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

Wayne State University, Detroit MI

Investigators

Abstract

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.