Improving Parallel Scalability for Molecular Dynamics Simulations in the Exascale Era

Molecular Dynamics (MD) simulations are timeconsuming as they simulate particle-to-particle interactions for increasingly larger particle counts. Applications split these interactions into short-range and long-range interactions at large counts as long-range ones can be approximated with a fundamentally different computation. State-of-the-art implementations map these computations to different processes, often on different nodes, which results in heterogeneous processes with disparate parallel scaling properties, with periodic information exchanges between them. This split and the communication that it requires creates a richer performance optimization space. Our fundamentally novel approach increases asynchrony between the process classes, which implies that up-to-date grid potentials are not always available and so we instead model evolving potentials based on their histories. We provide an analysis of the accuracy of our approach and assess the accuracy of trajectories and maintenance of simulation invariants. We implement our approach in the open-source LAMMPS MD code. Experimental timing measurements indicate up to 80 % speedup in those configurations where synchrony is a substantial bottleneck.