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Title: Toward Robust Scalable Algebraic Multigrid Solvers.

Abstract

Abstract not provided.

Authors:
; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1406967
Report Number(s):
SAND2016-10963PE
648732
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the UNM Applied Math & Statistics Seminar held October 27, 2016 in Albuquerque, NM.
Country of Publication:
United States
Language:
English

Citation Formats

Tuminaro, Raymond S., Cyr, Eric C, Shadid, John N., Noble, David R., Berger-Vergiat, Luc, Hu, Jonathan J., Prokopenko, Andrey, Siefert, Christopher, Wiesner, Tobias Albert, Perego, Mauro, Salinger, Andrew G., Tezaur, Irina Kalashnikova, and Price, Stephen. Toward Robust Scalable Algebraic Multigrid Solvers.. United States: N. p., 2016. Web.
Tuminaro, Raymond S., Cyr, Eric C, Shadid, John N., Noble, David R., Berger-Vergiat, Luc, Hu, Jonathan J., Prokopenko, Andrey, Siefert, Christopher, Wiesner, Tobias Albert, Perego, Mauro, Salinger, Andrew G., Tezaur, Irina Kalashnikova, & Price, Stephen. Toward Robust Scalable Algebraic Multigrid Solvers.. United States.
Tuminaro, Raymond S., Cyr, Eric C, Shadid, John N., Noble, David R., Berger-Vergiat, Luc, Hu, Jonathan J., Prokopenko, Andrey, Siefert, Christopher, Wiesner, Tobias Albert, Perego, Mauro, Salinger, Andrew G., Tezaur, Irina Kalashnikova, and Price, Stephen. 2016. "Toward Robust Scalable Algebraic Multigrid Solvers.". United States. doi:. https://www.osti.gov/servlets/purl/1406967.
@article{osti_1406967,
title = {Toward Robust Scalable Algebraic Multigrid Solvers.},
author = {Tuminaro, Raymond S. and Cyr, Eric C and Shadid, John N. and Noble, David R. and Berger-Vergiat, Luc and Hu, Jonathan J. and Prokopenko, Andrey and Siefert, Christopher and Wiesner, Tobias Albert and Perego, Mauro and Salinger, Andrew G. and Tezaur, Irina Kalashnikova and Price, Stephen},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month =
}

Conference:
Other availability
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  • This talk highlights some multigrid challenges that arise from several application areas including structural dynamics, fluid flow, and electromagnetics. A general framework is presented to help introduce and understand algebraic multigrid methods based on energy minimization concepts. Connections between algebraic multigrid prolongators and finite element basis functions are made to explored. It is shown how the general algebraic multigrid framework allows one to adapt multigrid ideas to a number of different situations. Examples are given corresponding to linear elasticity and specifically in the solution of linear systems associated with extended finite elements for fracture problems.
  • Abstract not provided.
  • The authors propose a parallel algebraic multilevel algorithm (AMG), which has the novel feature that the subproblem residing in each processor is defined over the entire partition domain, although the vast majority of unknowns for each subproblem are associated with the partition owned by the corresponding processor. This feature ensures that a global coarse description of the problem is contained within each of the subproblems. The advantages of this approach are that interprocessor communication is minimized in the solution process while an optimal order of convergence rate is preserved; and the speed of local subproblem solvers can be maximized usingmore » the best existing sequential algebraic solvers.« less
  • Algebraic Multigrid (AMG) solvers are an essential component of many large-scale scientific simulation codes. Their continued numerical scalability and efficient implementation is critical for preparing these codes for exascale. Our experiences on modern multi-core machines show that significant challenges must be addressed for AMG to perform well on such machines. We discuss our experiences and describe the techniques we have used to overcome scalability challenges for AMG on hybrid architectures in preparation for exascale.