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Atomistic simulation drives scientific advances in modern material scienceand accounts for a significant proportion of wall time on High PerformanceComputing facilities. It is important that algorithms are efficient andimplementations are performant in a continuously diversifying hardwarelandscape. Furthermore, they have to be portable to make best use of theavailable computing resource.  In this paper we assess the parallel performance of some key algorithmsimplemented in a performance portable framework developed by us. We considerMolecular Dynamics with short range interactions, the Fast Multipole Method andKinetic Monte Carlo. To assess the performance of emerging architectures, wecompare the Marvell ThunderX2 (ARM) architecture to traditional x86_64 hardwaremade available through the Azure cloud computing service.

Parallel Performance of ARM ThunderX2 for Atomistic Simulation Algorithms