Scalable Array SSA and Array Data Flow Analysis

Static Single Assignment (SSA) has been widely accepted as the intermediate program representation of choice in most modern compilers. It allows for a much more efficient data flow analysis of scalars and thus leads to better scalar optimizations. Unfortunately not much progress has been achieved in applying the same techniques to array data flow analysis, a very important and potentially powerful technology. In this paper we propose to improve the applicability and scalability of previous efforts in array SSA through the addition of a symbolic memory access descriptor for summarizing, in an aggregated and thus scalable manner, the accesses to the elements of an array over large, interprocedural program contexts. Scalar SSA functionality is extended using compact memory descriptors and thus provides a unified, scalable SSA representation. We then show the power of our new representation by using it to implement a basic data flow algorithm, reaching definitions. Finally we use this analysis to propagate array constants and obtain performance improvement (speedups) for real benchmark codes.