Parallel Flow-Sensitive Points-to Analysis
Zhao, Jisheng; Burke, Michael G.; Sarkar, Vivek
DateFebruary 1, 2017
Points-to analysis is a fundamental requirement for many program analyses, optimizations, and debugging/verification tools. However, finding an effective balance between performance, scalability and precision in points-to analysis remains a major challenge. Many flow-sensitive algorithms achieve a desirable level of precision, but are impractical for use on large software. Likewise, many flow-insensitive algorithms scale to large software, but do so with major limitations on precision. Further, given the recent multicore hardware trends, more attention needs to be paid to the use of parallelism for improved performance. In this paper, we introduce a new pointer analysis based on Pointer SSA form (an extension of Array SSA form,) which is flow-sensitive, memory efficient, and can readily be parallelized. It decomposes the points-to analysis into fine-grained units of work that can be easily implemented in an asynchronous task-parallel programming model. More specifically, our contributions are as follows: 1. A Pointer SSA (PSSA)-based scalable interprocedural flow-sensitive context-insensitive pointer analysis (PSSAPT) that produces both points-to and heap def-use information, and supports the task parallel programming model; 2. a preliminary evaluation, including scalability and precision, of the implementation of parallel PSSAPT using a lightweight task-parallel library. Our experimental results with 6 real world applications (including the 2.2MLOC Tizen OS framework) on a 12-core machine show an average speedup of 4.45 and maximum speedup of 7.35. Our evaluation also includes precision results for an inlinable indirect call analysis.