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This paper presents a new analysis for parallelizing compilers called predicated array data-flow analysis, whereby array dataflow analysis for parallelization and privatization is extended 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, which can consist of arbitrary program statements, 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.We have implemented predicated array data-llow analysis in the Stanford SUIF compiler. We describe features of the implementation and present experimental results that demonstrate this analysis improves the performance of three programs from the SPECgSFP benchmark suite.

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.

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This paper presents an evaluation of a new analysis for psrallelizing 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 e&action, 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.