Scalable Context-Sensitive Points-to Analysis Using Multi-dimensional Bloom Filters

Abstract. Context-sensitive points-to analysis is critical for several program optimizations. However, as the number of contexts grows exponentially, storage requirements for the analysis increase tremendously for large programs, making the analysis non-scalable. We propose a scalable flow-insensitive context-sensitive inclusion-based points-to analysis that uses a specially designed multi-dimensional bloom filter to store the points-to information. Two key observations motivate our proposal: (i) points-to information (between pointer-object and between pointerpointer) is sparse, and (ii) moving from an exact to an approximate representation of points-to information only leads to reduced precision without affecting correctness of the (may-points-to) analysis. By using an approximate representation a multi-dimensional bloom filter can significantly reduce the memory requirements with a probabilistic bound on loss in precision. Experimental evaluation on SPEC 2000 benchmarks and two...

• Type: Conference
• Year: 2009
• Where: APLAS
• Authors: Rupesh Nasre, Kaushik Rajan, Ramaswamy Govindarajan, Uday P. Khedker
• Keywords: APLAS 2009, Context-sensitive Points-to Analysis, Multi-dimensional Bloom filter, Points-to Analysis, Programming Languages