Automatic Feature Generation for Machine Learning Based Optimizing Compilation

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Hugh Leather and Edwin Bonilla and Michael O'Boyle",
  title =        "Automatic Feature Generation for Machine Learning
                 Based Optimizing Compilation",
  booktitle =    "2009. International Symposium on Code Generation and
  year =         "2009",
  month =        mar,
  pages =        "81--91",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, SBSE, Pentium 6, automatic feature
                 generation, compilation, compiler writer, feature
                 generation technique, grammar, loop unrolling, machine
                 learning, predictive modeling, grammars, learning
                 (artificial intelligence), program compilers",
  DOI =          "doi:10.1109/CGO.2009.21",
  abstract =     "Recent work has shown that machine learning can
                 automate and in some cases outperform hand crafted
                 compiler optimizations. Central to such an approach is
                 that machine learning techniques typically rely upon
                 summaries or features of the program. The quality of
                 these features is critical to the accuracy of the
                 resulting machine learned algorithm; no machine
                 learning method will work well with poorly chosen
                 features. However, due to the size and complexity of
                 programs, theoretically there are an infinite number of
                 potential features to choose from. The compiler writer
                 now has to expend effort in choosing the best features
                 from this space. This paper develops a novel mechanism
                 to automatically find those features which most improve
                 the quality of the machine learned heuristic. The
                 feature space is described by a grammar and is then
                 searched with genetic programming and predictive
                 modeling. We apply this technique to loop unrolling in
                 GCC 4.3.1 and evaluate our approach on a Pentium 6. On
                 a benchmark suite of 57 programs, GCC's hard-coded
                 heuristic achieves only 3percent of the maximum
                 performance available, while a state of the art machine
                 learning approach with hand-coded features obtains
                 59percent. Our feature generation technique is able to
                 achieve 76percent of the maximum available speedup,
                 outperforming existing approaches.",
  notes =        "p85 'Our search technique is a hybrid between
                 Grammatical Evolution [12] and Genetic Programming

                 Also known as \cite{4907653}",

Genetic Programming entries for Hugh Leather Edwin Bonilla Michael O'Boyle