Attribute Grammar Genetic Programming Algorithm for Automatic Code Parallelization

  • Daniel Howard
  • Conor Ryan
  • J. J. Collins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6935)


A method is presented for evolving individuals that use an Attribute Grammar (AG) in a generative way. AGs are considerably more flexible and powerful than the closed, context free grammars normally employed by GP. Rather than evolving derivation trees as in most approaches, we employ a two step process that first generates a vector of real numbers using standard GP, before using the vector to produce a parse tree. As the parse tree is being produced, the choices in the grammar depend on the attributes being input to the current node of the parse tree. The motivation is automatic parallelization or the discovery of a re-factoring of a sequential code or equivalent parallel code that satisfies certain performance gains when implemented on a target parallel computing platform such as a multicore processor. An illustrative and a computed example demonstrate this methodology.


Context Free Grammar Attribute Grammar Parallel Computing Automatic Parallelization Genetic Programming Grammatical Evolution Evolutionary Computation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel Howard
    • 1
  • Conor Ryan
    • 2
  • J. J. Collins
    • 2
  1. 1.Howard Science LimitedMalvernUnited Kingdom
  2. 2.Department of Computer Science and Information Science (CSIS)University of LimerickIreland

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