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dc.contributor.authorMartínez Frutos, Jesús 
dc.contributor.authorMartínez Castejón, Pedro Jesús 
dc.contributor.authorHerrero Pérez, David 
dc.date.accessioned2018-09-06T10:37:00Z
dc.date.available2018-09-06T10:37:00Z
dc.date.issued2017
dc.identifier.citationMARTÍNEZ FRUTOS, Jesús, MARTÍNEZ CASTEJÓN, Pedro Jesús, HERRERO PÉREZ, David. Efficient topology optimization using GPU computing with multilevel granularity. En: Advances in engineering software, vol. 06, p.47-62, April 2017. ISSN: 0965-9978es_ES
dc.identifier.issn0965-9978
dc.description.abstractThis paper proposes a well-suited strategy for High Performance Computing (HPC) of density-based topology optimization using Graphics Processing Units (GPUs). Such a strategy takes advantage of Massively Parallel Processing (MPP) architectures to overcome the computationally demanding procedures of density-based topology design, both in terms of memory consumption and processing time. This is done exploiting data locality and minimizing both memory consumption and data transfers. The proposed GPU instance makes use of different granularities for the topology optimization pipeline, which are selected to properly balance the workload between the threads exploiting the parallelization potential of massive parallel architectures. The performance of the fine-grained GPU instance of the solving stage is evaluated using two preconditioning techniques. The proposal is also compared with the classical CPU implementation for diverse topology optimization problems, including stiffness maximization, heat sink design and compliant mechanism design.es_ES
dc.description.sponsorshipWe gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the Tesla K40 GPU used for this research. This work has been supported by the Ministry of Economy and Competitiveness of the Government of Spain under the contract DPI2016-77538-R and by the “Fundación Séneca – Agencia de Ciencia y Tecnología de la Región de Murcia” under the contract 19274/PI/14.es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherElsevier Sciencees_ES
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0965997816302332es_ES
dc.rightsAtribución 3.0 España*
dc.rights© Copyright 2017 Elsevier Sciencees_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.titleEfficient topology optimization using GPU computing with multilevel granularityes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.otherMecánica de Medios Continuos y Teoría de Estructurases_ES
dc.subjectGPU computinges_ES
dc.subjectTopology optimizationes_ES
dc.subjectCompliancees_ES
dc.subjectCompliant mechanismes_ES
dc.subjectHeat conductiones_ES
dc.identifier.urihttp://hdl.handle.net/10317/7200
dc.peerreviewSies_ES
dc.contributor.investgroupMecánica computacional y computación científica (D005-04)es_ES
dc.identifier.doi10.1016/j.advengsoft.2017.01.009
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0965997816302332
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES
dc.relation.projectIDDPI2016- 77538-Res_ES
dc.relation.projectID19274/PI/14es_ES
dc.subject.unesco1210 Topologíaes_ES
dc.contributor.funderFundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia (19274/ PI/14)es_ES
dc.contributor.funderAEI/FEDER y UE (DPI2016-77538-R)es_ES


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