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dc.contributor.authordel Río Astorga, David
dc.contributor.authorDolz, Manuel F.
dc.contributor.authorSánchez García, Luis Miguel
dc.contributor.authorFernández Muñoz, Javier
dc.contributor.authorGarcía, J. Daniel
dc.date.accessioned2019-06-27T10:41:57Z
dc.date.available2019-06-27T10:41:57Z
dc.date.issued2017-03
dc.identifier.citationdel Rio Astorga, D., Dolz, M. F., Sánchez, L. M., Fernández, J., & García, J. D. (2018). An adaptive offline implementation selector for heterogeneous parallel platforms. The International Journal of High Performance Computing Applications, 32(6), 854–863. https://doi.org/10.1177/1094342017698746ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/182997
dc.description.abstractHeterogeneous parallel platforms, comprising multiple processing units and architectures, have become a cornerstone in improving the overall performance and energy efficiency of scientific and engineering applications. Nevertheless, taking full advantage of their resources comes along with a variety of difficulties: developers require technical expertise in using different parallel programming frameworks and previous knowledge about the algorithms used underneath by the application. To alleviate this burden, we present an adaptive offline implementation selector that allows users to better exploit resources provided by heterogeneous platforms. Specifically, this framework selects, at compile time, the tuple device-implementation that delivers the best performance on a given platform. The user interface of the framework leverages two C++ language features: attributes and concepts. To evaluate the benefits of this framework, we analyse the global performance and convergence of the selector using two different use cases. The experimental results demonstrate that the proposed framework allows users enhancing performance while minimizing efforts to tune applications targeted to heterogeneous platforms. Furthermore, we also demonstrate that our framework delivers comparable performance figures with respect to other approaches.ca_CA
dc.format.extent9 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherUniversidad de Salamancaca_CA
dc.rightsCopyright © The Author(s) 2017ca_CA
dc.subjectimplementation selectorca_CA
dc.subjectheterogeneous platformsca_CA
dc.subjectauto-tuningca_CA
dc.subjectC++ attributesca_CA
dc.subjectC++ conceptsca_CA
dc.titleAn adaptive offline implementation selector for heterogeneous parallel platformsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1177/1094342017698746
dc.relation.projectIDSpanish Ministerio de Economía y Competitividad (grant TIN2016-79637-P "Towards Unification of High Performance Computing (HPC) and Big Data Paradigms") ; EU (Projects ICT 644235 "REPHRASE: REfactoring Parallel Heterogeneous Resource-Aware Applications" and FP7 609666 "REPARA: Reengineering and Enabling Performance And poweR of Applications"ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.relation.publisherVersionhttps://journals.sagepub.com/doi/full/10.1177/1094342017698746ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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