Sparse Constant Propagation via Memory Classi cation Analysis

This article presents a novel Sparse Constant Propagation technique which provides a heretofore unknown level of practicality. Unlike other techniques which are based on data ow, it is based on the execution-order summarization sweep employed in Memory Classiication Analysis (MCA), a technique originally developed for array dependence analysis. This methodology achieves a precise description of memory reference activity within a summary representation that grows only linearly with program size. Because of this, the collected sparse constant information need not be artiicially limited to satisfy classical data ow lattice requirements, which constrain other algorithms to discard information in the interests of eecient termination. Sparse Constant Propagation is not only more eeective within the MCA framework, but it in fact generalizes the framework. Original MCA provids the means to break only simple induction and reduction types of ow-dependences. The integrated framework provides the means to also break ow-dependences for which array values can be propagated.