The program summary graph and flow-sensitive interprocedual data flow analysis

Abstract

This paper discusses a method for interprocedural data flow analysis which is powerful enough to express flowsensitive problems but fast enough to apply to very large programs. While such information could be applied toward standard program optimizations, the research described here is directed toward software tools for parallel programming, in which it is crucial. Many of the recent “supercomputers” can be roughly characterized as shared memory multi-processors. These include top-of-the-line systems from Cray Research and IBM, as well as multi-processor computers developed and successfully marketed by many younger companies. Development of efficient, correct programs on these machines presents new challenges to the designers of compilers, debuggers, and programming environments. Powerful analysis mechanisms have been developed for understanding the structure of programs. One such mechanism, data dependence analysis, has been evolving for many years. The product of data dependence analysis is a dota dependence gmph, a directed multi-graph that describes the interactions of program components through shared memory. Such a graph has been shown useful for a variety of applications from vectorization and parallelization to compiler management of locality. Another application of the data dependence graph is as an aid to static debugging of parallel programs. PTOOL [4] is a software system developed at Rice University to help programmers understand parallel programs. It is within this context that we at Rice have learned of the importance of interprocedural data flow analysis. I will briefly describe the PTOOL system and explain the kind of interprocedural information valuable in such an environment. PTOOL is designed to help locate interactions between