Abstract de la publi numéro 4571
We describe the improvements to the task
scheduling for MUMPS, an asynchronous distributed memory direct solver
for sparse linear systems. In the new approach, we determine, during
the analysis of the matrix, candidate processes for the tasks
that will be dynamically scheduled during the subsequent factorization.
This approach significantly improves the scalability of the solver
in terms of execution time and storage.
By comparison with the previous version of MUMPS, we demonstrate the
efficiency and the scalability of the new algorithm on up to 512
processors. Our test cases include matrices from
regular 3D grids and irregular grids from real-life applications.