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Efficient Program Compilation Through Machine Learning Techniques

  • Gennady Pekhimenko
  • Angela Demke Brown
Chapter

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.

Keywords

Execution Time Training Data Feature Vector Compiler Optimization Reduce Execution Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer New York 2011

Authors and Affiliations

  1. 1.Carnegie Mellon UniversityPittsburghUSA

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