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dc.contributor.authorCecka, Cris
dc.contributor.authorLew, Adrian J.
dc.contributor.authorDarve, E.
dc.date.accessioned2016-02-25T12:42:57Z
dc.date.available2016-02-25T12:42:57Z
dc.date.issued2010-08-23
dc.identifier.citationCecka C, Lew AJ, Darve E (2010) Assembly of finite element methods on graphics processors. Int J Numer Meth Engng 85: 640–669. Available: http://dx.doi.org/10.1002/nme.2989.
dc.identifier.issn0029-5981
dc.identifier.doi10.1002/nme.2989
dc.identifier.urihttp://hdl.handle.net/10754/597607
dc.description.abstractRecently, graphics processing units (GPUs) have had great success in accelerating many numerical computations. We present their application to computations on unstructured meshes such as those in finite element methods. Multiple approaches in assembling and solving sparse linear systems with NVIDIA GPUs and the Compute Unified Device Architecture (CUDA) are created and analyzed. Multiple strategies for efficient use of global, shared, and local memory, methods to achieve memory coalescing, and optimal choice of parameters are introduced. We find that with appropriate preprocessing and arrangement of support data, the GPU coprocessor using single-precision arithmetic achieves speedups of 30 or more in comparison to a well optimized double-precision single core implementation. We also find that the optimal assembly strategy depends on the order of polynomials used in the finite element discretization. © 2010 John Wiley & Sons, Ltd.
dc.description.sponsorshipThis work was partially supported by a research grant from the Academic Excellence Alliance program between King Abdullah University of Science and Technology (KAUST) and the Stanford University.
dc.publisherWiley-Blackwell
dc.subjectCUDA
dc.subjectFEM
dc.subjectFinite element method
dc.subjectGPGPU
dc.subjectGPU
dc.subjectHigh-performance computing
dc.subjectMulticore
dc.titleAssembly of finite element methods on graphics processors
dc.typeArticle
dc.identifier.journalInternational Journal for Numerical Methods in Engineering
dc.contributor.institutionStanford University, Palo Alto, United States
kaust.grant.programAcademic Excellence Alliance (AEA)


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