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dc.contributor.author
D'amato, Juan Pablo
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dc.contributor.author
Venere, Marcelo
dc.date.available
2016-08-05T19:51:04Z
dc.date.issued
2013-03
dc.identifier.citation
D'amato, Juan Pablo; Venere, Marcelo; A CPU–GPU framework for optimizing the quality of large meshes; Elsevier; Journal Of Parallel And Distributed Computing; 73; 8; 3-2013; 1127-1134
dc.identifier.issn
0743-7315
dc.identifier.uri
http://hdl.handle.net/11336/6967
dc.description.abstract
The 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.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Parallelism
dc.subject
Re-Meshing
dc.subject
Quality
dc.subject
Gpu
dc.subject.classification
Ciencias de la Computación
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
A CPU–GPU framework for optimizing the quality of large meshes
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-08-04T17:23:21Z
dc.journal.volume
73
dc.journal.number
8
dc.journal.pagination
1127-1134
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: 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.fil
Fil: 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.journal.title
Journal Of Parallel And Distributed Computing
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.jpdc.2013.03.007
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jpdc.2013.03.007
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0743731513000518
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