International audienceThe memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems. This paper describes a first implementation of an out-of-core extension to a parallel multifrontal solver (MUMPS). We show that larger problems can be solved on limited-memory machines with reasonable performance, and we illustrate the behaviour of our parallel out-of-core factorization. Then we use simulations to discuss how our algorithms can be modified to solve much larger problems
Factorizing a sparse matrix is a robust way to solve large sparse systems of linear equations. Howev...
We consider the solution of very large sparse systems of linear equations on parallel architectures....
International audienceWe study the memory scalability of the parallel multifrontal factorization of ...
International audienceThe memory usage of sparse direct solvers can be the bottleneck to solve large...
(eng) The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems ...
The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems involv...
International audienceABSTRACT The memory usage of sparse direct solvers can be the bottleneck to so...
International audienceHigh performance sparse direct solvers are often a method of choice in various...
High performance sparse direct solvers are often a method of choice in various simulation problems. ...
Poster Session of the Second International Workshop on Combinatorial Scientific Computing (CSC05), C...
We are interested in the memory usage of sparse direct solvers. We particularly focus on the parall...
(eng) The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems....
The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems. This ...
(eng) High performance sparse direct solvers are often a method of choice in various simulation prob...
(eng) We are concerned with the memory usage of sparse direct solvers. We particularly focus on the ...
Factorizing a sparse matrix is a robust way to solve large sparse systems of linear equations. Howev...
We consider the solution of very large sparse systems of linear equations on parallel architectures....
International audienceWe study the memory scalability of the parallel multifrontal factorization of ...
International audienceThe memory usage of sparse direct solvers can be the bottleneck to solve large...
(eng) The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems ...
The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems involv...
International audienceABSTRACT The memory usage of sparse direct solvers can be the bottleneck to so...
International audienceHigh performance sparse direct solvers are often a method of choice in various...
High performance sparse direct solvers are often a method of choice in various simulation problems. ...
Poster Session of the Second International Workshop on Combinatorial Scientific Computing (CSC05), C...
We are interested in the memory usage of sparse direct solvers. We particularly focus on the parall...
(eng) The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems....
The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems. This ...
(eng) High performance sparse direct solvers are often a method of choice in various simulation prob...
(eng) We are concerned with the memory usage of sparse direct solvers. We particularly focus on the ...
Factorizing a sparse matrix is a robust way to solve large sparse systems of linear equations. Howev...
We consider the solution of very large sparse systems of linear equations on parallel architectures....
International audienceWe study the memory scalability of the parallel multifrontal factorization of ...