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The solution of continuous and discrete-time Markovian models is still challenging mainly when we model large complex systems, for example, to obtain performance indexes of parallel and distributed systems. However, iterative numerical algorithms, even well-fitted to a multidimensional structured representation of Markov chains, still face the state space explosion problem. Discrete-event simulations can estimate the stationary distribution based on long run trajectories and are also alternative methods to estimate performance indexes of models. Perfect simulation algorithms directly build steady-state samples avoiding the warm-up period and the initial state bias of forward simulations. This paper introduces the concepts of backward coupling and the advantages of monotonicity properties and component-wise characteristics to simulate Stochastic Automata Networks (SAN). The main contribution is a novel technique to solve SANdescriptions originally unsolvable by iterative methods due to large state spaces. This method is extremely efficient when the state space is large and the model has dynamic monotonicity because it is possible to contract the reachable state space in a smaller set of maximal states. Component-wise characteristics also contribute to the state space reduction extracting extremal states of the model underlying chain. The efficiency of this technique applied to sample generation using perfect simulation is compared to the overall efficiency of using an iterative numerical method to predict performance indexes of SANmodels.