Name
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 3% of the maximum performance available, while a state of the art machine learning approach with hand-coded features obtains 59%. Our feature generation technique is able to achieve 76% of the maximum available speedup, outperforming existing approaches. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CGO.2009.21 | IEEE Computer Society |
Keywords | Field | DocType |
resulting machine,best feature,compiler optimizations,automatic feature generation,machine learning,existing approach,compiler writer,hand-coded features obtains,feature space,feature generation technique,art machine,predictive modeling,tree data structures,loop unrolling,grammar,genetic algorithms,optimizing compiler,predictive models,compilers,learning artificial intelligence,genetic programming,informatics,prediction model,compiler optimization,feature extraction,clustering,benchmark testing,data mining,grammars | Rule-based machine translation,Online machine learning,Feature vector,Active learning (machine learning),Computer science,Feature extraction,Optimizing compiler,Compiler,Artificial intelligence,Loop unrolling,Machine learning | Conference |
ISSN | Citations | PageRank |
2164-2397 | 43 | 1.52 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hugh Leather | 1 | 182 | 14.33 |
| Edwin V. Bonilla | 2 | 1008 | 53.32 |
| Michael O'Boyle | 3 | 405 | 19.81 |