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GPU Accelerated Substructuring Methods for Sparse Linear Systems

Abstract : In this paper, we present and analyze parallel substructuring methods based on conjugate gradient method, a iterative Krylov method, for solving sparse linear systems on GPUs. Numerical experiments performed on a set of matrices coming from the finite element analysis of large scale engineering problems, show the efficiency and robustness of substructuring methods based on iterative Krylov method for solving sparse linear systems in a context of a hybrid multi-core-GPU. © 2016 IEEE.
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https://hal-centralesupelec.archives-ouvertes.fr/hal-02426672
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Submitted on : Thursday, January 2, 2020 - 4:47:29 PM
Last modification on : Thursday, July 2, 2020 - 9:12:02 AM

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Abal-Kassim Cheik Ahamed, Frédéric Magoulès. GPU Accelerated Substructuring Methods for Sparse Linear Systems. 19th IEEE International Conference on Computational Science and Engineering, 14th IEEE International Conference on Embedded and Ubiquitous Computing and 15th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, CSE-EUC-DCABES 2016, Aug 2016, Paris, France. pp.614-625, ⟨10.1109/CSE-EUC-DCABES.2016.249⟩. ⟨hal-02426672⟩

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