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.