Low-rank compression

Improving the memory and time overhead of low-rank parallel linear sparse direct solvers

Through the recent improvements toward exascale supercomputer systems, huge computations can be performed in reasonable times by using massively parallelized operations. Unfortunately, the increase of the computational units in these systems does …

Deciding Non-Compressible Blocks in Sparse Direct Solvers using Incomplete Factorization

Deciding Non-Compressible Blocks in Sparse Direct Solvers using Incomplete Factorization

Recent Developments Around the Block Low-Rank PaStiX Solver

Sparse supernodal solver using block low-rank compression: Design, performance and analysis

Block Low-rank Algebraic Clustering for Sparse Direct Solvers

In this talk, we adress the Block Low-Rank (BLR) clustering problem, to cluster unknowns within separators appearing during the factorization of sparse matrices. We show that methods considering only intra-separators connectivity (i.e., k-way or …

Exploiting Parameterized Task-graph in Sparse Direct Solvers

Task-based programming models have been widely studied in the context of dense linear algebra, but remains less studied for the more complex sparse solvers. In this talk, we will present the use of two different programming models: Sequential Task …

Supernodes ordering to enhance Block Low-Rank compression in sparse direct solvers

On the use of low-rank arithmetic to reduce the complexity of parallel sparse linear solvers based on direct factorization techniques

Solving sparse linear systems is a problem that arises in many scientific applications, and sparse direct solvers are a time consuming and key kernel for those applications and for more advanced solvers such as hybrid direct-iterative solvers. For …

Utilisation de la compression low-rank pour réduire la complexité du solveur PaStiX