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dc.contributor.authorPérez-Serrano, Jesús
dc.contributor.authorImbernón, Baldomero
dc.contributor.authorCecilia, José María
dc.contributor.authorUjaldon-Martínez, Manuel 
dc.date.accessioned2024-02-09T08:46:02Z
dc.date.available2024-02-09T08:46:02Z
dc.date.issued2018-09
dc.identifier.citationPérez-Serrano J, Imbernón B, Cecilia JM, Ujaldón M. Energy-based tuning of metaheuristics for molecular docking on multi-GPUs. Concurrency Computat Pract Exper. 2018;30:e4684. https://doi.org/10.1002/cpe.4684es_ES
dc.identifier.urihttps://hdl.handle.net/10630/30237
dc.description.abstractVirtual Screening (VS) methods simulate molecular interactions in silico to look for the best chemical compound that interacts with a given molecular target. VS is becoming increasingly popular to accelerate the drug discovery process and constitute hard optimization problems with a huge computational cost. To deal with these two challenges, we have created METADOCK, an application that (1) enables a wide range of metaheuristics through a parametrized schema and (2) promotes the use of a multi-GPU environment within a heterogeneous cluster. Metaheuristics provide approximate solutions in a reasonable time frame, but, given the stochastic nature of real-life procedures, the energy budget goes hand in hand with acceleration to validate the pro- posed solution. This paper evaluates energy trade-offs and correlations with performance for a set of metaheuristics derived from METADOCK. We establish a solid inference from minimal power to maximal performance in GPUs, and from there, to optimal energy consumption. This way, ideal heuristics can be chosen according not only to best accuracy and performance but also to energy requirements. Our study starts with a preselection of parameterized metaheuristic functions, building blocks where we will find optimal patterns from power criteria while preserving parallelism through a GPU execution. We then establish a methodology to figure out the best instances of the parameterized kernels based on energy patterns obtained, which are analyzed from different viewpoints. We also compare the best workload distributions for optimal performance and power efficiency among Pascal and Maxwell GPUs on popular Titan models. Our experimental results demonstrate that the most power efficient GPU can be overloaded in order to reduce the total amount of energy required by as much as 20%, finding unique scenarios where Maxwell does it better in execution time, but with Pascal always ahead in performance per watt, reaching peaks of up to 40%.es_ES
dc.description.sponsorshipMinistry of Education of Spain, Grant/Award Number: TIN2013-42253-P and TIN2016-78799-P (AEI/FEDER, UE); Junta de Andalucia, Grant/Award Number: P12-TIC-1741; Nvidia, Grant/Award Number: GPU Education Center 2011-2016 and GPU Research Center 2012-2016es_ES
dc.language.isoenges_ES
dc.publisherWileyes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectSíntesis (Química) - Simulación por ordenador - Consumo de energíaes_ES
dc.subject.otherGPUes_ES
dc.subject.otherHeterogeneous computinges_ES
dc.subject.otherHPCes_ES
dc.subject.otherLow-poweres_ES
dc.subject.otherMetaheuristicses_ES
dc.subject.otherMolecular dockinges_ES
dc.titleEnergy-based tuning of metaheuristics for molecular docking on multi-GPUses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doihttps://doi.org/10.1002/cpe.4684
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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