Exploiting Pointer and Location Equivalence to Optimize Pointer Analysis

摘要

Pointer information is a prerequisite for most program analyses, and inclusion-based, i.e. Andersen-style, pointer analysis is widely used to compute such information. However, current inclusion-based analyses can have prohibitive costs in time and space, especially for programs with millions of lines of code. We present a suite of offline optimizations that exploit pointer and location equivalence to shrink the input to the subsequent pointer analysis without affecting precision, dramatically reducing both analysis time and memory consumption. Using a suite of six open-source C programs ranging in size from 169K to 2.17M LOC, we demonstrate that our techniques on average improve analysis time by 1.3-2.7× and reduce memory consumption by 3.2-6.9× over the best current techniques.