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    Iterative Data-flow Analysis, Revisited

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    Author
    Cooper, Keith D.; Harvey, Timothy J.; Kennedy, Ken
    Date
    March 26, 2004
    Abstract
    The iterative algorithm is widely used to solve instances of data-flow analysis problems. The algorithm is attractive because it is easy to implement and robust in its behavior. The theory behind the algorithm shows that, for a broad class of problems, it terminates and produces correct results. The theory also establishes a set of conditions where the algorithm runs in at most d(G) + 3 passes over the graph—a round-robin algorithm, running a "rapid'' framework, on a reducible graph. Fortunately, these restrictions encompass many practical analyses used in code optimization. In practice, compilers encounter situations that lie outside this carefully described region. Compilers encounter irreducible graphs—probably more often than the early studies suggest. They use variations of the algorithm other than the round-robin form. They run on problems that are not rapid. This paper explores both the theory and practice of iterative data-flow analysis. It explains the role of reducibility in the classic Kam-Ullman time bound. It presents experimental data to show that different versions of the iterative algorithm have distinctly different behavior. It gives practical advice that can improve the performance of iterative solvers on both reducible and irreducible graphs.
    Citation
    Cooper, Keith D., Harvey, Timothy J. and Kennedy, Ken. "Iterative Data-flow Analysis, Revisited." (2004) https://hdl.handle.net/1911/96324.
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    Technical report
    Citable link to this page
    https://hdl.handle.net/1911/96324
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    You are granted permission for the noncommercial reproduction, distribution, display, and performance of this technical report in any format, but this permission is only for a period of forty-five (45) days from the most recent time that you verified that this technical report is still available from the Computer Science Department of Rice University under terms that include this permission. All other rights are reserved by the author(s).
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    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map