Bit-vectorized GPU implementation of a stochastic cellular automaton model for surface growth

Jeffrey Kelling, G. Ódor, Sibylle Gemming

Research output: Conference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationINES 2016 - 20th Jubilee IEEE International Conference on Intelligent Engineering Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages233-237
Number of pages5
ISBN (Electronic)9781509012169
DOIs
Publication statusPublished - aug. 26 2016
Event20th Jubilee IEEE International Conference on Intelligent Engineering Systems, INES 2016 - Budapest, Hungary
Duration: jún. 30 2016júl. 2 2016

Other

Other20th Jubilee IEEE International Conference on Intelligent Engineering Systems, INES 2016
CountryHungary
CityBudapest
Period6/30/167/2/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Modelling and Simulation
  • Computer Science Applications
  • Numerical Analysis

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    Kelling, J., Ódor, G., & Gemming, S. (2016). Bit-vectorized GPU implementation of a stochastic cellular automaton model for surface growth. In INES 2016 - 20th Jubilee IEEE International Conference on Intelligent Engineering Systems, Proceedings (pp. 233-237). [7555127] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INES.2016.7555127