Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth
Helmholtz-Zentrum Dresden – Rossendorf, Department of Information Services and Computing, Bautzner Landstrasse 400, 01328 Dresden, Germany
arXiv:1606.00310 [cs.DC], (1 Jun 2016)
@article{kelling2016bitvectorized,
title={Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth},
author={Kelling, Jeffrey and Odor, Geza and Gemming, Sibylle},
year={2016},
month={jun},
archivePrefix={"arXiv"},
primaryClass={cs.DC}
}
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.
June 7, 2016 by hgpu
