In recent years, a variational methods called tensor network methods have been widely applied to obtaining partition functions of classical statistical models or low-energy states of quantum many-body systems. One of the best example is the success of the density matrix renormalization group for many-body systems. In this presentation, I would like to introduce some features of typical tensor network methods and discuss potential bottleneck points in large-scale parallel computations of the tensor network methods.