Estimating the Impact of Scalable Pointer Analysis on Optimization

This paper addresses the following question: Do scalable control-flow-insensitive pointer analyses provide the level of precision required to make them useful in compiler optimizations? We first describe alias frequency, a metric that measures the ability of a pointer analysis to determine that pairs of memory accesses in C programs cannot be aliases. We believe that this kind of information is useful for a variety of optimizations, while remaining independent of a particular optimization. We show that control-flow and context insensitive analyses provide the same answer as the best possible pointer analysis on at least 95% of all statically generated alias queries. In order to understand the potential run-time impact of the remaining 5% queries, we weight the alias queries by dynamic execution counts obtained from profile data. Flow-insensitive pointer analyses are accurate on at least 95% of the weighted alias queries as well. We then examine whether scalable pointer analyses are inaccurate on the remaining 5% alias queries because they are context-insensitive. To this end, we have developed a new context-sensitive pointer analysis that also serves as a general engine for tracing the flow of values in C programs. To our knowledge, it is the first technique for performing context-sensitive analysis with subtyping that scales to millions of lines of code. We find that the new algorithm does not identify fewer aliases than the context-insensitive analysis.