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Title page for ETD etd-07162007-235959


Type of Document Dissertation
Author Birch, Johnnie L.
URN etd-07162007-235959
Title Methods for Linear and Nonlinear Array Data Dependence Analysis with the Chains of Recurrences Algebra
Degree Doctor of Philosophy
Department Computer Science, Department of
Advisory Committee
Advisor Name Title
Robert Van Engelen Committee Chair
David Whalley Committee Member
Kyle Gallivan Committee Member
Paul Ruscher Committee Member
Xin Yuan Committee Member
Keywords
  • Chains of Recurrences
  • Depedence Testing
  • Loop Analysis
  • Induction Variable
  • Loop Analysis
  • CR
Date of Defense 2007-08-02
Availability unrestricted
Abstract
The presence of data dependences between statements in a loop iteration space imposes strict

constraints on statement order and loop restructuring when preserving program semantics. A

compiler determines the safe partial ordering of statements that enhance performance by explicitly

disproving the presence of dependences. As a result, the false positive rate of a dependence analysis

technique is a crucial factor in the effectiveness of a restructuring compiler’s ability to optimize

the execution of performance-critical code fragments. This dissertation investigates reducing the

false positive rate by improving the accuracy of analysis methods for dependence problems and

increasing the total number of problems analyzed. Fundamental to these improvements is the

rephrasing of the dependence problem in terms of Chains of Recurrences (CR), a formalism that

has been shown to be conducive to efficient loop induction variable analysis. An infrastructure

utilizing CR-analysis methods and enhanced dependence testing techniques is developed and

tested. Experimental results indicate capabilities of dependence analysis methods can be improved

without a reduction in efficiency. This results in a reduction in the false positive rate and an increase

in the number of optimized and parallelized code fragments.

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