RaceFree: an efficient multi-threading model for determinism

  • Authors:

  • Kai Lu;Xu Zhou;Xiaoping Wang;Wenzhe Zhang;Gen Li

  • Affiliations:

  • National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China

  • Venue:
  • Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
  • Year:
  • 2013

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Abstract

Current deterministic systems generally incur large overhead due to the difficulty of detecting and eliminating data races. This paper presents RaceFree, a novel multi-threading runtime that adopts a relaxed deterministic model to provide a data-race-free environment for parallel programs. This model cuts off unnecessary shared-memory communication by isolating threads in separated memories, which eliminates direct data races. Meanwhile, we leverage the happen-before relation defined by applications themselves as one-way communication pipes to perform necessary thread communication. Shared-memory communication is transparently converted to message-passing style communication by our Memory Modification Propagation (MMP) mechanism, which propagates local memory modifications to other threads through the happen-before relation pipes. The overhead of RaceFree is 67.2% according to our tests on parallel benchmarks.