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Parallel Seismic Wave Propagation on NUMA architectures

Fabrice Dupros 1 Christiane Pousa Ribeiro 2, 3, * Alexandre Carissimi 4, * Jean-François Mehaut 3
* Corresponding author
1 BRGM - Bureau de Recherches Géologiques et Minières (BRGM)
2 LIG - Laboratoire d'Informatique de Grenoble
3 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
4 UFRGS - Instituto de Informática da UFRGS
Abstract : Simulation of large scale seismic wave propagation is an important tool in seismology for efficient strong motion analysis and risk mitigation. Being particularly CPU-consuming, this three-dimensional problem makes use of parallel computing to improve the performance and the accuracy of the simulations. The trend in parallel computing is to increase the number of cores available at the shared-memory level with possible non-uniform cost of memory accesses. We therefore need to consider new approaches more suitable to such parallel systems. In this paper, we firstly report on the impact of memory affinity on the parallel performance of seismic simulations. We introduce a methodology combining efficient thread scheduling and careful data placement to overcome the limitation coming from both the parallel algorithm and the memory hierarchy. The MAi (Memory Affinity interface) is used to smoothly adapt the memory policy to the underlying architecture. We evaluate our methodology on computing nodes with different NUMA characteristics. A maximum gain of 53% is reported in comparison with a classical OpenMP implementation.
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Conference papers
Complete list of metadatas
https://hal.inria.fr/hal-00788916
Contributor : Arnaud Legrand <>
Submitted on : Friday, February 15, 2013 - 1:46:23 PM
Last modification on : Friday, October 25, 2019 - 1:29:34 AM

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CNRS | INRIA | BRGM | LIG | UGA | POLYTECH-GRENOBLE

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Fabrice Dupros, Christiane Pousa Ribeiro, Alexandre Carissimi, Jean-François Mehaut. Parallel Seismic Wave Propagation on NUMA architectures. Proceedings of International Conference on Parallel Computing (ParCO), 2009, Lyon, France. pp.67-74, ⟨10.3233/978-1-60750-530-3-67⟩. ⟨hal-00788916⟩

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