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dc.contributor.authorLázaro Muñoz, Antonio José
dc.contributor.authorLópez Albelda, Bernabé
dc.contributor.authorGonzalez-Linares, Jose Maria 
dc.contributor.authorGuil-Mata, Nicolas 
dc.date.accessioned2018-07-16T11:34:20Z
dc.date.available2018-07-16T11:34:20Z
dc.date.created2018-07-13
dc.date.issued2018-07-16
dc.identifier.urihttps://hdl.handle.net/10630/16279
dc.description.abstractConcurrent execution of tasks in GPUs can reduce the computation time of a workload by overlapping data transfer and execution commands. However it is difficult to implement an efficient run- time scheduler that minimizes the workload makespan as many execution orderings should be evaluated. In this paper, we employ scheduling theory to build a model that takes into account the device capabili- ties, workload characteristics, constraints and objec- tive functions. In our model, GPU tasks schedul- ing is reformulated as a flow shop scheduling prob- lem, which allow us to apply and compare well known methods already developed in the operations research field. In addition we develop a new heuristic, specif- ically focused on executing GPU commands, that achieves better scheduling results than previous tech- niques. Finally, a comprehensive evaluation, showing the suitability and robustness of this new approach, is conducted in three different NVIDIA architectures (Kepler, Maxwell and Pascal).en_US
dc.description.sponsorshipProyecto TIN2016- 0920R, Universidad de Málaga (Campus de Excelencia Internacional Andalucía Tech) y programa de donación de NVIDIA Corporation.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectArquitectura de redes informáticas - Congresosen_US
dc.subject.otherGPUen_US
dc.subject.otherTask reorderingen_US
dc.titleA scheduling theory framework for GPU tasks efficient executionen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitleJornadas Sartecoen_US
dc.relation.eventplaceTeruelen_US
dc.relation.eventdate12-14 de Septiembre de 2018en_US
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*


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