International audienceMany scientific applications need to solve very large sparse linear systems in order to simulate phenomena close to the reality. Grid computing is an answer to the growing demand of computational power. In a grid computing environment, communication times are significant and the bandwidth is variable, therefore frequent synchronizations slow down performances. Thus it is desirable to reduce the number of synchronizations in a parallel direct algorithm. Inspired from multisplitting techniques, the GREMLINS (GRid Efficient Methods for LINear Systems) solver we developed consists of solving several linear problems obtained by splitting. The principle of the balancing algorithm is presented, and experimental results are gi...
The solution of large sparse linear systems is often the most time-consuming part of many science an...
The solution of large sparse linear systems is often the most time-consuming part of many science an...
. The efficiency of solving sparse linear systems on parallel processors and more complex multiclust...
International audienceTraditional largesparselinearsolvers are not suited in agrid computing environ...
International audienceSolving large sparse linear systems is essential in numerous scientific domain...
The goal of this paper is to introduce a new approach to the building of efficient distributed linea...
In this paper we describe an efficient iterative algorithm for solving large sparse linear systems o...
International audienceGrid computing focuses on making use of a very large amount of resources from ...
International audienceIn this paper, we present, evaluate and analyse the performance of parallel sy...
Abstract Efficiently solving large sparse linear systems on loosely coupled net-works of computers i...
AbstractWe propose a hybrid sparse system solver for handling linear systems using algebraic domain ...
The need to solve large sparse linear systems of equations efficiently lies at the heart of many app...
We propose a hybrid sparse system solver for handling linear systems using algebraic domain decompos...
The paper deals with parallel approach for the numerical solution of large, sparse, non-symmetric sy...
International audienceIn this paper, we revisit the Krylov multisplitting algorithm presented in Hua...
The solution of large sparse linear systems is often the most time-consuming part of many science an...
The solution of large sparse linear systems is often the most time-consuming part of many science an...
. The efficiency of solving sparse linear systems on parallel processors and more complex multiclust...
International audienceTraditional largesparselinearsolvers are not suited in agrid computing environ...
International audienceSolving large sparse linear systems is essential in numerous scientific domain...
The goal of this paper is to introduce a new approach to the building of efficient distributed linea...
In this paper we describe an efficient iterative algorithm for solving large sparse linear systems o...
International audienceGrid computing focuses on making use of a very large amount of resources from ...
International audienceIn this paper, we present, evaluate and analyse the performance of parallel sy...
Abstract Efficiently solving large sparse linear systems on loosely coupled net-works of computers i...
AbstractWe propose a hybrid sparse system solver for handling linear systems using algebraic domain ...
The need to solve large sparse linear systems of equations efficiently lies at the heart of many app...
We propose a hybrid sparse system solver for handling linear systems using algebraic domain decompos...
The paper deals with parallel approach for the numerical solution of large, sparse, non-symmetric sy...
International audienceIn this paper, we revisit the Krylov multisplitting algorithm presented in Hua...
The solution of large sparse linear systems is often the most time-consuming part of many science an...
The solution of large sparse linear systems is often the most time-consuming part of many science an...
. The efficiency of solving sparse linear systems on parallel processors and more complex multiclust...