Automatic feature generation for machine learning--based optimising compilation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Leather:2014:AFG,
  author =       "Hugh Leather and Edwin Bonilla and Michael O'Boyle",
  title =        "Automatic feature generation for machine
                 learning--based optimising compilation",
  journal =      "ACM Transactions on Architecture and Code
                 Optimization",
  volume =       "11",
  number =       "1",
  pages =        "14:1--14:32",
  month =        feb,
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1145/2536688",
  ISSN =         "1544-3566 (print), 1544-3973 (electronic)",
  bibdate =      "Fri Mar 14 17:30:52 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/taco.bib",
  abstract =     "Recent work has shown that machine learning can
                 automate and in some cases outperform handcrafted
                 compiler optimisations. 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 learnt 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 article develops a novel mechanism to
                 automatically find those features that most improve the
                 quality of the machine learnt heuristic. The feature
                 space is described by a grammar and is then searched
                 with genetic programming and predictive modelling. 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, GCCs hard-coded heuristic
                 achieves only 3percent of the maximum performance
                 available, whereas 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.",
  acknowledgement = "Nelson H. F. Beebe, University of Utah, Department
                 of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake
                 City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1
                 801 581 4148, e-mail: \path|beebe@math.utah.edu|,
                 \path|beebe@acm.org|, \path|beebe@computer.org|
                 (Internet), URL:
                 \path|http://www.math.utah.edu/~beebe/|",
  articleno =    "14",
  fjournal =     "ACM Transactions on Architecture and Code Optimization
                 (TACO)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J924",
  doi-url =      "http://dx.doi.org/10.1145/2536688",
}

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

Citations