GPU implementation of the Rosenbluth generation method for static Monte Carlo simulations

We present parallel version of Rosenbluth Self-Avoiding Walk generation method implemented on Graphics Processing Units (GPUs) using CUDA libraries. The method scales almost linearly with the number of CUDA cores and the method efficiency has only hardware limitations. The method is introduced in tw...

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Bibliographic Details
Published inarXiv.org
Main Authors Guo, Yachong, Baulin, Vladimir A
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.04.2017
Subjects
Algorithms
Computer simulation
Cubic lattice
Graphics processing units
Monte Carlo simulation
Physics - Computational Physics
Physics - Soft Condensed Matter
Sampling methods
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Summary:We present parallel version of Rosenbluth Self-Avoiding Walk generation method implemented on Graphics Processing Units (GPUs) using CUDA libraries. The method scales almost linearly with the number of CUDA cores and the method efficiency has only hardware limitations. The method is introduced in two realizations: on a cubic lattice and in real space. We find a good agreement between serial and parallel implementations and consistent results between lattice and real space realizations of the method for linear chain statistics. The developed GPU implementations of Rosenbluth algorithm can be used in Monte Carlo simulations and other computational methods that require large sampling of molecules conformations.
ISSN:2331-8422
DOI:10.48550/arxiv.1704.04381