Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]

Luc-Henri Jolly, A. Duran, Louis Lagardère, J. Ponder, P. Ren, Jean‐Philip Piquemal
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引用次数: 8

Abstract

This living paper reviews the present High Performance Computing (HPC) capabilities of the Tinker-HP molecular modeling package. We focus here on the reference, double precision, massively parallel molecular dynamics engine present in Tinker-HP and dedicated to perform large scale simulations. We show how it can be adapted to recent Intel Central Processing Unit (CPU) petascale architectures. First, we discuss the new set of Intel Advanced Vector Extensions 512 (Intel AVX-512) instructions present in recent Intel processors (e.g., the Intel Xeon Scalable and Intel Xeon Phi 2nd generation processors) allowing for larger vectorization enhancements. These instructions constitute the central source of potential computational gains when using the latest processors, justifying important vectorization efforts for developers. We then briefly review the organization of the Tinker-HP code and identify the computational hotspots which require Intel AVX-512 optimization and we propose a general and optimal strategy to vectorize those particular parts of the code. We intended to present our optimization strategy in a pedagogical way so it could benefit to other researchers and students interested in gaining performances in their own software. Finally we present the performance enhancements obtained compared to the unoptimized code both sequentially and at the scaling limit in parallel for classical non-polarizable (CHARMM) and polarizable force fields (AMOEBA). This paper never ceases to be updated as we accumulate new data on the associated Github repository between new versions of this living paper.
提高Tinker-HP分子建模包的性能[第v1.0条]
本文回顾了目前Tinker-HP分子建模包的高性能计算(HPC)能力。我们专注于参考,双精度,大规模并行分子动力学引擎目前在Tinker-HP,并致力于执行大规模模拟。我们将展示如何使其适应最新的英特尔中央处理单元(CPU)千兆级架构。首先,我们讨论了最新英特尔处理器(例如,英特尔至强可扩展和英特尔至强Phi第二代处理器)中出现的一组新的英特尔高级矢量扩展512(英特尔AVX-512)指令,允许更大的向量化增强。当使用最新的处理器时,这些指令构成了潜在计算增益的主要来源,为开发人员证明了重要的向量化工作的合理性。然后,我们简要回顾了Tinker-HP代码的组织结构,并确定了需要英特尔AVX-512优化的计算热点,并提出了一种通用的最佳策略来对代码的这些特定部分进行矢量化。我们打算以教学的方式呈现我们的优化策略,以便它可以使其他对自己的软件获得性能感兴趣的研究人员和学生受益。最后给出了在经典非极化力场(CHARMM)和极化力场(AMOEBA)下,与未优化代码相比,在顺序和并行缩放极限下获得的性能增强。当我们在相关的Github存储库中积累新数据时,本文永远不会停止更新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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