In this paper, we are concerned about computing in parallel several entries of the inverse of a large sparse matrix. We assume that the matrix has already been factorized by a direct method and that the factors are distributed. Entries are efficiently computed by exploiting sparsity of the right-hand sides and the solution vectors in the triangular solution phase. We demonstrate that in this setting, parallelism and computational efficiency are two contrasting objectives. We develop an efficient approach and show its efficacy by runs using the MUMPS code that implements a parallel multifrontal method
A notable characteristic of the scientific computing and machine learning prob-lem domains is the la...
AbstractCoarse grain parallel codes for solving sparse systems of linear algebraic equations can be ...
Sequential and parallel algorithms based on the LU factorization or the QR factorization have been i...
In this paper, we are concerned about computing in parallel several entries of the inverse of a larg...
International audienceIn this paper, we consider the computation in parallel of several entries of t...
We consider the solution of very large systems of linear equations with direct multifrontal methods....
We present the submatrix method, a highly parallelizable method for the approximate calculation of i...
International audienceThe inverse of an irreducible sparse matrix is structurally full, so that it i...
Nous nous intéressons à la résolution de systèmes linéaires creux de très grande taille par des méth...
A few parallel algorithms for solving triangular systems resulting from parallel factorization of sp...
We consider the parallel computation of the diagonal of the inverse of a large sparse matrix. This p...
International audienceDirect methods for the solution of sparse systems of linear equations of the f...
We consider the solution of very large sparse systems of linear equations on parallel architectures....
We consider direct methods to solve sparse linear systems AX = B, where A is a sparse matrix of size...
This paper considers several algorithms for parallelizing the procedure of forward and back substitu...
A notable characteristic of the scientific computing and machine learning prob-lem domains is the la...
AbstractCoarse grain parallel codes for solving sparse systems of linear algebraic equations can be ...
Sequential and parallel algorithms based on the LU factorization or the QR factorization have been i...
In this paper, we are concerned about computing in parallel several entries of the inverse of a larg...
International audienceIn this paper, we consider the computation in parallel of several entries of t...
We consider the solution of very large systems of linear equations with direct multifrontal methods....
We present the submatrix method, a highly parallelizable method for the approximate calculation of i...
International audienceThe inverse of an irreducible sparse matrix is structurally full, so that it i...
Nous nous intéressons à la résolution de systèmes linéaires creux de très grande taille par des méth...
A few parallel algorithms for solving triangular systems resulting from parallel factorization of sp...
We consider the parallel computation of the diagonal of the inverse of a large sparse matrix. This p...
International audienceDirect methods for the solution of sparse systems of linear equations of the f...
We consider the solution of very large sparse systems of linear equations on parallel architectures....
We consider direct methods to solve sparse linear systems AX = B, where A is a sparse matrix of size...
This paper considers several algorithms for parallelizing the procedure of forward and back substitu...
A notable characteristic of the scientific computing and machine learning prob-lem domains is the la...
AbstractCoarse grain parallel codes for solving sparse systems of linear algebraic equations can be ...
Sequential and parallel algorithms based on the LU factorization or the QR factorization have been i...