Parallel simulation of asynchronous Cellular Automata evolution

For simulating physical and chemical processes on molecular level, asynchronous cellular automata with probabilistic transition rules are widely used being sometimes referred to as Monte-Carlo methods. The simulation requires a huge cellular space and millions of iterative steps for obtaining the CA evolution representing a real scene of the process. This may be attained by allocating the CA evolution program onto a multiprocessor system.

We propose a new parallelization method of asynchronous CA based on its stochastic properties. The efficiency assessment and experimental results are presented.