Non-Local Lattice Encoding for Bit-Vectorized Cellular Automata GPU Implementations


Non-Local Lattice Encoding for Bit-Vectorized Cellular Automata GPU Implementations

Kelling, J.; Ódor, G.; Gemming, S.

In many areas from physics to economics and social sciences, there are current problems that can be mapped to stochastic cellular automata (SCA). In combination with machine learning techniques, cellular automata with learned rules can be used to efficiently predict real world systems. In physics, they are used to study atomistically the size and shape evolution of micro- and nanostructures, providing insights into processes of self-organization crucial to today's nanotechnology. We present an extremely efficient SCA implementation of a surface growth model using bit-vectorization enhanced by non-local encoding on GPU. The employed technique and non-local encoding can be transfered to other applications.

Keywords: GPGPU; Surface Growth; Kardar-Parisi-Zhang; Monte-Carlo; Cellular Automaton

Permalink: https://www.hzdr.de/publications/Publ-23299
Publ.-Id: 23299