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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