Section: New Results

Robust Memory-Aware Mapping for Parallel Multifrontal Factorizations

Participants : Emmanuel Agullo [HIEPACS project-team] , Patrick Amestoy [INPT-IRIT] , Alfredo Buttari [CNRS-IRIT] , Abdou Guermouche [HIEPACS project-team] , Jean-Yves L'Excellent, François-Henry Rouet [Lawrence Berkeley Laboratory, CA, USA] .

In this work, we study 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., the proportional mapping) cannot provide a high memory efficiency, which means that they tend to let the memory usage of the factorization grow when the number of processes increases. We propose “memory-aware” algorithms that aim at maximizing the granularity of parallelism while respecting memory constraints. These algorithms provide accurate memory estimates prior to the factorization and can significantly enhance the robustness of a multifrontal code. We illustrate our approach with experiments performed on large matrices.

This work has been published in the SIAM Journal on Scientific Computing [1].