The popularity of GPGPUs in high performance platforms for scientific computing in recent times has renewed interest in approximate inverse preconditioners for Krylov methods. We have recently introduced some new algorithmic variants [6] of popular approximate inverse methods. We now report on the behaviour of these variations in high performance multilevel preconditioning frameworks, and we present the software framework that enable
Many scientific applications require the solution of large and sparse linear systems of equations us...
Abstract. We investigate the use of sparse approximate-inverse preconditioners for the iterative sol...
Inexact (variable) preconditioning of Multilevel Krylov methods (MK methods) for the solution of lin...
The popularity of GPGPUs in high performance platforms for scientific computing in recent times has...
Simulation with models based on partial differential equations often requires the solution of (seque...
The efficient parallel solution to large sparse linear systems of equations Ax = b is a central issu...
This paper describes and tests a parallel implementation of a factorized approximate inverse precond...
The class of preconditioning that approximates the inverse of the matrix A is studied in the thesis....
A number of recently proposed preconditioning techniques based on sparse approximate inverses are co...
A sparse approximate inverse technique is introduced to solve general sparse linear systems. The spa...
We introduce a novel strategy for parallel preconditioning of large-scale linear systems by means of...
We present the results of numerical experiments aimed at comparing two recently proposed sparse appr...
Neste trabalho de dissertação apresentaremos uma classe de precondicionadores baseados na aproximaçã...
In a large number of scientific applications, the solution of sparse linear systems is the stage tha...
Many scientific applications require the solution of large and sparse linear systems of equations us...
Abstract. We investigate the use of sparse approximate-inverse preconditioners for the iterative sol...
Inexact (variable) preconditioning of Multilevel Krylov methods (MK methods) for the solution of lin...
The popularity of GPGPUs in high performance platforms for scientific computing in recent times has...
Simulation with models based on partial differential equations often requires the solution of (seque...
The efficient parallel solution to large sparse linear systems of equations Ax = b is a central issu...
This paper describes and tests a parallel implementation of a factorized approximate inverse precond...
The class of preconditioning that approximates the inverse of the matrix A is studied in the thesis....
A number of recently proposed preconditioning techniques based on sparse approximate inverses are co...
A sparse approximate inverse technique is introduced to solve general sparse linear systems. The spa...
We introduce a novel strategy for parallel preconditioning of large-scale linear systems by means of...
We present the results of numerical experiments aimed at comparing two recently proposed sparse appr...
Neste trabalho de dissertação apresentaremos uma classe de precondicionadores baseados na aproximaçã...
In a large number of scientific applications, the solution of sparse linear systems is the stage tha...
Many scientific applications require the solution of large and sparse linear systems of equations us...
Abstract. We investigate the use of sparse approximate-inverse preconditioners for the iterative sol...
Inexact (variable) preconditioning of Multilevel Krylov methods (MK methods) for the solution of lin...