Meta optimization: improving compiler heuristics with machine learning

  • Authors:

  • Mark Stephenson;Saman Amarasinghe;Martin Martin;Una-May O'Reilly

  • Affiliations:

  • Massachusetts Institute of Technology, Cambridge, MA;Massachusetts Institute of Technology, Cambridge, MA;Massachusetts Institute of Technology, Cambridge, MA;Massachusetts Institute of Technology, Cambridge, MA

  • Venue:
  • PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
  • Year:
  • 2003

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Abstract

Compiler writers have crafted many heuristics over the years to approximately solve NP-hard problems efficiently. Finding a heuristic that performs well on a broad range of applications is a tedious and difficult process. This paper introduces Meta Optimization, a methodology for automatically fine-tuning compiler heuristics. Meta Optimization uses machine-learning techniques to automatically search the space of compiler heuristics. Our techniques reduce compiler design complexity by relieving compiler writers of the tedium of heuristic tuning. Our machine-learning system uses an evolutionary algorithm to automatically find effective compiler heuristics. We present promising experimental results. In one mode of operation Meta Optimization creates application-specific heuristics which often result in impressive speedups. For hyperblock formation, one optimization we present in this paper, we obtain an average speedup of 23% (up to 73%) for the applications in our suite. Furthermore, by evolving a compiler's heuristic over several benchmarks, we can create effective, general-purpose heuristics. The best general-purpose heuristic our system found for hyperblock formation improved performance by an average of 25% on our training set, and 9% on a completely unrelated test set. We demonstrate the efficacy of our techniques on three different optimizations in this paper: hyperblock formation, register allocation, and data prefetching.