  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.
