Using the run-time sizes of data structures to guide parallel-thread creation

  • Authors:
  • Lorenz Huelsbergen;James R. Larus;Alexander Aiken

  • Affiliations:
  • AT&T Bell Labs;Univ. of Wisconsin, Madison;Univ. of California, Berkeley

  • Venue:
  • LFP '94 Proceedings of the 1994 ACM conference on LISP and functional programming
  • Year:
  • 1994

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Abstract

Dynamic granularity estimation is a new technique for automatically identifying expressions in functional languages for parallel evaluation. Expressions with little computation relative to thread-creation costs should evaluate sequentially for maximum performance. Static identification of such threads is however difficult. Therefore, dynamic granularity estimation has compile-time and run-time components: Abstract interpretation statically identifies functions whose complexity depends on data structure sizes; the run-time system maintains approximations to these sizes. Compiler-inserted checks consult this size information to make thread creation decisions dynamically.We describe dynamic granularity estimation for a list-based functional language. Extension to general recursive data structures and imperative operations is possible. Performance measurements of dynamic granularity estimation in a parallel ML implementation on a shared-memory machine demonstrate the possibility of large reductions (20%) in execution time.