Sound and precise analysis of parallel programs through schedule specialization

  • Authors:

  • Jingyue Wu;Yang Tang;Gang Hu;Heming Cui;Junfeng Yang

  • Affiliations:

  • Columbia University, New York, NY, USA;Columbia University, New York, NY, USA;Columbia University, New York, NY, USA;Columbia University, New York, NY, USA;Columbia University, NEW YORK, NY, USA

  • Venue:
  • Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
  • Year:
  • 2012

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Abstract

Parallel programs are known to be difficult to analyze. A key reason is that they typically have an enormous number of execution interleavings, or schedules. Static analysis over all schedules requires over-approximations, resulting in poor precision; dynamic analysis rarely covers more than a tiny fraction of all schedules. We propose an approach called schedule specialization to analyze a parallel program over only a small set of schedules for precision, and then enforce these schedules at runtime for soundness of the static analysis results. We build a schedule specialization framework for C/C++ multithreaded programs that use Pthreads. Our framework avoids the need to modify every analysis to be schedule-aware by specializing a program into a simpler program based on a schedule, so that the resultant program can be analyzed with stock analyses for improved precision. Moreover, our framework provides a precise schedule-aware def-use analysis on memory locations, enabling us to build three highly precise analyses: an alias analyzer, a data-race detector, and a path slicer. Evaluation on 17 programs, including 2 real-world programs and 15 popular benchmarks, shows that analyses using our framework reduced may-aliases by 61.9%, false race reports by 69%, and path slices by 48.7%; and detected 7 unknown bugs in well-checked programs.