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A CPU–GPU framework for optimizing the quality of large meshes

cnea.tipodocumentoARTÍCULO CIENTÍFICO
dc.contributor.authorD'amato, Juan Pablo
dc.contributor.authorVenere, Marcelo
dc.date.accessioned2025-12-11T23:08:15Z
dc.date.available2025-12-11T23:08:15Z
dc.date.issued2013-03
dc.description.abstractThe automatic generation of 3D finite element meshes (FEM) is still a bottle neck for the simulation of large fluid-dynamic problems. Although today there are several algorithms that can generate good meshes without user intervention, in cases where the geometry changes during the calculation and thousands of meshes must be constructed, the computational cost of this process can exceed the cost of the FEM. There has been a lot of work in FEM parallelization and the algorithms work well in different parallel architectures, but at present there has not been much success in the parallelization of mesh generation methods. This paper will present a massive parallelization scheme for re-meshing with tetrahedral elements using the local modification algorithm. This method is frequently used to improve the quality of elements once the mesh has been generated, but we will show it can also be applied as a re-generation process, starting with the distorted and invalid mesh of the previous step. The parallelization is carried out using OpenCL and OpenMP in order to test the method in multiple CPU architecture and also in Graphic Processors (GPU). Finally we present the speedup and quality results obtained in meshes with hundreds of thousands of elements and different parallel APIs.
dc.description.institutionalaffiliationFil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil; Argentina
dc.description.institutionalaffiliationFil: Venere, Marcelo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina. Comision Nacional de Energia Atomica. Gerencia Quimica. CAC; Argentina
dc.identifier.issn0743-7315
dc.identifier.urihttps://nuclea.cnea.gob.ar/handle/20.500.12553/7625
dc.publisherElsevier
dc.relationinfo:eu-repo/semantics/reference/hdl/11336/6967
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.jpdc.2013.03.007
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jpdc.2013.03.007
dc.relationinfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0743731513000518
dc.rights.licenseinfo:eu-repo/semantics/openAccess
dc.subjectParallelism
dc.subjectRe-Meshing
dc.subjectQuality
dc.subjectGpu
dc.subjectCiencias de la Computación
dc.subjectCiencias de la Computación e Información
dc.subjectCIENCIAS NATURALES Y EXACTAS
dc.titleA CPU–GPU framework for optimizing the quality of large meshes
dc.typeARTÍCULO
dc.type.versionVersión publicada

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