GPU Accelerated Computing Towards a Fast and Scalable Seismic Wave Modelling in SEISCOPE SEM46 Code
| Autor: | J. Cao, R. Brossier, E. Cabrera, J. De la Puente, L. Métivier, A. Tarayoun |
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| Přispěvatelé: | Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Equations aux Dérivées Partielles (EDP), Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
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| Zdroj: | Sixth EAGE High Performance Computing Workshop Sixth EAGE High Performance Computing Workshop, Sep 2022, Milan, Italy. pp.1-5, ⟨10.3997/2214-4609.2022615010⟩ |
| Popis: | International audience; Main objectives This study aims at the development of GPU-accelerated code for a fast and scalable seismic wave modelling using the spectral element method (SEM) within the framework of our full waveform modelling and inversion code SEM46. Overall, it contains the single GPU algorithm investigation to explore the computational efficiency that stems from the application of Cartesian-based structured meshes and the multi-GPU implementation based on the domain-decomposition strategy. New aspects covered (1) Three types of parallel prototypes of SEM-based CUDA kernel are presented and compared in terms of modelling accuracy and computational efficiency. (2) To benefit from the Cartesian-based structured mesh, an element-wise parallelization with odd-even mesh coloring is proposed which achieves a significant speedup over the equivalent serial CPU reference code. (3) With the help of CUDA-AWARE MPI, an excellent scaling is obtained in the domain-decomposition-based multi-GPU implementation, which boosts its applicability on large-scale realistic problems. |
| Databáze: | OpenAIRE |
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