Evaluation of predicated array data-flow analysis for automatic parallelization

  • Authors:

  • Sungdo Moon;Mary W. Hall

  • Affiliations:

  • Information Sciences Institute, University of Southern California, Marina del Rey, CA;Information Sciences Institute, University of Southern California, Marina del Rey, CA

  • Venue:
  • Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
  • Year:
  • 1999

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Abstract

This paper presents an evaluation of a new analysis for parallelizing compilers called predicated array data-flow analysis. This analysis extends array data-flow analysis for parallelization and privatization to associate predicates with data-flow values. These predicates can be used to derive conditions under which dependences can be eliminated or privatization is possible. These conditions can be used both to enhance compile-time analysis and to introduce run-time tests that guard safe execution of a parallelized version of a computation.As compared to previous work that combines predicates with array data-flow analysis, our approach is distinguished by two features: (1) it derives low-cost, run-time parallelization tests; and, (2) it incorporates predicate embedding and predicate extraction, which translate between the domain of predicates and data-flow values to derive more precise analysis results. We present extensive experimental results across three benchmark suites and one additional program, demonstrating that predicated array data-flow analysis parallelizes more than 40% of the remaining inherently parallel loops left unparallelized by the SUIF compiler and that it yields improved speedups for 5 programs.