International audiencePaStiX is a scientific library that provides a high performance direct supernodal solver for very large sparse linear systems. It relies on a block factorization based on an hybrid ordering (Nested Dissection + Halo Approximate Minimum Degree) obtained using the Scotch library. Efficient static scheduling and memory management are used to solve irregular problems with more of 25 millions unknowns on clusters of SMP nodes. In order to solve larger 3D problems, we apply these blockwise algorithms to compute robust and efficient parallel ILU preconditioners
Iterative methods for solving large sparse systems of linear equations are widely used in many HPC a...
We introduce a novel strategy for parallel preconditioning of large-scale linear systems by means of...
AbstractThis paper introduces several strategies to deal with pivot blocks in multi-level block inco...
. The efficiency of solving sparse linear systems on parallel processors and more complex multiclust...
It is important to have a fast, robust and scalable algorithm to solve a sparse linear system AX=B. ...
This article surveys preconditioning techniques for the iterative solution of large linear systems, ...
International audienceSolving large sparse linear systems by iterative methods has often been quite ...
International audienceSolving large sparse systems of linear equations is a crucial and time-consumi...
International audienceIn the context of solving sparse linear systems, an ordering process partition...
A novel parallel preconditioner combining a generalized Factored Sparse Approximate Inverse (FSAI) w...
This paper provides a comprehensive study and comparison of two state-of-the-art direct solvers for ...
The need to solve large sparse linear systems of equations efficiently lies at the heart of many app...
We address the hard question of efficient use on parallel platforms, of incomplete factorization p...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
We present a class of parallel preconditioning strategies built on a multilevel block incomplete LU ...
Iterative methods for solving large sparse systems of linear equations are widely used in many HPC a...
We introduce a novel strategy for parallel preconditioning of large-scale linear systems by means of...
AbstractThis paper introduces several strategies to deal with pivot blocks in multi-level block inco...
. The efficiency of solving sparse linear systems on parallel processors and more complex multiclust...
It is important to have a fast, robust and scalable algorithm to solve a sparse linear system AX=B. ...
This article surveys preconditioning techniques for the iterative solution of large linear systems, ...
International audienceSolving large sparse linear systems by iterative methods has often been quite ...
International audienceSolving large sparse systems of linear equations is a crucial and time-consumi...
International audienceIn the context of solving sparse linear systems, an ordering process partition...
A novel parallel preconditioner combining a generalized Factored Sparse Approximate Inverse (FSAI) w...
This paper provides a comprehensive study and comparison of two state-of-the-art direct solvers for ...
The need to solve large sparse linear systems of equations efficiently lies at the heart of many app...
We address the hard question of efficient use on parallel platforms, of incomplete factorization p...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
We present a class of parallel preconditioning strategies built on a multilevel block incomplete LU ...
Iterative methods for solving large sparse systems of linear equations are widely used in many HPC a...
We introduce a novel strategy for parallel preconditioning of large-scale linear systems by means of...
AbstractThis paper introduces several strategies to deal with pivot blocks in multi-level block inco...