Genetic Programming Applied to Compiler Heuristic Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{stephenson03,
  author =       "Mark Stephenson and Una-May O'Reilly and 
                 Martin C. Martin and Saman Amarasinghe",
  title =        "Genetic Programming Applied to Compiler Heuristic
                 Optimization",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2003",
  year =         "2003",
  editor =       "Conor Ryan and Terence Soule and Maarten Keijzer and 
                 Edward Tsang and Riccardo Poli and Ernesto Costa",
  volume =       "2610",
  series =       "LNCS",
  pages =        "238--253",
  address =      "Essex",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  ISBN =         "3-540-00971-X",
  URL =          "http://www.martincmartin.com/papers/GPApppliedToCompilerHeuristicOptimizationStephenson.pdf",
  URL =          "http://www.cag.lcs.mit.edu/metaopt/papers/metaopt-euro-gp.ps",
  URL =          "http://citeseer.ist.psu.edu/574999.html",
  URL =          "http://citeseer.ist.psu.edu/569364.html",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=238",
  abstract =     "Genetic programming (GP) has a natural niche in the
                 optimization of small but high payoff software
                 heuristics. We use GP to optimize the priority
                 functions associated with two well known compiler
                 heuristics: predicated hyperblock formation, and
                 register allocation. Our system achieves impressive
                 speedups over a standard baseline for both problems.
                 For hyperblock selection, application-specific
                 heuristics obtain an average speedup of 23percent (up
                 to 73percent) for the applications in our suite. By
                 evolving the compiler's heuristic over several
                 benchmarks, the best general-purpose heuristic our
                 system found improves the predication algorithm by an
                 average of 25percent on our training set, and 9percent
                 on a completely unrelated test set. We also improve a
                 well-studied register allocation heuristic. On average,
                 our system obtains a 6percent speedup when it
                 specializes the register allocation algorithm for
                 individual applications. The general-purpose heuristic
                 for register allocation achieves a 3percent
                 improvement.",
  notes =        "EuroGP'2003 held in conjunction with EvoWorkshops
                 2003",
}

Genetic Programming entries for Mark Stephenson Una-May O'Reilly Martin C Martin Saman Amarasinghe