Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

TL;DR

This paper introduces a highly efficient GPU implementation of a stochastic cellular automaton model for surface growth, enabling large-scale simulations that can inform physical systems and explore properties like roughening.

Contribution

The paper presents a novel bit-vectorized GPU implementation of a stochastic cellular automaton for surface growth, capable of simulating billions of sites efficiently.

Findings

Able to simulate billions of lattice sites on a single GPU

Provides insights into cases with arbitrary random probabilities

Enables large-scale studies of surface growth properties

Abstract

Stochastic surface growth models aid in studying properties of universality classes like the Kardar--Paris--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.