Skip to Main content Skip to Navigation
New interface
Journal articles

Robust memory-aware mappings for parallel multifrontal factorizations

Abstract : 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.
Complete list of metadata

Cited literature [26 references]  Display  Hide  Download
Contributor : Abdou Guermouche Connect in order to contact the contributor
Submitted on : Wednesday, July 27, 2016 - 10:08:36 AM
Last modification on : Friday, November 18, 2022 - 9:25:24 AM


Files produced by the author(s)



Emmanuel Agullo, Patrick Amestoy, Alfredo Buttari, Abdou Guermouche, Jean-Yves L'Excellent, et al.. Robust memory-aware mappings for parallel multifrontal factorizations. SIAM Journal on Scientific Computing, 2016, 38 (3), pp.C256 - C279. ⟨10.1137/130938505⟩. ⟨hal-01334113v2⟩



Record views


Files downloads