The Generalized Intensional Transformation for Implementing Lazy Functional Languages

  • Georgios Fourtounis
  • Nikolaos Papaspyrou
  • Panos Rondogiannis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7752)


The intensional transformation is a promising technique for implementing lazy functional languages based on a demand-driven execution model. Despite its theoretical elegance and its simple and efficient execution model, the intensional transformation suffered, until now, from two main drawbacks: it could only be applied to programs that manipulate primitive data-types and it could only compile a simple (and rather restricted) class of higher-order functions. In this paper we remedy the above two deficiencies, obtaining a transformation algorithm that is applicable to mainstream lazy functional languages. The proposed transformation initially uses defunctionalization in order to eliminate higher-order functions from the source program. The original intensional transformation is then extended in order to apply to the target first-order language with user-defined data types that resulted from the defunctionalization. It is demonstrated that the proposed technique can be used to compile a relatively large subset of Haskell into portable C code whose performance is comparable to existing mainstream implementations.


intensional transformation dataflow programming defunctionalization compilation lazy functional languages 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arvind, R.S.N.: Executing a program on the MIT tagged-token dataflow architecture. IEEE Transactions on Computers 39, 300–318 (1990)CrossRefGoogle Scholar
  2. 2.
    Ashcroft, E.A., Faustini, A.A., Jagannathan, R., Wadge, W.W.: Multidimensional Programming. Oxford University Press (1995)Google Scholar
  3. 3.
    Boquist, U., Johnsson, T.: The GRIN project: A highly optimising back end for lazy functional languages. In: Kluge, W. (ed.) IFL 1996. LNCS, vol. 1268, pp. 58–84. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  4. 4.
    Charalambidis, A., Grivas, A., Papaspyrou, N.S., Rondogiannis, P.: Efficient intensional implementation for lazy functional languages. Mathematics in Computer Science 2(1), 123–141 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    De La Encina, A., Peña, R.: From natural semantics to C: A formal derivation of two STG machines. Journal of Functional Programming 19(1), 47–94 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Fourtounis, G., Papaspyrou, N., Rondogiannis, P.: The intensional transformation for functional languages with user-defined data types. In: Proceedings of the 8th Panhellenic Logic Symposium, pp. 38–42 (2011)Google Scholar
  7. 7.
    Friedman, D.P., Wise, D.S.: CONS should not evaluate its arguments. In: Proceedings of the International Colloquium on Automata, Languages and Programming, pp. 257–284 (1976)Google Scholar
  8. 8.
    Henderson, P., Jr. Morris, J.H.: A lazy evaluator. In: Proceedings of the 3rd ACM SIGACT-SIGPLAN Symposium on Principles on Programming Languages, pp. 95–103. ACM, New York (1976)Google Scholar
  9. 9.
    Kirkham, C., Gurd, J., Watson, I.: The Manchester prototype dataflow computer. Communications of the ACM, 34–52 (1985)Google Scholar
  10. 10.
    Krivine, J.L.: Un interpréteur du lambda-calcul,
  11. 11.
    Partain, W.: The nofib benchmark suite of Haskell programs. In: Proceedings of the 1992 Glasgow Workshop on Functional Programming, pp. 195–202 (1993)Google Scholar
  12. 12.
    Pingali, K.: Lazy evaluation and the logic variable. In: Proceedings of the 2nd International Conference on Supercomputing, pp. 560–572. ACM, New York (1988)Google Scholar
  13. 13.
    Plaice, J., Mancilla, B.: The practical uses of TransLucid. In: Proceedings of the 1st International Workshop on Context-aware Software Technology and Applications, pp. 13–16. ACM, New York (2009)Google Scholar
  14. 14.
    Pottier, F., Gauthier, N.: Polymorphic typed defunctionalization and concretization. Higher-Order and Symbolic Computation 19, 125–162 (2006)CrossRefzbMATHGoogle Scholar
  15. 15.
    Reynolds, J.C.: Definitional interpreters for higher-order programming languages. In: Proceedings of the 25th ACM National Conference, pp. 717–740. ACM (1972)Google Scholar
  16. 16.
    Rondogiannis, P., Wadge, W.W.: First-order functional languages and intensional logic. Journal of Functional Programming 7(1), 73–101 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Rondogiannis, P., Wadge, W.W.: Higher-order functional languages and intensional logic. Journal of Functional Programming 9(5), 527–564 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Traub, K.R.: A compiler for the MIT tagged-token dataflow architecture. Technical report, Massachusetts Institute of Technology, Cambridge, MA, USA (1986)Google Scholar
  19. 19.
    Wadge, W., Aschroft, E.A.: Lucid, the Dataflow Programming Language. Academic Press (1985)Google Scholar
  20. 20.
    Yaghi, A.A.: The Intensional Implementation Technique for Functional Languages. Ph.D. thesis, Department of Computer Science, University of Warwick, Coventry, UK (1984)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Georgios Fourtounis
    • 1
  • Nikolaos Papaspyrou
    • 1
  • Panos Rondogiannis
    • 2
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensGreece
  2. 2.Department of Informatics and TelecommunicationsUniversity of AthensGreece

Personalised recommendations