Memory-based scheduling for a parallel multifrontal solver

Summary form only given. The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems. We describe dynamic scheduling strategies that aim at reducing the memory usage of a parallel direct solver. Combined to static modifications of the tasks dependency graph, experiments show that such techniques have a good potential to improve the memory usage of a parallel multifrontal solver, MUMPS.