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dc.contributor.authorMoretón Fernández, Ana
dc.contributor.authorOrtega Arranz, Héctor 
dc.contributor.authorLlanos Ferraris, Diego Rafael 
dc.date.accessioned2018-03-17T12:11:49Z
dc.date.available2019-01-01T00:40:28Z
dc.date.issued2017
dc.identifier.citationInternational Journal of High Performance Computing Applicationses
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/29124
dc.descriptionProducción Científicaes
dc.description.abstractNowadays the use of hardware accelerators, such as the graphics processing units or XeonPhi coprocessors, is key in solving computationally costly problems that require high performance computing. However, programming solutions for an efficient deployment for these kind of devices is a very complex task that relies on the manual management of memory transfers and configuration parameters. The programmer has to carry out a deep study of the particular data that needs to be computed at each moment, across different computing platforms, also considering architectural details. We introduce the controller concept as an abstract entity that allows the programmer to easily manage the communications and kernel launching details on hardware accelerators in a transparent way. This model also provides the possibility of defining and launching central processing unit kernels in multi-core processors with the same abstraction and methodology used for the accelerators. It internally combines different native programming models and technologies to exploit the potential of each kind of device. Additionally, the model also allows the programmer to simplify the proper selection of values for several configuration parameters that can be selected when a kernel is launched. This is done through a qualitative characterization process of the kernel code to be executed. Finally, we present the implementation of the controller model in a prototype library, together with its application in several case studies. Its use has led to reductions in the development and porting costs, with significantly low overheads in the execution times when compared to manually programmed and optimized solutions which directly use CUDA and OpenMP.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherSAGEes
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleControllers: an abstraction to ease the use of hardware acceleratorses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holderSAGEes
dc.identifier.doi10.1177/1094342017702962es
dc.relation.publisherversionhttp://journals.sagepub.com/doi/abs/10.1177/1094342017702962es
dc.peerreviewedSIes
dc.description.embargo2019-01-01es
dc.description.projectMICINN (Spain) and ERDF program of the European Union: HomProg-HetSys project (TIN2014-58876-P), and COST Program Action IC1305: Network for Sustainable Ultrascale Computing (NESUS).es
dc.rightsAttribution 4.0 International


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