The program dependence graph and its use in optimization

  • Jeanne Ferrante
  • Karl J. Ottenstein
  • Joe D. Warren
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 167)


In this paper we present an intermediate program representation, called a program dependence graph or PDG, which summarizes not only the data dependences of each operation but also summarizes the control dependences of the operations. Data dependences represent only the relevant data flow relationships of the program. Analagously, control dependences represent only the relevant control flow relationships of the program, in contrast to the usual control flow graph. The PDG allows transformations such as vectorization, which previously required special treatuent of control dependence, to be performed in a manner which is uniform for both control and data dependences. Program transformations which require interaction of the two can also be easily handled by the representation. As an example, a new incremental approach to modifying data dependences resulting from branch deletion is introduced. Another value of our representation is that many traditional optimizations operate more efficiently on the PDG. Since dependences in the PDG connect computationally relevant parts of the program, a single walk of these dependences is sufficient to perform many optimizations.


Dependence Graph Program Transformation Control Flow Graph Control Dependence Vectorization Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Allen, Frances E. and Cocke, John. A catalogue of optimizing transformations in Design and Optimization of Compilers (Randall Rustin, Ed.) Prentice-Hall (1972) 1–30.Google Scholar
  2. 2.
    Allen, J.R., Kennedy, Ken, Porterfield, Carrie, and Warren, Joe. Conversion of control dependence to data dependence. 10th Ann. ACM Symp. on Princ. of Prog. Lang. Austin, Texas (January, 1983) 177–189.Google Scholar
  3. 3.
    Arvind, Gostelow, Kim, and Plouffe, Wil. An asynchronous programming language and computing machine. University of California at Irvine Tech Report TR114A (December, 1978)Google Scholar
  4. 4.
    Dennis, Jack B. Data flow supercomputers. IEEE Computer 13, 11 (Nov. 1980) 48–56.Google Scholar
  5. 5.
    Ferrante, Jeanne, and Ottenstein, Karl. A program form based on data dependency in predicate regions. 10th Ann. ACM Symp. on Princ. of Prog. Lang. Austin, Texas (January, 1983) 217–231.Google Scholar
  6. 6.
    Ferrante, Jeanne, Ottenstein, Karl, and Warren, Joe D. The program dependence graph and its use in optimization. In preparation.Google Scholar
  7. 7.
    Graham, Susan and Wegman, Mark. A fast and usually linear algorithm for global flow analysis. JACM 231 (January, 1976) 172–202.Google Scholar
  8. 8.
    Kas'janov, V.N. Distinguishing hammocks in a directed graph. Soviet Math. Doklady 165 (1975) 448–450.Google Scholar
  9. 9.
    Kuck, D. J; Kuhn, R. H., Padua, D. A.; Leasure, B. and Wolfe, M. Dependence graphs and compiler optimizations. 8th Ann. ACM Symp. on Princ. of Prog. Lang. Williamsburg, VA (Jan. 26–28, 1981) 207–218.Google Scholar
  10. 10.
    Ottenstein, Karl J. Data-flow graphs as an intermediate program form. Ph.D. Thesis. Computer Sciences Dept., Purdue Univ. (August 1978) 283 pages.Google Scholar
  11. 11.
    Padua, David A., Kuck, David J., and Lawrie, Duncan. High-Speed multiprocessors and their compilers. IEEE Transactions on Computers 29,9 (September, 1980) 763–776.Google Scholar
  12. 12.
    Padua Haiek, David Alejan. Multiprocessors: Discussion of some theoretical and practical problems. Ph.D. Thesis. Computer Sciences Dept., University of Illinois (1980).Google Scholar
  13. 13.
    Ryder, Barbara G. Incremental data flow analysis. 10th Ann. ACM Symp. on Princ. of Prog. Lang. Austin, Texas (January, 1983) 167–176.Google Scholar
  14. 14.
    Warren, Joe. A hierarchical basis for reordering transformations. 11th Ann. ACM Symp. on Princ. of Prog. Lang. Salt Lake City, Utah (January, 1984), 272–282.Google Scholar
  15. 15.
    Wegman, Mark. Summarizing graphs by regular expressions. 10th Ann. ACM Symp. on Princ. of Prog. Lang. Austin, Texas (January, 1983) 203–212.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1984

Authors and Affiliations

  • Jeanne Ferrante
    • 1
  • Karl J. Ottenstein
    • 2
  • Joe D. Warren
    • 3
  1. 1.IBM T. J. Watson Research CenterYorktown HeightsUSA
  2. 2.Dept. of Math. and Computer ScienceMichigan Technological UniversityHoughtonUSA
  3. 3.Department of Computer ScienceCornell UniversityIthacaUSA

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