Section: New Results

Robust memory-aware mappings for parallel multifrontal factorization

We have studied the memory scalability of the parallel multifrontal factorization of sparse matrices. In particular, we are interested in controlling the active memory specific to the multifrontal factorization. We illustrate why commonly used mapping strategies (e.g. proportional mapping) cannot achieve a high memory efficiency. We propose a class of “memory-aware” algorithms that aim at maximizing performance under given memory constraints, and explain why they provide reliable memory estimates, thus a more robust solver. We study these issues in the context of the MUMPS solver, in which new experimental static scheduling strategies have been implemented and experimented on large matrices [46] .