Parallel Performance of ARM ThunderX2 for Atomistic Simulation Algorithms
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Atomistic simulation drives scientific advances in modern material science and accounts for a significant proportion of wall time on High Performance Computing facilities. It is important that algorithms are efficient and implementations are performant in a continuously diversifying hardware landscape. Furthermore, they have to be portable to make best use of the available computing resource. In this paper we assess the parallel performance of some key algorithms implemented in a performance portable framework developed by us. We consider Molecular Dynamics with short range interactions, the Fast Multipole Method and Kinetic Monte Carlo. To assess the performance of emerging architectures, we compare the Marvell ThunderX2 (ARM) architecture to traditional x86_64 hardware made available through the Azure cloud computing service.
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