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Robust Memory-Aware Mappings for Parallel Multifrontal Factorizations

Abstract : We study the memory scalability of the parallel multifrontal factorization of sparse matrices. 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 memory constraints. These algorithms provide both accurate memory predictions and a robust solver. We illustrate our approach with experiments performed on large matrices with the MUMPS solver.
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Contributor : Emmanuel Agullo Connect in order to contact the contributor
Submitted on : Thursday, August 30, 2012 - 9:45:33 PM
Last modification on : Friday, November 18, 2022 - 9:28:27 AM


  • HAL Id : hal-00726644, version 1


Emmanuel Agullo, Patrick Amestoy, Alfredo Buttari, Abdou Guermouche, Jean-Yves L'Excellent, et al.. Robust Memory-Aware Mappings for Parallel Multifrontal Factorizations. SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP 2012), SIAM Activity Group on Supercomputing, Feb 2012, Savannah, Georgia, United States. ⟨hal-00726644⟩



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