Grand Canonical Ensemble Monte Carlo Simulation on a Transputer Array

Zara, Stephen J.; Nicholson, D.

doi:10.1080/08927029008022134

Cited by 11 publications

(4 citation statements)

References 3 publications

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“…Multi-threading in RMCSANS was achieved the same way as in RMC++ multi, using a farm-type algorithm [25]. The speed-up due to multi-threading was determined on an Intel(R) Xeon(R) E5345 2.33 GHz computer using Debian GNU Linux 2.6.23.13 with gcc version 4.2.3 with maximum 8 threads on the 8 processor cores.…”

Section: Appendix B the Adapted MD Potential Used For Systems C And Dmentioning

confidence: 99%

RMCSANS—modelling the inter-particle term of small angle scattering data via the reverse Monte Carlo method

Gereben

Pusztai

McGreevy³

2010

J. Phys.: Condens. Matter

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A new reverse Monte Carlo (RMC) method has been developed for creating three-dimensional structures in agreement with small angle scattering data. Extensive tests, using computer generated quasi-experimental data for aggregation processes via constrained RMC and Langevin molecular dynamics, were performed. The software is capable of fitting several consecutive time frames of scattering data, and movie-like visualization of the structure (and its evolution) either during or after the simulation is also possible.

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Section: Appendix B the Adapted MD Potential Used For Systems C And Dmentioning

confidence: 99%

RMCSANS—modelling the inter-particle term of small angle scattering data via the reverse Monte Carlo method

Gereben

Pusztai

McGreevy³

2010

J. Phys.: Condens. Matter

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“…Since GCEs contain systems with differences in both particle configuration and number, grand canonical MC (GCMC) methods supplement Metropolis configuration space sampling with random particle insertions and deletions [9,10]. GCMC procedures have been employed in a wide range of technical fashions for example by taking advantage of parallelization [11,12] or by coupling them to force-based methods like molecular dynamics (MD) [13,14] or Brownian dynamics (BD) [15] and for a variety of applications, including the simulation of electrolyte solutions [1,16], prediction of water positions in crystal structures [17,18], adsorption processes [19], systems dominated by quantum effects [20], dynamical treatment of lipid bilayers [21] and finally biomolecular ion atmospheres. These latter simulations have typically focused on DNA [22,23] and rarely included other macromolecular polyions (one exception is the treatment of an ion channel in Ref.…”

Section: Introductionmentioning

confidence: 99%

ISIM: A Program for Grand Canonical Monte Carlo Simulations of the Ionic Environment of Biomolecules

Vitalis

Baker

McCammon

2004

Molecular Simulation

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In this work we present a new software package (ISIM), which represents a flexible, computational tool for simulations of electrolyte solutions via a grand canonical Monte Carlo procedure (GCMC) with a specific capability of treating biomolecules in solution. The GCMC method provides a powerful tool for studying the ionic environments of highly charged macromolecules with attention to the atomic detail of both the solute and the mobile counterions. The ISIM software differs from previous schemes mainly by treating different ion types independently and offering a new parameterization procedure for calibrating excess chemical potentials and bulk ion concentrations. Additionally, ISIM leverages the APBS software package to provide accurate descriptions of the biomolecular electrostatic potential through the efficient solution of Poisson's equation. ISIM has been validated on a variety of test systems; we successfully reproduce elementary properties of electrolyte solutions as well as theoretical and experimental results for challenging test systems like Calmodulin and DNA.

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“…The authors suggest that this approach may be limited for larger atomistic systems. In this work, we present an alternative off-lattice GPU-enabled algorithm for the chemical simulation of LennardJones particles, based on the heavily multithreaded principle of energetic decomposition, also known as the 'farm algorithm' which early CPU-based parallel computing studies [41] suggested, but did produce insufficient performance. However, the GPU architecture requires a re-examination of the older algorithm that is deemed inefficient on CPUs.…”

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confidence: 99%

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

Hailat

Russo

Rushaidat

et al. 2013

International Journal of Parallel, Emergent and Distributed Sys

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Graphics processing units (GPUs) offer parallel computing power that usually requires a cluster of networked computers or a supercomputer to accomplish. While writing kernel code is fairly straightforward, achieving efficiency and performance requires very careful optimisation decisions and changes to the original serial algorithm. We introduce a parallel canonical ensemble Monte Carlo (MC) simulation that runs entirely on the GPU. In this paper, we describe two MC simulation codes of Lennard-Jones particles in the canonical ensemble, a single CPU core and a parallel GPU implementations. Using Compute Unified Device Architecture, the parallel implementation enables the simulation of systems containing over 200,000 particles in a reasonable amount of time, which allows researchers to obtain more accurate simulation results. A remapping algorithm is introduced to balance the load of the device resources and demonstrate by experimental results that the efficiency of this algorithm is bounded by available GPU resource. Our parallel implementation achieves an improvement of up to 15 times on a commodity GPU over our efficient single core implementation for a system consisting of 256k particles, with the speedup increasing with the problem size. Furthermore, we describe our methods and strategies for optimising our implementation in detail.

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Grand Canonical Ensemble Monte Carlo Simulation on a Transputer Array

Cited by 11 publications

References 3 publications

RMCSANS—modelling the inter-particle term of small angle scattering data via the reverse Monte Carlo method

RMCSANS—modelling the inter-particle term of small angle scattering data via the reverse Monte Carlo method

ISIM: A Program for Grand Canonical Monte Carlo Simulations of the Ionic Environment of Biomolecules

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

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