Chimera: hybrid program analysis for determinism

  • Authors:

  • Dongyoon Lee;Peter M. Chen;Jason Flinn;Satish Narayanasamy

  • Affiliations:

  • University of Michigan, Ann Arbor, MI, USA;University of Michigan, Ann Arbor, MI, USA;University of Michigan, Ann Arbor, MI, USA;University of Michigan, Ann Arbor, MI, USA

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

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Abstract

Chimera uses a new hybrid program analysis to provide deterministic replay for commodity multiprocessor systems. Chimera leverages the insight that it is easy to provide deterministic multiprocessor replay for data-race-free programs (one can just record non-deterministic inputs and the order of synchronization operations), so if we can somehow transform an arbitrary program to be data-race-free, then we can provide deterministic replay cheaply for that program. To perform this transformation, Chimera uses a sound static data-race detector to find all potential data-races. It then instruments pairs of potentially racing instructions with a weak-lock, which provides sufficient guarantees to allow deterministic replay but does not guarantee mutual exclusion. Unsurprisingly, a large fraction of data-races found by the static tool are false data-races, and instrumenting them each of them with a weak-lock results in prohibitively high overhead. Chimera drastically reduces this cost from 53x to 1.39x by increasing the granularity of weak-locks without significantly compromising on parallelism. This is achieved by employing a combination of profiling and symbolic analysis techniques that target the sources of imprecision in the static data-race detector. We find that performance overhead for deterministic recording is 2.4% on average for Apache and desktop applications and about 86% for scientific applications.