Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth
Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth
Kelling, J.; Ódor, G.; Gemming, S.
Stochastic surface growth models aid in studying properties of universality classes like the Kardar--Parisi--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.
Keywords: Surface Growth; Kardar-Parisi-Zhang; GPU; Monte-Carlo; Stochastic Cellular Automaton
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Contribution to proceedings
20th Jubilee IEEE International Conference on Intelligent Engineering Systems 2016, 30.06.-02.07.2016, Budapest, Ungarn
Proceedings of the 20th Jubilee IEEE International Conference on Intelligent Engineering Systems 2016: IEEE
DOI: 10.1109/INES.2016.7555127
Cited 4 times in Scopus -
Lecture (Conference)
20th Jubilee IEEE International Conference on Intelligent Engineering Systems 2016: IEEE, 30.06.-02.07.2016, Budapest, Ungarn -
Contribution to WWW
https://arxiv.org/abs/1606.00310
Permalink: https://www.hzdr.de/publications/Publ-23689
