Computing the bisimulation partition of a graph plays a key role in various application areas. Two nodes are bisimular if and only if they obtain the same signature. Faced with a big graph, traditional in-memory algorithms can hardly meet practical need. Recently, researchers have proposed two kinds of MapReduce based distributed algorithms to handle bisimulation partition on the big graph, both calculating localized bisimulation. MapReduce based algorithms suffer from huge network communication burden, at the meantime, we only need local information to calculate the signature of a node. Motivated by these observations, we propose an algorithm based on a graph processing system that only uses local information to calculate the signature of a node. We argue that our algorithm can make considerable reduction on the network transportation during computation. In the experiment calculated on the big graph which contains billions of edges, our algorithm can be seven to sixteen times faster even without graph pre-partition.