Adapting a parallel sparse direct solver to architectures with clusters of SMPs
We consider the direct solution of general sparse linear systems baseds on a multifrontal method. The approach combines partial static scheduling of the task dependency graph during the symbolic factorization and distributed dynamic scheduling during the numerical factorization to balance the work a...
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| Vydané v: | Parallel computing Ročník 29; číslo 11; s. 1645 - 1668 |
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| Jazyk: | English |
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Elsevier B.V
01.11.2003
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| ISSN: | 0167-8191, 1872-7336 |
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| Abstract | We consider the direct solution of general sparse linear systems baseds on a multifrontal method. The approach combines partial static scheduling of the task dependency graph during the symbolic factorization and distributed dynamic scheduling during the numerical factorization to balance the work among the processes of a distributed memory computer. We show that to address clusters of Symmetric Multi-Processor (SMP) architectures, and more generally non-uniform memory access multiprocessors, our algorithms for both the static and the dynamic scheduling need to be revisited to take account of the non-uniform cost of communication. The performance analysis on an IBM SP3 with 16 processors per SMP node and up to 128 processors shows that we can significantly reduce both the amount of inter-node communication and the solution time. |
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| AbstractList | We consider the direct solution of general sparse linear systems baseds on a multifrontal method. The approach combines partial static scheduling of the task dependency graph during the symbolic factorization and distributed dynamic scheduling during the numerical factorization to balance the work among the processes of a distributed memory computer. We show that to address clusters of Symmetric Multi-Processor (SMP) architectures, and more generally non-uniform memory access multiprocessors, our algorithms for both the static and the dynamic scheduling need to be revisited to take account of the non-uniform cost of communication. The performance analysis on an IBM SP3 with 16 processors per SMP node and up to 128 processors shows that we can significantly reduce both the amount of inter-node communication and the solution time. |
| Author | Amestoy, Patrick R Duff, Iain S Vömel, Christof Pralet, Stéphane |
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| Cites_doi | 10.1145/77626.77627 10.1016/S0045-7825(99)00242-X 10.1002/(SICI)1096-9128(200002/03)12:2/3<69::AID-CPE472>3.0.CO;2-W 10.1137/0611010 10.1137/S0895479899358194 10.1007/BFb0031609 10.1137/S0895479897319313 10.1016/S0167-8191(01)00141-7 10.1137/0715069 10.1016/0010-4655(96)00017-3 10.1137/0614019 10.1137/S0895479894278952 10.1137/1034004 10.1137/S0895479895291765 10.1007/978-1-4615-5205-5_1 10.1145/504210.504212 10.1137/0905045 10.1145/356044.356047 10.1137/0914074 10.1137/S0895479896302692 |
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| Keywords | Task scheduling Dynamic scheduling MUMPS Distributed memory algorithms Sparse linear systems |
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| Title | Adapting a parallel sparse direct solver to architectures with clusters of SMPs |
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