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Section: New Software and Platforms
MUMPS
A MUltifrontal Massively Parallel Solver
Keywords: High-Performance Computing - Direct solvers - Finite element modelling
Functional Description
MUMPS is a software library to solve large sparse linear systems (AX=B) on sequential and parallel distributed memory computers. It implements a sparse direct method called the multifrontal method. It is used worldwide in academic and industrial codes, in the context numerical modeling of physical phenomena with finite elements. Its main characteristics are its numerical stability, its large number of features, its high performance and its constant evolution through research and feedback from its community of users. Examples of application fields include structural mechanics, electromagnetism, geophysics, acoustics, computational fluid dynamics. MUMPS has been developed by INPT(ENSEEIHT)-IRIT, Inria, CERFACS, University of Bordeaux, CNRS and ENS Lyon.
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Participants: Patrick Amestoy, Alfredo Buttari, Jean-Yves L'Excellent, Chiara Puglisi, Mohamed Sid-Lakhdar, Bora Uçar, Marie Durand, Abdou Guermouche, Maurice Bremond, Guillaume Joslin, Stéphane Pralet, Aurélia Fevre, Clément Weisbecker, Theo Mary, Emmanuel Agullo, Jacko Koster, Tzvetomila Slavova and François-Henry Rouet
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Partners: Université de Bordeaux - CNRS - CERFACS - ENS Lyon - INPT - IRIT - Université de Lyon - Université de Toulouse - LIP
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Public releases in 2015: MUMPS 5.0.0 (February 2015), including major improvements in terms of performance and robustness, and MUMPS 5.0.1 (July 2015)
Following the creation in 2014 of a consortium for industrial users of Mumps (http://mumps-consortium.org ), some collaborations with industry (scientific exchanges, support, releases in advance) are mentioned in Section 8.1 . We pursued our work on block low-rank solvers [2] (Section 7.13 ), which was extended and applied to 3D frequency domain seismic modeling [19] , [18] (Section 7.15 ) in the context of an on-going collaboration with the Seiscope consortium (https://seiscope2.obs.ujf-grenoble.fr/?lang=en? ). We also worked on the parallel computation of selected entries of the inverse of a sparse matrix [3] (Section 7.14 ).