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A general data dependence analysis for parallelizing compilers

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An Erratum to this article was published on 19 March 2008

Abstract

Many dependence tests have been proposed for loop parallelization in the case of arrays with linear subscripts, but little work has been done on the arrays with nonlinear subscripts, which sometimes occur in parallel benchmarks and scientific and engineering applications. This paper focuses on array subscripts coupled integer power index variables. We attempt to use the integer interval theory to solve the above difficult dependence test problem. Some “interval solution” rules for polynomial equations have been proposed in this paper. Furthermore, based on the proposed rules, we present a novel approach to loop dependence analysis, which is termed the Polynomial Variable Interval test or PVI test, and also develop a related algorithm. Some case studies show that the PVI test is effective and efficient. Compared to the I test and Omega test, the PVI test makes significant improvement, and is, therefore, a more general scheme of dependence test.

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Authors and Affiliations

  1. Department of Statistics, University of California at Berkeley, Berkeley, CA, 94720, USA

    Jing Zhou

  2. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China

    Jing Zhou & Guosun Zeng

Authors
  1. Jing Zhou
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  2. Guosun Zeng
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Correspondence to Jing Zhou.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s11227-008-0197-x

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Zhou, J., Zeng, G. A general data dependence analysis for parallelizing compilers. J Supercomput 45, 236–252 (2008). https://doi.org/10.1007/s11227-007-0168-7

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  • DOI: https://doi.org/10.1007/s11227-007-0168-7

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