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.

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 but highly efficient simulations using a stochastic cellular automaton. 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.

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

  • Lecture (others)
    Seminar Topical Problems, 14.06.2017, Chemnitz, Deutschland

Permalink: https://www.hzdr.de/publications/Publ-25653
Publ.-Id: 25653