Abstract
We present an efficient array data flow analysis based global communication optimizer which manages the analysis cost by partitioning the data flow problems into subproblems and solving the subproblems one at a time in a demand driven manner. In comparison to traditional array data flow based techniques, our scheme greatly reduces the memory requirement and manages the analysis time more effectively. The optimizer performs message vectorization, global redundant communication elimination and global communication scheduling. Our experience with the optimizer suggests that array data flow analysis for communication optimization is efficient and effective.
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