Parallel sparse flow-sensitive points-to analysis

Abstract

This paper aims to contribute to further advances in pointer (or points-to) analysis algorithms along the combined dimen- sions of precision, scalability, and performance. For precision, we aim to support interprocedural ow-sensitive analysis. For scalability, we aim to show that our approach scales to large applications with reasonable memory requirements. For performance, we aim to design a points-to analysis algo- rithm that is amenable to parallel execution. The algorithm introduced in this paper achieves all these goals. As an ex- ample, our experimental results show that our algorithm can analyze the 2.2MLOC Tizen OS framework with < 16GB of memory while delivering an average analysis rate of > 10KLOC/second. Our points-to analysis algorithm, PSEGPT, is based on the Pointer Sparse Evaluation Graph (PSEG) form, a new analysis representation that combines both points-to and heap def-use information. PSEGPT is a scalable interprocedural flow-sensitive context-insensitive points-to analy- sis that is amenable to efficient task-parallel implementa- tions, even though points-to analysis is usually viewed as a challenge problem for parallelization. Our experimental results with 6 real-world applications on a 12-core machine show an average parallel speedup of 4.45× and maximum speedup of 7.35×. The evaluation also includes precision results by demonstrating that our algorithm identifies significantly more inlinable indirect calls (IICs) than SUPT and SS, two states of the art SSA-based points-to analyses implemented in LLVM.