Efficient Parallel Monte-Carlo Simulations for Large-Scale Studies of Surface Growth Processes


Efficient Parallel Monte-Carlo Simulations for Large-Scale Studies of Surface Growth Processes

Kelling, J.; Ódor, G.; Weigel, M.; Gemming, G.

Lattice Monte Carlo methods are used to investigate far from and out-of-equilibrium systems, including surface growth, spin systems and solid mixtures. Such studies require observations of large systems over long times scales, to allow structures to grow over orders of magnitude, which necessitates massively parallel simulations. This talk presents work done to address the problem of parallel processing introducing correlations in Monte Carlo updates. Studies of the effect of correlations on scaling and dynamical properties of surface growth systems and related lattice gases is investigated further by comparing results obtained by correlation-free and intrinsically correlated simulations. Where the latter, based on a stochastic cellular automaton approach, are of interest because of their high efficiency. The primary subject of study is the Kardar–Parisi–Zhang surface growth in (2+1) dimensions. Key physical insights about this universality class, like precise universal exponent values and exponent relations, obtained from large-scale simulations are presented.
At the end of the talk, I will also speak about my current work at the computational science group at HZDR, which includes problems like frameworkdevelopment, image analysis and related machine learning applications.

Keywords: Lattice Monte Carlo; GPU; Surface Growth; Kardar-Parisi-Zhang

  • Lecture (Conference)
    IHRS NanoNet Annual Workshop 2017, 16.-18.08.2017, Neuklingenberg, Deutschland

Permalink: https://www.hzdr.de/publications/Publ-25961
Publ.-Id: 25961