Accelerating data race detection with minimal hardware support

  • Authors:
  • Rodrigo Gonzalez-Alberquilla;Karin Strauss;Luis Ceze;Luis Piñuel

  • Affiliations:
  • Univ. Complutense de Madrid, Madrid, Spain;Microsoft Research, Redmond WA and University of Washington, Seattle WA;University of Washington, Seattle WA;Univ. Complutense de Madrid, Madrid, Spain

  • Venue:
  • Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
  • Year:
  • 2011

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Abstract

We propose a high performance hybrid hardware/software solution to race detection that uses minimal hardware support. This hardware extension consists of a single extra instruction, StateChk, that simply returns the coherence state of a cache block without requiring any complex traps to handlers. To leverage this support, we propose a new algorithm for race detection. This detection algorithm uses StateChk to eliminate many expensive operations. We also propose a new execution schedule manipulation heuristic to achieve high coverage rapidly. This approach is capable of detecting virtually all data races detected by a traditional happened-before data race detection approach, but at significantly lower space and performance overhead.