GPU-based assembly of stiffness matrices in the parallel multilevel partition of unity method

dc.contributor.authorKanis, Sebastiande
dc.date.accessioned2012-08-13de
dc.date.accessioned2016-03-31T07:59:40Z
dc.date.available2012-08-13de
dc.date.available2016-03-31T07:59:40Z
dc.date.issued2012de
dc.description.abstractMany real world problems can be modeled with Partial Differential Equations (PDEs). Since for many PDEs no exact solution can be found, there exists a variety of methods which give an approximate solution to those PDEs. One method which can be applied to find an approximate solution for elliptic PDEs is the Parallel Multilevel Partition of Unity Method (PMPUM). The major computational effort in this method is needed for the discretization of the differential operator. In this work we focus on the applicability of General-purpose computing on graphics processing units (GPGPU) on this task. A GPGPU implementation of the PMPUM is and a comparison to a given CPU implementation is presented. It is shown that the implementation using a GPGPU approach can be applied to many cases arising in the PMPUM to improve the performance.en
dc.identifier.other370783913de
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-75933de
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/2893
dc.identifier.urihttp://dx.doi.org/10.18419/opus-2876
dc.language.isoende
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.subject.ddc004de
dc.titleGPU-based assembly of stiffness matrices in the parallel multilevel partition of unity methoden
dc.typeStudyThesisde
ubs.fakultaetFakultät Informatik, Elektrotechnik und Informationstechnikde
ubs.institutInstitut für Parallele und Verteilte Systemede
ubs.opusid7593de
ubs.publikation.typStudienarbeitde

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