(214w) Simulation of Molecules of Arbitrary Geometry Using GO-MC (GPU-Optimized Monte Carlo)

Authors: 
Mick, J. R., Wayne State University
Rushaidat, K. I., Wayne State University
Li, Y., Wayne State University
Hailat, E., Wayne State University
Schwiebert, L., Wayne State University
Potoff, J. J., Wayne State University



The simulation of systems containing over 100,000 atoms has become commonplace with the development open-source parallelized molecular dynamics simulation codes, such as LAMMPS and NAMD [1, 2].    In addition to outstanding scaling across CPUs, these codes, and others, such as HOOMD [3], have been written to utilize inexpensive graphics processors.  For the simulation of processes at constant temperature and or pressure, molecular dynamics is clearly the method of choice, however, chemical or biological processes that occur at constant chemical potential require simulation in an ensemble that allows for fluctuations in the number of molecules.   Simulation in open systems is performed typically with Monte Carlo simulations in either the grand canonical or Gibbs ensembles.  However, the large system sizes [4, 5] required for biomolecular systems make such calculations computationally prohibitive using existing serial codes. 

In this work, a multi-ensemble Monte Carlo simulation engine, GO-MC(GPU-Optimized Monte Carlo), capable of simulating systems of tens of thousands of molecules of arbitrary geometry on a desktop workstation is presented.  The engine is built on past code to simulate linear molecules on the GPU [6, 7] and is written in C++ using NVIDIA Corp.’s Compute Unified Device Architecture (CUDA) API.  Performance comparisons are made between GPU implementations of the coupled-decoupled and a coupled configurational bias methods [8-10]; as measured by acceptance rates and computation time on the device.  The coupled approach, while inefficient on the CPU, shows promise on the GPU due to better thread balancing.  Performance improvements due to the use of a nearest neighbor cell list [11] are discussed.   Further code optimizations available on the NVIDIA Kepler architecture are described.

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