PLDS: Partitioning linked data structures for parallelism

  • Authors:

  • Min Feng;Changhui Lin;Rajiv Gupta

  • Affiliations:

  • University of California, Riverside, CA;University of California, Riverside, CA;University of California, Riverside, CA

  • Venue:
  • ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
  • Year:
  • 2012

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Abstract

Recently, parallelization of computations in the presence of dynamic data structures has shown promising potential. In this paper, we present PLDS, a system for easily expressing and efficiently exploiting parallelism in computations that are based on dynamic linked data structures. PLDS improves the execution efficiency by providing support for data partitioning and then distributing computation across threads based on the partitioning. Such computations often require the use of speculation to exploit dynamic parallelism. PLDS supports a conditional speculation mechanism that reduces the cost of speculation. PLDS can be employed in the context of different forms of parallelism, which to cover a wide range of parallel applications. PLDS provides easy-to-use compiler directives, using enabling programmers to choose from among a variety of data partitionings to distribute computation across threads in a partitioning-sensitive fashion, and to use conditional speculation when required. We evaluate our implementation of PLDS using ten benchmarks, of which six are parallelized using speculation. PLDS achieves 1.3x--6.9x speedups on an 8-core machine.