001     874426
005     20210130004655.0
024 7 _ |a 2128/24533
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037 _ _ |a FZJ-2020-01436
041 _ _ |a English
100 1 _ |a Huber, Markus
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
111 2 _ |a NIC Symposium 2020
|c Jülich
|d 2020-02-27 - 2020-02-28
|w Germany
245 _ _ |a Massively Parallel Multigrid with Direct Coarse Grid Solvers
260 _ _ |a Jülich
|c 2020
|b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
295 1 0 |a NIC Symposium 2020
300 _ _ |a 335 - 344
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a Contribution to a conference proceedings
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336 7 _ |a Contribution to a book
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490 0 _ |a Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series
|v 50
520 _ _ |a Multigrid methods play an important role in the numerical approximation of partial differential equations. As long as only a moderate number of processors is used, many alternatives can be used as solver for the coarsest grid. However, when the number of processors increases, then standard coarsening will stop while the problem is still large and the communication overhead for solving the corresponding coarsest grid problem may dominate. In this case, the coarsest grid must be agglomerated to only a subset of the processors. This article studies the use of sparse direct methods for solving the coarsest grid problem as it arises in a multigrid hierarchy. We use as test case a Stokes-type model and solve algebraic saddle point systems with up to O(10$^{11}$) degrees of freedom on a current peta-scale supercomputer. We compare the sparse direct solver with a preconditioned minimal residual iteration and show that the sparse direct method can exhibit better parallel efficiency.
536 _ _ |a 899 - ohne Topic (POF3-899)
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700 1 _ |a Kohl, Nils
|0 P:(DE-HGF)0
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700 1 _ |a Leleux, Philippe
|0 P:(DE-HGF)0
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700 1 _ |a Rüde, Ulrich
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Thönnes, Dominik
|0 P:(DE-HGF)0
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700 1 _ |a Wohlmuth, Barbara
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787 0 _ |i IsPartOf
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856 4 _ |y OpenAccess
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856 4 _ |y OpenAccess
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910 1 _ |a Technische Universität München
|0 I:(DE-588b)36241-4
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910 1 _ |a Friedrich-Alexander Universität
|0 I:(DE-HGF)0
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910 1 _ |a CERFACS
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910 1 _ |a Friedrich-Alexander Universität
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910 1 _ |a CERFACS
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910 1 _ |a Friedrich-Alexander Universität
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910 1 _ |a Technische Universität München
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913 1 _ |a DE-HGF
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914 1 _ |y 2020
915 _ _ |a OpenAccess
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915 _ _ |a Creative Commons Attribution CC BY 4.0
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920 1 _ |0 I:(DE-Juel1)NIC-20090406
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