This paper proposes and analyzes a class of multigrid smoothers called the parallel multiplicative (PM) smoother by subspace decomposition techniques. It shows that the well known additive and multiplicative smoothers and the JSOR smoother are special cases of the PM smoother, and their smoothing properties can be obtained directly from the PM analysis. Moreover, numerical results are presented in this paper to show that the JSOR smoother is more robust and effective than the damped Jacobi smoother on current MIMD parallel computers
Many scientific applications require the solution of large and sparse linear systems of equations us...
In modern large-scale supercomputing applications, Algebraic Multigrid (AMG) is a leading choice for...
The multigrid method has been shown to be the most effective general method for solving the multi-di...
This paper surveys the techniques that are necessary for constructing compu-tationally ecient parall...
Abstract. Various forms of sparse approximate inverses (SAI) have been shown to be useful for precon...
Gauss–Seidel is often the smoother of choice within multigrid applications. In the context of unstru...
Efficient solution of partial differential equations require a match between the algorithm and the t...
The multigrid algorithm is a fast and efficient (in fact provably optimal) method for solving a wide...
Smoother is the most important component of parallel multigrid methods, however, the widely used Gau...
The parallel multigrid algorithm of Frederickson and McBryan (1987) is considered. This algorithm us...
Efficient solution of partial differential equations require a match between the algorithm and the t...
The development of high performance, massively parallel computers and the increasing demands of comp...
The Algebraic Multigrid (AMG) method has over the years developed into an ecient tool for solving un...
Sparse approximate inverses ' usefulness in a parallel environment has motivated much interest ...
We analyze the multigrid method accelerated by a minimal residual smoothing (MRS) technique. We prov...
Many scientific applications require the solution of large and sparse linear systems of equations us...
In modern large-scale supercomputing applications, Algebraic Multigrid (AMG) is a leading choice for...
The multigrid method has been shown to be the most effective general method for solving the multi-di...
This paper surveys the techniques that are necessary for constructing compu-tationally ecient parall...
Abstract. Various forms of sparse approximate inverses (SAI) have been shown to be useful for precon...
Gauss–Seidel is often the smoother of choice within multigrid applications. In the context of unstru...
Efficient solution of partial differential equations require a match between the algorithm and the t...
The multigrid algorithm is a fast and efficient (in fact provably optimal) method for solving a wide...
Smoother is the most important component of parallel multigrid methods, however, the widely used Gau...
The parallel multigrid algorithm of Frederickson and McBryan (1987) is considered. This algorithm us...
Efficient solution of partial differential equations require a match between the algorithm and the t...
The development of high performance, massively parallel computers and the increasing demands of comp...
The Algebraic Multigrid (AMG) method has over the years developed into an ecient tool for solving un...
Sparse approximate inverses ' usefulness in a parallel environment has motivated much interest ...
We analyze the multigrid method accelerated by a minimal residual smoothing (MRS) technique. We prov...
Many scientific applications require the solution of large and sparse linear systems of equations us...
In modern large-scale supercomputing applications, Algebraic Multigrid (AMG) is a leading choice for...
The multigrid method has been shown to be the most effective general method for solving the multi-di...