footprint which is ideal for small problems, and others (OpenCL) are suited for heterogeneous architectures but they require a very complex development process. The conclusions also show that the vectorization support is more critical than multitasking to achieve efficiency for those problems where this approach fits.en
dc.description.sponsorshipThis work has been partially funded by the project “Input/Output Scalable Techniques for distributed and high-performance computing environments” of MINISTERIO DE CIENCIA E INNOVACIÓN, TIN2010-16497. The work of J. Daniel García has been funded by "FUNDACIÓN CAJAMADRID" through a grant for Mobility of Madrid Public Universities Professors.en
dc.format.extent23es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationNew generation computing (2013). 31(3), pp. 139-161en
dc.identifier.doihttps://doi.org/10.1007/s00354-013-0301-5
dc.identifier.issn0288-3635
dc.identifier.publicationfirstpage139es
dc.identifier.publicationissue3es
dc.identifier.publicationlastpage161es
dc.identifier.publicationtitleNew generation computingen
dc.identifier.publicationvolume31es
dc.identifier.urihttps://hdl.handle.net/10016/25767
dc.identifier.uxxiAR/0000018791
dc.language.isoengen
dc.publisherOhmsha Ltd. and Springer Japanen
dc.relation.projectIDGobierno de España. TIN2010-16497es
dc.rights© Ohmsha and Springer Japan 2013en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherParallel Programmingen
dc.subject.otherVector Instructionsen
dc.subject.otherMultithreadingen
dc.subject.otherPerformance Analysisen
dc.subject.otherEfficiency Analysisen
dc.subject.otherPower Consumptionen
dc.titleA Comparative study and evaluation of parallel programming models for shared-memory parallel architecturesen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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