• DocumentCode
    3008417
  • Title

    Automatic Determination of May/Must Set Usage in Data-Flow Analysis

  • Author

    Stone, Andrew ; Strout, Michelle ; Behere, Shweta

  • Author_Institution
    Colorado State Univ., Fort Collins, CO
  • fYear
    2008
  • fDate
    28-29 Sept. 2008
  • Firstpage
    153
  • Lastpage
    162
  • Abstract
    Data-flow analysis is a common technique to gather program information for use in transformations such as register allocation, dead-code elimination, common subexpression elimination, scheduling, and others. Tools for generating data-flow analysis implementations remove the need for implementers to explicitly write code that iterates over statements in a program, but still require them to implement details regarding the effects of aliasing, side effects, arrays, and user-defined structures. This paper presents the DFAGen Tool, which generates implementations for locally separable (e.g. bit-vector) data-flow analyses that are pointer, side-effect, and aggregate cognizant from an analysis specification that assumes only scalars. Analysis specifications are typically seven lines long and similar to those in standard compiler textbooks. The main contribution of this work is the automatic determination of may and must set usage within automatically generated data-flow analysis implementations.
  • Keywords
    data flow analysis; program compilers; software tools; DFAGen tool; common sub expression elimination; compiler textbooks; data-flow analysis; dead-code elimination; may set usage; must set usage; register allocation; Aggregates; Algorithm design and analysis; Data analysis; Debugging; Equations; Flow graphs; Information analysis; Parallel processing; Switches; Transfer functions; Compilers; Data-flow analysis; Domain specific languages; May must determination; Optimization; Program analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Source Code Analysis and Manipulation, 2008 Eighth IEEE International Working Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-0-7695-3353-7
  • Type

    conf

  • DOI
    10.1109/SCAM.2008.28
  • Filename
    4637548