Abstract

This paper introduces a novel set of heuristics for identifying logically related sections of the heap such as recursive data structures, objects that are part of the same multi-component structure, and related groups of objects stored in the same collection/array. When combined with lifetime properties of these structures, this information can be used to drive a range of program optimizations including pool allocation, object co-location, static deallocation, and region-based garbage collection. The technique outlined in this paper also improves the efficiency of the static analysis by providing a compact normal form for the abstract models (speeding the convergence of the static analysis).

We focus on two techniques for grouping parts of the heap. The first is a technique for identifying recursive data structures in object-oriented programs based on connectivity and type information. The second technique is a method for grouping objects that make up the same composite structure and that allows us to partition the objects stored in a collection/array into groups based on a similarity relation. We provide a parametric component in the similarity relation to support specialized analysis applications (e.g. numeric analysis of object fields). Using the Em3d and Barnes-Hut benchmarks from the JOlden suite we show how these grouping methods can be used to identify various types of logical structures and enable the application of many region-based optimizations.