A parallel CUDA implementation of the Gauss-Legendre-spherical-t method for electrostatic interactions.

IF 3.1 2区 化学 Q3 CHEMISTRY, PHYSICAL
James E Gonzales, Wonmuk Hwang, Bernard R Brooks
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引用次数: 0

Abstract

Computing electrostatic interactions remains the bottleneck of molecular dynamics (MD) simulations despite more than a century of effort in developing methods to accelerate the calculation. Previously, we have developed the spherical grids and treecode and Gauss-Legendre-spherical-t (GLST) algorithms for electrostatic interactions. Here, we explain the computational details and discuss the performance of GLST. The GLST algorithm achieves O(N) scaling and should be less demanding in parallel communication compared with the widely used particle mesh Ewald method and likely comparable to the communication costs of the fast multipole method. We find that GLST is suitable for rapid calculation of long-range electrostatic interactions in MD simulations as it has highly tunable accuracy and should scale well on massively parallel computing architectures. The GLST software presented here is available as a standalone library on GitHub.

静电相互作用的gauss - legende - sphere -t方法的并行CUDA实现。
计算静电相互作用仍然是分子动力学(MD)模拟的瓶颈,尽管一个多世纪以来人们一直在努力开发加速计算的方法。在此之前,我们已经开发了用于静电相互作用的球面网格和树码以及gauss - legende - sphere -t (GLST)算法。在这里,我们解释了计算细节并讨论了GLST的性能。GLST算法实现了0 (N)缩放,与广泛使用的粒子网格Ewald方法相比,对并行通信的要求更低,并且可能与快速多极方法的通信成本相当。我们发现GLST适合于MD模拟中远程静电相互作用的快速计算,因为它具有高度可调的精度,并且应该在大规模并行计算架构上很好地扩展。这里提供的GLST软件是GitHub上的一个独立库。
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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来源期刊
Journal of Chemical Physics
Journal of Chemical Physics 物理-物理:原子、分子和化学物理
CiteScore
7.40
自引率
15.90%
发文量
1615
审稿时长
2 months
期刊介绍: The Journal of Chemical Physics publishes quantitative and rigorous science of long-lasting value in methods and applications of chemical physics. The Journal also publishes brief Communications of significant new findings, Perspectives on the latest advances in the field, and Special Topic issues. The Journal focuses on innovative research in experimental and theoretical areas of chemical physics, including spectroscopy, dynamics, kinetics, statistical mechanics, and quantum mechanics. In addition, topical areas such as polymers, soft matter, materials, surfaces/interfaces, and systems of biological relevance are of increasing importance. Topical coverage includes: Theoretical Methods and Algorithms Advanced Experimental Techniques Atoms, Molecules, and Clusters Liquids, Glasses, and Crystals Surfaces, Interfaces, and Materials Polymers and Soft Matter Biological Molecules and Networks.
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