论文标题
ARM Thunderx2的平行性能用于原子模拟算法
Parallel Performance of ARM ThunderX2 for Atomistic Simulation Algorithms
论文作者
论文摘要
原子模拟推动了现代材料科学的科学进步，并且在高性能计算设施上占据了很大一部分的墙壁时间。重要的是，算法是有效的，并且实现在连续多元化的硬件景观中表现出色。此外，它们必须便携式以充分利用可用的计算资源。 在本文中，我们评估了我们开发的性能便携式框架中实现的某些关键算法的并行性能。我们考虑具有短距离相互作用的分子动力学，即快速多极方法和动力学蒙特卡洛。为了评估新兴体系结构的性能，我们将Marvell Thunderx2（ARM）体系结构与传统的X86_64硬件进行比较。
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.
