Combining Task-based Parallelism and Adaptive Mesh Refinement Techniques in Molecular Dynamics Simulations

Abstract : Modern parallel architectures require applications to generate massive parallelism so as to feed their large number of cores and their wide vector units. We revisit the extensively studied classical Molecular Dynamics N-body problem in the light of these hardware constraints. We use Adaptive Mesh Refinement techniques to store particles in memory, and to optimize the force computation loop using multi-threading and vectorization-friendly data structures. Our design is guided by the need for load balancing and adaptivity raised by highly dynamic particle sets, as typically observed in simulations of strong shocks resulting in material micro-jetting. We analyze performance results on several simulation scenarios, over nodes equipped by Intel Xeon Phi Knights Landing (KNL) or Intel Xeon Skylake (SKL) processors. Performance obtained with our OpenMP implementation outperforms state-of-the-art implementations (LAMMPS) on both steady and micro-jetting particles simulations. In the latter case, our implementation is 4.7 times faster on KNL, and 2 times faster on SKL.
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Raphaël Prat, Laurent Colombet, Raymond Namyst. Combining Task-based Parallelism and Adaptive Mesh Refinement Techniques in Molecular Dynamics Simulations. ICPP18, International Conference on Parallel Processing., Aug 2018, Eugene, United States. ⟨10.1145/3225058.3225085⟩. ⟨hal-01833266⟩

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