In this paper we study the use of long distance interpolation methods with the low complexity coarsening algorithm PMIS. AMG performance and scalability is compared for classical as well as long distance interpolation methods on parallel computers. It is shown that the increased interpolation accuracy largely restores the scalability of AMG convergence factors for PMIS-coarsened grids, and in combination with complexity reducing methods, such as interpolation truncation, one obtains a class of parallel AMG methods that enjoy excellent scalability properties on large parallel computers
This paper studies AMG (algebraic multigrid) methods that utilize energy minimization construction o...
We introduce a coarsening algorithm for algebraic multigrid (AMG) based on the concept of compatible...
Algebraic multigrid (AMG) is a popular iterative solver and preconditioner for large sparse linear s...
Algebraic multigrid (AMG) is a very efficient iterative solver and preconditioner for large unstruct...
The development of high performance, massively parallel computers and the increasing demands of comp...
With new more aggressive coarsening algorithms that while reducing memory also degrade convergence o...
Solving partial differential equations (PDEs) using analytical techniques is intractable for all but...
The Algebraic Multigrid (AMG) method has over the years developed into an ecient tool for solving un...
A typical approach to decrease computational costs and memory requirements of classical algebraic mu...
The parallel multigrid algorithm of Frederickson and McBryan (1987) is considered. This algorithm us...
Multigrid methods are often the most efficient approaches for solving the very large linear systems...
AbstractThe convergence theory for algebraic multigrid (AMG) algorithms proposed in Chang and Huang ...
The convergence rate of standard multigrid algorithms degenerates on problems with stretched grids o...
Algebraic Multigrid (AMG) is an efficient multigrid method for solving large problems, using only th...
Algebraic multigrid (AMG) solves linear systems based on multigrid principles, but in a way that onl...
This paper studies AMG (algebraic multigrid) methods that utilize energy minimization construction o...
We introduce a coarsening algorithm for algebraic multigrid (AMG) based on the concept of compatible...
Algebraic multigrid (AMG) is a popular iterative solver and preconditioner for large sparse linear s...
Algebraic multigrid (AMG) is a very efficient iterative solver and preconditioner for large unstruct...
The development of high performance, massively parallel computers and the increasing demands of comp...
With new more aggressive coarsening algorithms that while reducing memory also degrade convergence o...
Solving partial differential equations (PDEs) using analytical techniques is intractable for all but...
The Algebraic Multigrid (AMG) method has over the years developed into an ecient tool for solving un...
A typical approach to decrease computational costs and memory requirements of classical algebraic mu...
The parallel multigrid algorithm of Frederickson and McBryan (1987) is considered. This algorithm us...
Multigrid methods are often the most efficient approaches for solving the very large linear systems...
AbstractThe convergence theory for algebraic multigrid (AMG) algorithms proposed in Chang and Huang ...
The convergence rate of standard multigrid algorithms degenerates on problems with stretched grids o...
Algebraic Multigrid (AMG) is an efficient multigrid method for solving large problems, using only th...
Algebraic multigrid (AMG) solves linear systems based on multigrid principles, but in a way that onl...
This paper studies AMG (algebraic multigrid) methods that utilize energy minimization construction o...
We introduce a coarsening algorithm for algebraic multigrid (AMG) based on the concept of compatible...
Algebraic multigrid (AMG) is a popular iterative solver and preconditioner for large sparse linear s...