Efficient Program Compilation through Machine Learning Techniques
Software Automatic Tuning: From Concepts to State-of-the-Art Results, Ken Naono, Keita Teranishi, John Cavazos and Reiji Suda, editors, Springer, September 2010
Abstract
The wealth of available compiler optimizations leads to the dual problems of finding the best set of optimizations and the best heuristic parameters to tune each optimization. We describe how machine learning techniques, such as logistic regression, can be used to address these problems. We focus on decreasing the compile time for a static commercial compiler, while preserving the execution time. We show that we can speed up the compile process by at least a factor of two with almost the same generated code quality on the SPEC2000 benchmark suite, and that our logistic classifier achieves the same prediction quality for non-SPEC benchmarks.
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