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This paper evaluates the performance of a novel view-oriented parallel programming style for parallel programming on cluster computers. View-oriented parallel programming is based on distributed shared memory which is friendly and easy for programmers to use. It requires the programmer to divide shared data into views according to the memory access pattern of the parallel algorithm. One of the advantages of this programming style is that it offers the performance potential for the underlying distributed shared memory system to optimize consistency maintenance. Also it allows the programmer to participate in performance optimization of a program through wise partitioning of the shared data into views. Experimental results demonstrate a significant performance gain of the programs based on the view-oriented parallel programming style.