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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Stephenson, M., O’Reilly, UM., Martin, M.C., Amarasinghe, S. (2003). Genetic Programming Applied to Compiler Heuristic Optimization. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_22
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DOI: https://doi.org/10.1007/3-540-36599-0_22
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