(51e) GPU Accelerated Configurational Bias Monte Carlo Simulations of Linear Alkanes

Authors: 
Mick, J. R., Wayne State University
Russo, V., Wayne State University
Hailat, E., Wayne State University
Ibrahem, K., 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 common place with the development of highly parallelized molecular dynamics simulation engines, such as LAMMPS and NAMD.    In addition to outstanding scaling across CPUs, these codes, and others, such as HOOMD [1], 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 [23] required for biomolecular systems make such calculations computationally prohibitive using existing serial codes. 

  In this work, a Monte Carlo simulation engine capable of simulating linear chain molecules is presented.  This code is written in a mixture of C/C++ and NVIDIA’s CUDA GPU-programming API.  This code includes a GPU accelerated version of the configurational-bias algorithm.  The performance of the code is demonstrated through the calculation of vapor-liquid coexistence curves and chemical potentials for linear alkanes from methane to decane as a function of system size for systems of 128 to 128,000 interaction sites. The efficiency of the proposed GPU based algorithms is assessed through comparison to state of the art, special purpose serial CPU-bound codes.

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3.            Sanbonmatsu, K.Y., S. Joseph, and C.-S. Tung, Simulating movement of tRNA into the ribosome during decoding. Proceedings of the National Academy of Sciences of the United States of America, 2005. 102(44): p. 15854-15859.