An investigation of the efficient implementation of cellular automata on multi-core CPU and GPU hardware

• We examine speed-up factors of GPGPU over CPU a number key parameters of CA.
• The amount of activity is found to play a large role in GPGPU speed-ups.
• The GPGPU shows minimal variation in processing time under activity variation.
• Speed-ups are found to be proportionate to the arithmetic activity/neighbourhood size.
• The scale of generations and cells required to gain largest speed-ups is shown.

Cellular automata (CA) have proven to be excellent tools for the simulation of a wide variety of phenomena in the natural world. They are ideal candidates for acceleration with modern general purpose-graphical processing units (GPU/GPGPU) hardware that consists of large numbers of small, tightly-coupled processors. In this study the potential for speeding up CA execution using multi-core CPUs and GPUs is investigated and the scalability of doing so with respect to standard CA parameters such as lattice and neighbourhood sizes, number of states and generations is determined. Additionally the impact of ‘Activity’ (the number of ‘alive’ cells) within a given CA simulation is investigated in terms of both varying the random initial distribution levels of ‘alive’ cells, and via the use of novel state transition rules; where a change in the dynamics of these rules (i.e. the number of states) allows for the investigation of the variable complexity within.