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Data flow analysis techniques have traditionally been restricted to the analysis of scalar variables. This retriction, however, imposes a limitation on the kinds of optimizations that can be performed in loops containing array references. We present a data flow framework for array reference analysis that provides the information needed in various optimizations targeted at sequential or fine-grained parallel architectures. The framework extends the traditional scalar framework by incorporating iteration distance values into the analysis to qualify the computed data flow solution during the fixed point iteration. Analyses phrased in this framework are capable of discovering recurrent access patterns among array references that evolve during the execution of a loop. Applications of our framework are discussed for register allocation, load/store optimizations, and controlled loop unrolling.

A practical data flow framework for array reference analysis and its use in optimizations