Automatic feature generation for machine learning--based optimising compilation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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 F P O'Boyle

Citations