Automatic feature generation for machine learning--based optimising compilation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Leather:2014:AFG,
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author = "Hugh Leather and Edwin Bonilla and Michael O'Boyle",
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title = "Automatic feature generation for machine
learning--based optimising compilation",
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journal = "ACM Transactions on Architecture and Code
Optimization",
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volume = "11",
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number = "1",
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pages = "14:1--14:32",
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month = feb,
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year = "2014",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1145/2536688",
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ISSN = "1544-3566 (print), 1544-3973 (electronic)",
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bibdate = "Fri Mar 14 17:30:52 MDT 2014",
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bibsource = "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/taco.bib",
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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.",
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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/|",
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articleno = "14",
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fjournal = "ACM Transactions on Architecture and Code Optimization
(TACO)",
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journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J924",
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doi-url = "http://dx.doi.org/10.1145/2536688",
- }
Genetic Programming entries for
Hugh Leather
Edwin Bonilla
Michael F P O'Boyle
Citations