NDLI: Evaluation of predicated array data-flow analysis for automatic parallelization

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Evaluation of predicated array data-flow analysis for automatic parallelization

Content Provider	ACM Digital Library
Author	Moon, Sungdo Hall, Mary W.
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.
Starting Page	84
Ending Page	95
Page Count	12
File Format	PDF
ISBN	1581131003
DOI	10.1145/301104.301112
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1999-05-01
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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