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Scientific computing algorithms on parallel computing environments are popularly used to simulate scientific and engineering phenomena rather than physical experimentations. The performance of these applications on parallel computing environments depends on the communication delay between processors. To reduce the delay, communication patterns have been studied by many research scientists. The communication characteristics enables us to better understand the performance behaviors of scientific applications and allows us to predict the performance of large scale applications using a model of smaller version application programs. In this paper, we analyzed the communication behavior using NAS-MPI benchmark programs which have been used to represent scientific and engineering workloads. The experimental results show that the communication patterns such as communication timing, sizes of messages and destinations could be used to predict the performance.