Work-stealing for mixed-mode parallelism by deterministic team-building

  • Authors:

  • Martin Wimmer;Jesper Larsson Träff

  • Affiliations:

  • University of Vienna, Vienna, Austria;University of Vienna, Vienna, Austria

  • Venue:
  • Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
  • Year:
  • 2011

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Abstract

We show how to extend classical work-stealing to deal with tightly coupled data parallel tasks that can require any number of threads r ≥ 1 for their execution, and term this extension work-stealing with deterministic team-building. As threads become idle they attempt to join a team of threads designated for a task requiring r 1 threads for its execution, alternatively to steal a task, requiring no central coordination. Team building and stealing are done according to a deterministic hierarchy and involve at most a logarithmic number of possibly randomized steal attempts. Threads attempting to join the team for a task requiring a large number of threads may help smaller teams while waiting for the large team to form. Once a team has been formed the threads can in close coordination execute the data parallel task. Implementation can be done with standard lock-free data structures, and takes only a single extra compare-and-swap (CAS) operation per thread to build a team. In the degenerate case where all tasks require only a single thread, the implementation coincides with a locality aware work-stealing implementation. Using a prototype C++ implementation of our extended work-stealing algorithm, a mixed-mode parallel Quicksort algorithm with a data parallel partitioning step has been implemented. We compare our (improved) implementation of this algorithm on top of our extended work-stealing scheduler to a standard task-parallel implementation with this scheduler, and with Intel Cilk Plus and Threading Building Blocks. In addition, we also compare to the optimized parallel MCSTL Quicksort. Results are shown for a 32-core Intel Nehalem EX system and a 16-core Sun T2+ system supporting up to 128 hardware threads. The mixed-mode parallel algorithm performs consistently better than the fork-join implementation, often significantly.