An Efficient Cell List Implementation for Monte Carlo Simulation on GPUs

Maximizing the performance potential of the modern day GPU architecture requires judicious utilization of available parallel resources. Although dramatic reductions can often be obtained through straightforward mappings, further performance improvements often require algorithmic redesigns to more closely exploit the target architecture. In this paper, we focus on efficient molecular simulations for the GPU and propose a novel cell list algorithm that better utilizes its parallel resources. Our goal is an efficient GPU implementation of large-scale Monte Carlo simulations for the grand canonical ensemble. This is a particularly challenging application because there is inherently less computation and parallelism than in similar applications with molecular dynamics. Consistent with the results of prior researchers, our simulation results show traditional cell list implementations for Monte Carlo simulations of molecular systems offer effectively no performance improvement for small systems [5, 14], even when porting to the GPU. However for larger systems, the cell list implementation offers significant gains in performance. Furthermore, our novel cell list approach results in better performance for all problem sizes when compared with other GPU implementations with or without cell lists.Comment: 30 page

GOMC Optimized Monte Carlo Talk

<i>GPU Optimized </i><i>Monte Carlo (GOMC) is open-source software for simulating many-body molecular systems using the Metropolis Monte Carlo algorithm. It supports simulations in a variety of ensembles, which include canonical, isothermal-isobaric, grand canonical, and Gibbs ensemble. This allows GOMC to be used to study vapor-liquid and liquid-liquid equilibria, adsorption in porous materials, surfactant self-assembly, and condensed phase structure for complex molecules. GOMC supports a variety of all-atom, united atom, and coarse grained force fields such as OPLS, TraPPE, Mie, and Martini. The software has been written in object oriented C++, and uses OpenMP and NVIDIA CUDA to allow for execution on multi-core CPU and GPU architectures. The combined multi-core CPU and GPU parallelization achieves up to two orders of magnitude speed-up compared to serial execution.</i

GPU Optimized Monte Carlo (GOMC)

<i>GPU Optimized </i><i>Monte Carlo (GOMC) is open-source software for simulating many-body molecular systems using the Metropolis Monte Carlo algorithm. It supports simulations in a variety of ensembles, which include canonical, isothermal-isobaric, grand canonical, and Gibbs ensemble. This allows GOMC to be used to study vapor-liquid and liquid-liquid equilibria, adsorption in porous materials, surfactant self-assembly, and condensed phase structure for complex molecules. GOMC supports a variety of all-atom, united atom, and coarse grained force fields such as OPLS, TraPPE, Mie, and Martini. The software has been written in object oriented C++, and uses OpenMP and NVIDIA CUDA to allow for execution on multi-core CPU and GPU architectures. The combined multi-core CPU and GPU parallelization achieves up to two orders of magnitude speed-up compared to serial execution.</i

Mie Potentials for Phase Equilibria: Application to Alkenes

Transferable united-atom force fields based on Mie potentials are presented for alkenes. Monte Carlo simulations in the grand canonical ensemble, combined with histogram reweighting, are used to determine vapor–liquid coexistence curves, vapor pressures, heats of vaporization, boiling points, and critical properties for 1-alkenes from ethene to 1-octene. To assess the transferability of the optimized parameters, additional calculations are performed for the cis and trans isomers of 2-butene and 2-pentene and the dienes 1,3-butadiene and 1,5-hexadiene. Saturated liquid densities for the 1-alkenes, 2-pentenes, and 1,5-hexadiene are predicted to within 1 % of experimental data, while deviations of (2 to 5) % from experiment were observed for <i>cis</i>-2-butene and 1,3-butadiene, respectively. Vapor pressures for the alkenes are predicted to within (2 to 15) % of experiment, with errors increasing with chain length and at lower temperatures. Critical temperatures are predicted to within 1 % of experiment for all molecules except for 1,3-butadiene, where the critical temperature is under-predicted by 3.5 %. Transferability is further evaluated through calculations of binary mixture vapor–liquid equilibria. Predictions of the Mie potentials for ethane + propene and 1-butane + 1-hexene are indistinguishable from experimental data

Transferable Force Fields from Experimental Scattering Data with Machine Learning Assisted Structure Refinement

Deriving transferable pair potentials from experimental neutron and X-ray scattering measurements has been a longstanding challenge in condensed matter physics. State-of-the-art scattering analysis techniques estimate real-space microstructure from reciprocal-space total scattering data by refining pair potentials to obtain agreement between simulated and experimental results. Prior attempts to apply these potentials with molecular simulations have revealed inaccurate predictions of thermodynamic fluid properties. In this Letter, a machine learning assisted structure-inversion method applied to neutron scattering patterns of the noble gases (Ne, Ar, Kr, and Xe) is shown to recover transferable pair potentials that accurately reproduce both microstructure and vapor–liquid equilibria from the triple to critical point. Therefore, it is concluded that a single neutron scattering measurement is sufficient to predict macroscopic thermodynamic properties over a wide range of states and provide novel insight into local atomic forces in dense monatomic systems