Abstract

Data flow analysis techniques have traditionally been restricted to the analysis of scalar variables. This restriction, 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. The framework is practical in that the fixed point solution requires at most three passes over the body of structured loops. Applications of our framework are discussed for register allocation, load/store optimizations, and controlled loop unrolling.

Citations