Finite Element Algorithms and Data Structures on Graphical Processing Units
The finite element method (FEM) is one of the most commonly used techniques for the solution of partial differential equations on unstructured meshes. This paper discusses both the assembly and the solution phases of the FEM with special attention to the balance of computation and data movement. We...
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| Vydané v: | International journal of parallel programming Ročník 43; číslo 2; s. 203 - 239 |
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| Jazyk: | English |
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01.04.2015
Springer Nature B.V |
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| ISSN: | 0885-7458, 1573-7640 |
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| Abstract | The finite element method (FEM) is one of the most commonly used techniques for the solution of partial differential equations on unstructured meshes. This paper discusses both the assembly and the solution phases of the FEM with special attention to the balance of computation and data movement. We present a GPU assembly algorithm that scales to arbitrary degree polynomials used as basis functions, at the expense of redundant computations. We show how the storage of the stiffness matrix affects the performance of both the assembly and the solution. We investigate two approaches: global assembly into the CSR and ELLPACK matrix formats and matrix-free algorithms, and show the trade-off between the amount of indexing data and stiffness data. We discuss the performance of different approaches in light of the implicit caches on Fermi GPUs and show a speedup over a two-socket 12-core CPU of up to 10 times in the assembly and up to 6 times in the solution phase. We present our sparse matrix-vector multiplication algorithms that are part of a conjugate gradient iteration and show that a matrix-free approach may be up to two times faster than global assembly approaches and up to 4 times faster than NVIDIA’s cuSPARSE library, depending on the preconditioner used. |
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| AbstractList | The finite element method (FEM) is one of the most commonly used techniques for the solution of partial differential equations on unstructured meshes. This paper discusses both the assembly and the solution phases of the FEM with special attention to the balance of computation and data movement. We present a GPU assembly algorithm that scales to arbitrary degree polynomials used as basis functions, at the expense of redundant computations. We show how the storage of the stiffness matrix affects the performance of both the assembly and the solution. We investigate two approaches: global assembly into the CSR and ELLPACK matrix formats and matrix-free algorithms, and show the trade-off between the amount of indexing data and stiffness data. We discuss the performance of different approaches in light of the implicit caches on Fermi GPUs and show a speedup over a two-socket 12-core CPU of up to 10 times in the assembly and up to 6 times in the solution phase. We present our sparse matrix-vector multiplication algorithms that are part of a conjugate gradient iteration and show that a matrix-free approach may be up to two times faster than global assembly approaches and up to 4 times faster than NVIDIA's cuSPARSE library, depending on the preconditioner used. |
| Author | Reguly, I. Z. Giles, M. B. |
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| Cites_doi | 10.1016/j.jcp.2005.01.005 10.1007/s00450-010-0109-1 10.1145/882262.882364 10.1137/0724090 10.1007/BF01399315 10.1002/nme.2989 10.1016/j.jpdc.2009.01.006 10.1016/j.procs.2010.04.203 10.1016/j.compfluid.2010.08.012 10.1016/j.parco.2011.08.003 10.1016/j.parco.2011.08.001 |
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| SubjectTerms | Algorithms Approximation Assembly Computation Computer architecture Computer Science Finite element analysis Finite element method Indexing Linear algebra Mathematical analysis Mathematical models Methods Partial differential equations Phases Processor Architectures Software Engineering/Programming and Operating Systems Sparsity Studies Theory of Computation |
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