Meta optimization: improving compiler heuristics with machine learning

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...

• Type: Conference
• Year: 2003
• Where: PLDI
• Authors: Mark Stephenson, Saman P. Amarasinghe, Martin C. Martin, Una-May O'Reilly
• Keywords: Compiler Heuristics, Compiler Writers, Meta Optimization, PLDI 2003, Programming Languages