Towards Highly scalable Ab Initio Molecular Dynamics (AIMD) Simulations on the Intel Knights Landing Manycore Processor

M. Jacquelin, W. A. Jong, E. Bylaska
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引用次数: 14

Abstract

The Ab Initio Molecular Dynamics (AIMD) method allows scientists to treat the dynamics of molecular and condensed phase systems while retaining a first-principles-based description of their interactions. This extremely important method has tremendous computational requirements, because the electronic Schrodinger equation, approximated using Kohn-Sham Density Functional Theory (DFT), is solved at every time step. With the advent of manycore architectures, application developers have a significant amount of processing power within each compute node that can only be exploited through massive parallelism. A compute intensive application such as AIMD forms a good candidate to leverage this processing power. In this paper, we focus on adding thread level parallelism to the plane wave DFT methodology implemented in NWChem. Through a careful optimization of tall-skinny matrix products, which are at the heart of the Lagrange Multiplier and non-local pseudopotential kernels, as well as 3D FFTs, our OpenMP implementation delivers excellent strong scaling on the latest Intel Knights Landing (KNL) processor. We assess the efficiency of our Lagrange multipliers kernels by building a Roofline model of the platform, and verify that our implementation is close to the roofline for various problem sizes. Finally, we present strong scaling results on the complete AIMD simulation for a 64 water molecules test case, that scales up to all 68 cores of the Knights Landing processor.
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在Intel Knights Landing多核处理器上实现高度可扩展的从头算分子动力学(AIMD)模拟
从头算分子动力学(AIMD)方法允许科学家处理分子和凝聚相系统的动力学,同时保留基于第一性原理的相互作用描述。这种极其重要的方法具有巨大的计算需求,因为使用Kohn-Sham密度泛函理论(DFT)近似的电子薛定谔方程在每个时间步上都要求解。随着多核体系结构的出现,应用程序开发人员在每个计算节点中拥有大量的处理能力,这些处理能力只能通过大规模并行性来利用。像AIMD这样的计算密集型应用程序可以很好地利用这种处理能力。在本文中,我们着重于在NWChem中实现的平面波DFT方法中添加线程级并行性。通过对高细矩阵产品(拉格朗日乘法器和非局部伪势内核以及3D fft的核心)的精心优化,我们的OpenMP实现在最新的英特尔骑士登陆(KNL)处理器上提供了出色的强大缩放。我们通过构建平台的rooline模型来评估拉格朗日乘数核的效率,并验证我们的实现接近各种问题规模的rooline。最后,我们展示了64个水分子测试用例的完整AIMD模拟的强大缩放结果,该模拟可扩展到Knights Landing处理器的所有68核。
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