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dc.contributor.authorLópez Albelda, Bernabé
dc.contributor.authorCastro, Francisco M.
dc.contributor.authorGonzalez-Linares, Jose Maria 
dc.contributor.authorGuil-Mata, Nicolas 
dc.date.accessioned2022-09-07T12:14:47Z
dc.date.available2022-09-07T12:14:47Z
dc.date.created2022-09
dc.date.issued2022-08
dc.identifier.urihttps://hdl.handle.net/10630/24921
dc.description.abstractCurrent execution of kernels on GPUs allows improving the use of hardware resources and reducing the execution time of co-executed kernels. In addition, efficient kernel-oriented scheduling policies pursuing criteria based on fairness or Quality of Service can be implemented. However, achieved co-executing performance strongly depends on how GPU resources are partitioned between kernels. Thus, precise slowdown models that predict accurate co-execution performance must be used to fulfill scheduling policy requirements. Most recent slowdown models work with Spatial Multitask (SMT) partitioning, where Stream Multiprocessors (SMs) are distributed among tasks. In this work, we show that Simultaneous Multikernel (SMK) partitioning, where kernels share the SMs, obtains better performance. However, kernel interference in SMK occurs not only in global memory, as in the SMT case, but also within the SM, leading to high prediction errors. Here, we propose a modification of a previous state-of-the-art slowdown model to reduce median prediction error from 27.92% to 9.50%. Moreover, this new slowdown model is used to implement a scheduling policy that improves fairness by 1.41x on average compared to even partitioning, whereas previous models reach only 1.21x on average.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech P18-FR-3130 UMA20-FEDERJA-059 PID2019-105396RB-I00es_ES
dc.language.isoenges_ES
dc.publisherJosé Cano, Phil Trinderes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectGKS (Sistema de ordenador)es_ES
dc.subjectKernel, Funciones dees_ES
dc.subject.otherConcurrent Kernel Executiones_ES
dc.subject.otherSimultaneous Multikerneles_ES
dc.subject.otherSlowdown modeles_ES
dc.subject.otherFairness schedulinges_ES
dc.titleA Hybrid Piece-Wise Slowdown Model for Concurrent Kernel Execution on GPUes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleEuro-Par 2022es_ES
dc.relation.eventplaceGlasgow, Reino Unidoes_ES
dc.relation.eventdateAgosto 2022es_ES


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