Parallel Monte Carlo simulation in the canonical ensemble on the graphics processing unit

Hailat, Eyad; Russo, Vincent; Rushaidat, Kamel; Mick, Jason R.; Schwiebert, Loren; Potoff, Jeffrey J.

doi:10.1080/17445760.2013.833617

Cited by 5 publications

(6 citation statements)

References 34 publications

(37 reference statements)

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“…That means that new algorithms had to be developed that map optimally on GPU hardware. Examples of these modifications are: nonbonded‐interactions and efficient cell list implementations, electrostatics, many‐body potentials, ReaxFF, rigid‐body constraints, Monte Carlo, and MD . Other use cases are to speed up post‐processing, screening, accelerating analysis of void space in porous materials on multicore and GPU platforms, and boosting theoretical zeolitic framework generation for the determination of new materials structures .…”

Section: Discussionmentioning

confidence: 99%

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Dubbeldam

Walton

Vlugt

et al. 2019

Advcd Theory and Sims

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Molecular simulations are an excellent tool to study adsorption and diffusion in nanoporous materials. Examples of nanoporous materials are zeolites, carbon nanotubes, clays, metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and zeolitic imidazolate frameworks (ZIFs). The molecular confinement these materials offer has been exploited in adsorption and catalysis for almost 50 years. Molecular simulations have provided understanding of the underlying shape selectivity, and adsorption and diffusion effects. Much of the reliability of the modeling predictions depends on the accuracy and transferability of the force field. However, flexibility and the chemical and structural diversity of MOFs add significant challenges for engineering force fields that are able to reproduce experimentally observed structural and dynamic properties. Recent developments in design, parameterization, and implementation of force fields for MOFs and zeolites are reviewed.contain silicon and oxygen. They are readily available, very stable, and relatively cheap. The material should have the right combination of high adsorption selectivity, combined with adequate capacity for use in fixed-bed devices. Recently, new classes of nanoporous materials have been designed that have stability, high void volumes, and well defined tailorable cavities of uniform size. Example are metal-organic frameworks (MOFs), [1][2][3][4][5][6][7] covalent organic frameworks (COFs), [8,9] and zeolitic imidazolate frameworks (ZIFs). [10] These novel materials posses almost unlimited structural variety because of the many combinations of building blocks that can be imagined. The building blocks self-assembly during synthesis into crystalline materials that, after evacuation of the structure, can find applications in adsorption separations, air purification, gas storage, chemical sensing, and catalysis. [4,11] The judicious selection of building blocks allows the pore volume and functionality to be tailored in a rational manner. Because of the designprinciple underlying the synthesis, it is important to understand structure-properties relations, such as the mechanisms of gas separation as a function of shape and size of the pore system, in order to create "blueprints to MOF-design." Computer simulation is cost-effective, fast, and allows for rapid identification of important structural parameters affecting adsorption.Most of the published works on molecular simulation studying zeolites have used the Kiselev model. [12] In this model, zeolite atoms are fixed and interactions of guest atoms with silicon atoms is accounted for by an effective interaction only with the surrounding oxygen atoms. Interactions between sorbate molecules and the host are modeled by placing Lennard-Jones sites and partial charges on all framework atoms and sorbate molecules. The Kiselev model is attractive because of its simplicity and computational efficiency. This model has shown significant success, both in using the molecular dynamics method to compute, for example, the diffu...

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Section: Discussionmentioning

confidence: 99%

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Dubbeldam

Walton

Vlugt

et al. 2019

Advcd Theory and Sims

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“…A. Yaseen and Y. Li used the remapping method to calculate the total energy on GPU for protein systems [37]. A group at Wayne State University [39,40] where each block performs an individual Monte Carlo simulation [41,42,43]. We have not found any previous realization of MC simulation for large-scale long range interacting systems on GPU.…”

Section: Introductionmentioning

confidence: 99%

“…A. Yaseen and Y. Li used the remapping method to calculate the total energy on GPU for protein systems [37]. A group at Wayne State University [39,40] realized a GPU code for Gibbs ensemble MC simulation of simple liquids. J.…”

Section: Introductionmentioning

confidence: 99%

A GPU-based large-scale Monte Carlo simulation method for systems with long-range interactions

Liang

Xing

2017

Journal of Computational Physics

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In this work we present an efficient implementation of Canonical Monte Carlo simulation for Coulomb many body systems on graphics processing units (GPU). Our method takes advantage of the GPU Single Instruction, Multiple Data (SIMD) architectures, and adopts the sequential updating scheme of Metropolis algorithm. It makes no approximation in the computation of energy, and reaches a remarkable 440-fold speedup, compared with the serial implementation on CPU. We further use this method to simulate primitive model electrolytes, and measure very precisely all ion-ion pair correlation functions at high concentrations. From these data, we extract the renormalized Debye length, renormalized valences of constituent ions, and renormalized dielectric constants. These results demonstrate unequivocally physics beyond the classical Poisson-Boltzmann theory.

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“…However, this is not an unrealistic problem size for high performance parallel computing systems. In previous work, our research group developed a Monte Carlo simulation engine for GPUs .…”

Section: Related Workmentioning

confidence: 99%

“…This work is presented in the context of an open‐source software program for running Monte Carlo simulations , but these techniques are general and can be applied to other programs. More information about this program, including a detailed review of other research efforts on simulating molecular systems using Monte Carlo simulation can be found in .…”

Section: Introductionmentioning

confidence: 99%

Improving performance of GPU code using novel features of the NVIDIA kepler architecture

Schwiebert

Hailat

et al. 2016

Concurrency and Computation

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SUMMARYGraphics processing unit (GPU) computing is a popular approach to simulating complex models and performing massive calculations. GPUs have attracted a great deal of interest because they offer both high performance and energy efficiency. Efficient General-Purpose computation on Graphics Processing Units requires good parallelism, memory coalescing, regular memory access, small overhead on data exchange between the CPU and the GPU, and few explicit global synchronizations, which are usually gained from optimizing the algorithms. Besides these advantages, the proper use of some novel features provided on NVIDIA GPUs can offer further improvement. In this paper, we modify an existing optimized GPU application to illustrate the potential performance gains of these features and to demonstrate the performance trade offs of different implementation choices. The paper focuses on the challenges of reducing interactions between CPU and GPU and reducing the use of explicit synchronization. We explain how to achieve these objectives using two features of the Kepler architecture, warp shuffle, and dynamic parallelism. We find that a judicious use of these two techniques, eliminating repeated operations and synchronizations, results in significantly better performance. We describe various pitfalls encountered in optimizing our code to use these two features and how these were addressed. In particular, we identify a subtle data race condition when using dynamic parallelism under certain circumstances, and present our solution. Finally, we present a technique to trade off the allocation of various device resources to find the parameters that offer the best performance.

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Parallel Monte Carlo simulation in the canonical ensemble on the graphics processing unit

Cited by 5 publications

References 34 publications

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

A GPU-based large-scale Monte Carlo simulation method for systems with long-range interactions

Improving performance of GPU code using novel features of the NVIDIA kepler architecture

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