Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth
Distributed, Parallel, and Cluster Computing
2017-02-10 v1 Materials Science
Statistical Mechanics
Computational Physics
Abstract
Stochastic surface growth models aid in studying properties of universality classes like the Kardar--Paris--Zhang class. High precision results obtained from large scale computational studies can be transferred to many physical systems. Many properties, such as roughening and some two-time functions can be studied using stochastic cellular automaton (SCA) variants of stochastic models. Here we present a highly efficient SCA implementation of a surface growth model capable of simulating billions of lattice sites on a single GPU. We also provide insight into cases requiring arbitrary random probabilities which are not accessible through bit-vectorization.
Cite
@article{arxiv.1606.00310,
title = {Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth},
author = {Jeffrey Kelling and Géza Ódor and Sibylle Gemming},
journal= {arXiv preprint arXiv:1606.00310},
year = {2017}
}
Comments
INES 2016, Budapest http://www.ines-conf.org/ines-conf/2016index.html