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Genetic Programming Applied to Compiler Heuristic Optimization
| Book Series | Lecture Notes in Computer Science |
| Publisher | Springer Berlin / Heidelberg |
| ISSN | 0302-9743 (Print) 1611-3349 (Online) |
| Volume | Volume 2610/2003 |
| Book | Genetic Programming |
| DOI | 10.1007/3-540-36599-0 |
| Copyright | 2003 |
| ISBN | 978-3-540-00971-9 |
| DOI | 10.1007/3-540-36599-0_22 |
| Pages | 231-280 |
| Subject Collection | Computer Science |
| SpringerLink Date | Wednesday, January 01, 2003 |
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Genetic Programming Applied to Compiler Heuristic Optimization
Mark Stephenson6 , Una-May O’Reilly7 , Martin C. Martin7 and Saman Amarasinghe6 
| (6) |
Laboratory for Computer Science, USA |
| (7) |
Artificial Intelligence Laboratory, Massachusetts Inst. of Technology, 02139 Cambridge, MA |
Abstract
Genetic programming (GP) has a natural niche in the optimization of small but high payo. software heuristics. We use GP to
optimize the priority functions associated with two well known compiler heuristics: predicated hyperblock formation, and register
allocation. Our system achieves impressive speedups over a standard baseline for both problems. For hyperblock selection,
application-specific heuristics obtain an average speedup of 23% (up to 73%) for the applications in our suite. By evolving
the compiler’s heuristic over several benchmarks, the best general-purpose heuristic our system found improves the predication
algorithm by an average of 25% on our training set, and 9% on a completely unrelated test set. We also improve a well-studied
register allocation heuristic. On average, our system obtains a 6% speedup when it specializes the register allocation algorithm
for individual applications. The general-purpose heuristic for register allocation achieves a 3% improvement.
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