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Predictive modeling methodology for compiler phase-ordering
| Content Provider | ACM Digital Library |
|---|---|
| Author | Ashouri, Amir Hossein Silvano, Cristina Bignoli, Andrea Palermo, Gianluca |
| Abstract | Today's compilers offer a huge number of transformation options to choose among and this choice can significantly impact on the performance of the code being optimized. Not only the selection of compiler options represents a hard problem to be solved, but also the ordering of the phases is adding further complexity, making it a long standing problem in compilation research. This paper presents an innovative approach for tackling the compiler phase-ordering problem by using predictive modeling. The proposed methodology enables i) to efficiently explore compiler exploration space including optimization permutations and repetitions and ii) to extract the application dynamic features to predict the next-best optimization to be applied to maximize the performance given the current status. Experimental results are done by assessing the proposed methodology with utilizing two different search heuristics on the compiler optimization space and it demonstrates the effectiveness of the methodology on the selected set of applications. Using the proposed methodology on average we observed up to 4% execution speedup with respect to LLVM standard baseline. |
| Starting Page | 7 |
| Ending Page | 12 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450340526 |
| DOI | 10.1145/2872421.2872424 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-01-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Machine learning Autotuning Phase-ordering Compilers |
| Content Type | Text |
| Resource Type | Article |
