SSE: Development of a High-Performance Parallel Gibbs Ensemble Monte Carlo Simulation Engine

SSE：高性能并行吉布斯集成蒙特卡罗仿真引擎的开发

• 批准号: 1642406
• 负责人: Jeffrey Potoff
• 金额: $ 49.99万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1642406&HistoricalAwards=false
• 关键词: SSE; Development; Performance; Parallel; Gibbs

The use of molecular simulation to study complex physical phenomena at the atomic level has grown exponentially over the last decade with increasing CPU power and the development of parallel molecular dynamics codes that scale efficiently over thousands of processors. Molecular dynamics codes that utilize parallel computation on CPUs and GPUs are relatively well developed, however, there are a number of problems that cannot be simulated with this methodology. Specifically, problems that require the simulation of an open system, such as adsorption in porous materials, require an alternative methodology that allows for fluctuation in the number of molecules in the system. In addition, there are a number of systems where the presence of large free energy barriers and slow diffusion preclude the use of standard molecular dynamics. Notable examples include the prediction of phase equilibria in multi-component lipid bilayers, or polymers. For these types of problems, Monte Carlo or hybrid Monte Carlo/molecular dynamics simulations have the potential to significantly improve computational efficiency. This project is focused on the development of the open-source Monte Carlo simulation engine, GOMC, which is able to use low cost graphics processing units (GPUs) and multi-core processors (CPUs) to significantly reduce computational time. This effort will enable Monte Carlo simulations to be performed with higher fidelity and for larger systems than is currently accessible with standard Monte Carlo simulation codes, enabling the accelerated development of new materials by domain scientists. In addition, this project will provide training for graduate and undergraduate students in Monte Carlo simulation, design of efficient algorithms for parallel computation on a variety of hardware architectures, and software development. Tutorials and other educational materials will be created to support the use of GOMC for teaching Monte Carlo simulation of molecular systems to students in undergraduate and graduate courses at Wayne State as well as other universities. The free distribution of GOMC, along with the tutorials for using the software, will enable other research groups to solve important research problems quickly and accurately. This project, supported by the Office of Advanced Cyberinfrastructure (OAC), and the divisions of Material Research and Chemistry in the Directorate of Mathematical and Physical Sciences, and the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET) in the Directorate of Engneering, will result in software that enables new and better science. It also serves the educational mission of the National, through its active involvement of graduate and undergraduate students.Parallelization of Monte Carlo is complicated by the inherently sequential nature of the algorithm, which limits the reuse of code from molecular dynamics, and necessitates the development of new approaches. The team's previous efforts have shown that despite the sequential nature of Monte Carlo, graphics processors (GPU) and multi-core CPUs can be used to yield significant reductions in wall-clock time required for a given calculation compared to a traditional serial CPU Monte Carlo code. This effort led to the creation of the open-source Monte Carlo simulation engine GPU Optimized Monte Carlo (GOMC). This work will add significant functional and computational enhancements to be added to GOMC. These enhancements include: (1) support for polarizable force fields based on the Drude oscillator and AMOEBA models, (2) advanced configurational bias moves, such as concerted rotation, double bridging, and aggregation-volume bias (3) multi-molecule moves (4) hybrid Monte Carlo/molecular dynamics simulations (5) new optimizations for multi-core and GPU architectures. The project will enable the simulation of large systems (100,000 atoms) at constant chemical potential, providing insight into a broad array of problems such as polymer, lipid and ionic liquid phase behavior, molecular self-assembly, the stabilization of nano and micro particle dispersions for drug delivery, and membrane fusion under physiologically relevant conditions.

在过去的十年里，随着CPU能力的提高和并行分子动力学代码的发展，分子模拟在原子水平上研究复杂的物理现象已经呈指数级增长，这些代码可以在数千个处理器上有效地扩展。 利用CPU和GPU上的并行计算的分子动力学代码相对较好地发展，然而，存在许多不能用这种方法模拟的问题。具体来说，需要模拟开放系统的问题，例如多孔材料中的吸附，需要一种允许系统中分子数量波动的替代方法。 此外，有许多系统中存在的大的自由能势垒和缓慢的扩散排除了使用标准的分子动力学。 值得注意的例子包括预测多组分脂质双层或聚合物中的相平衡。 对于这些类型的问题，蒙特卡罗或混合蒙特卡罗/分子动力学模拟有可能显着提高计算效率。 该项目的重点是开发开源蒙特卡洛模拟引擎GOMC，该引擎能够使用低成本图形处理单元（GPU）和多核处理器（CPU）来显着减少计算时间。 这一努力将使蒙特卡洛模拟能够以更高的保真度进行，并且比目前使用标准蒙特卡洛模拟代码可访问的系统更大，从而使领域科学家能够加速新材料的开发。此外，该项目将为研究生和本科生提供蒙特卡罗模拟、在各种硬件架构上进行并行计算的有效算法设计和软件开发方面的培训。 将创建教程和其他教育材料，以支持使用GOMC教授韦恩州立大学以及其他大学的本科生和研究生课程的分子系统蒙特卡罗模拟。GOMC的免费分发，沿着使用该软件的教程，将使其他研究小组能够快速准确地解决重要的研究问题。该项目由高级网络基础设施办公室（OAC），数学和物理科学理事会的材料研究和化学部门以及工程理事会的化学，生物工程，环境和运输系统（CBET）部门支持，将产生能够实现新的和更好的科学的软件。它还通过研究生和本科生的积极参与，为国家的教育使命服务。蒙特卡罗的离散化是复杂的算法，这限制了分子动力学代码的重用，并需要开发新的方法。 该团队之前的努力表明，尽管蒙特卡洛具有顺序性，但与传统的串行CPU蒙特卡洛代码相比，图形处理器（GPU）和多核CPU可以显著减少给定计算所需的挂钟时间。 这一努力导致了开源蒙特卡罗模拟引擎GPU优化蒙特卡罗（GOMC）的创建。 这项工作将增加显着的功能和计算增强被添加到GOMC。 这些增强功能包括：（1）支持基于Drude振子和AMOEBA模型的可极化力场;（2）先进的配置偏置移动，如协同旋转、双桥接和聚集体积偏置;（3）多分子移动;（4）混合Monte Carlo/分子动力学模拟;（5）针对多核和GPU架构的新优化。该项目将能够在恒定的化学势下模拟大型系统（100，000个原子），深入了解一系列广泛的问题，如聚合物，脂质和离子液体相行为，分子自组装，用于药物递送的纳米和微米颗粒分散体的稳定性，以及生理相关条件下的膜融合。

项目成果

GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids

• DOI: 10.1016/j.softx.2018.11.005
• 发表时间: 2019-01
• 期刊: SoftwareX
• 影响因子: 3.4
• 作者: Younes Nejahi;M. S. Barhaghi;J. Mick;B. Jackman;Kamel Rushaidat;Yuanzhe Li;L. Schwiebert;J. Potoff

Molecular exchange Monte Carlo: A generalized method for identity exchanges in grand canonical Monte Carlo simulations

• DOI: 10.1063/1.5025184
• 发表时间: 2018-08-21
• 期刊: JOURNAL OF CHEMICAL PHYSICS
• 影响因子: 4.4
• 作者: Barhaghi, Mohammad Soroush;Torabi, Korosh;Potoff, Jeffrey J.
• 通讯作者: Potoff, Jeffrey J.

Effect of fluorination on the partitioning of alcohols

氟化对醇分配的影响

• DOI: 10.1080/00268976.2019.1669837
• 发表时间: 2019
• 期刊: Molecular Physics
• 影响因子: 1.7
• 作者: Soroush Barhaghi, Mohammad;Luyet, Chloe;Potoff, Jeffrey J.
• 通讯作者: Potoff, Jeffrey J.

Histogram-Free Reweighting with Grand Canonical Monte Carlo: Post-simulation Optimization of Non-bonded Potentials for Phase Equilibria

使用大正则蒙特卡罗进行无直方图重加权：相平衡非键势的仿真后优化

• DOI: 10.1021/acs.jced.8b01232
• 发表时间: 2019
• 期刊: Journal of Chemical & Engineering Data
• 作者: Messerly, Richard A.;Soroush Barhaghi, Mohammad;Potoff, Jeffrey J.;Shirts, Michael R.
• 通讯作者: Shirts, Michael R.

Prediction of phase equilibria and Gibbs free energies of transfer using molecular exchange Monte Carlo in the Gibbs ensemble

• DOI: 10.1016/j.fluid.2018.12.032
• 发表时间: 2019-05-01
• 期刊: FLUID PHASE EQUILIBRIA
• 影响因子: 2.6
• 作者: Barhaghi, Mohammad Soroush;Potoff, Jeffrey J.
• 通讯作者: Potoff, Jeffrey J.