Finding a ground state of a given Hamiltonian on a graph $G=(V,E)$ is an important but hard problem. One of the potential methods is to use a Markov chain Monte Carlo to sample the Gibbs distribution whose highest peaks correspond to the ground states. In this short paper, we investigate the stochastic cellular automata, in which all spins are updated independently and simultaneously. We prove that (i) if the temperature is sufficiently high and fixed, then the mixing time is at most of order $log|V|$, and that (ii) if the temperature drops in time $n$ as $1/log n$, then the limiting measure is uniformly distributed over the ground states.
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