Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network

  Advanced Search  

Meta Optimization: Improving Compiler Heuristics with Machine Learning

Summary: Meta Optimization:
Improving Compiler Heuristics with Machine Learning
Mark Stephenson and
Saman Amarasinghe
Massachusetts Institute of Technology
Laboratory for Computer Science
Cambridge, MA 02139
{mstephen, saman}@cag.lcs.mit.edu
Martin Martin and Una-May O'Reilly
Massachusetts Institute of Technology
Artificial Intelligence Laboratory
Cambridge, MA 02139
{mcm, unamay}@ai.mit.edu
Compiler writers have crafted many heuristics over the years
to approximately solve NP-hard problems efficiently. Find-
ing a heuristic that performs well on a broad range of ap-
plications is a tedious and difficult process. This paper in-
troduces Meta Optimization, a methodology for automat-
ically fine-tuning compiler heuristics. Meta Optimization


Source: Amarasinghe, Saman - Computer Science and Artificial Intelligence Laboratory & Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)
Fischer, Charles N. - Department of Computer Sciences, University of Wisconsin at Madison


Collections: Computer Technologies and Information Sciences