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I was one of the first researchers to introduce the use of machine learning to optimize an optimizing compiler itself. Compilers typically contain many heuristics to solve hard problems approximately and efficiently. Finding heuristics that perform well on a broad range of applications and processors is one of the most complex tasks faced by compiler writers. My research involves using machine learning techniques to automatically construct compiler optimization heuristics.  I have shown that this technique can completely eliminate the human from heuristic design.  My research on applying machine learning to compiler optimizations received the NSF CAREER award.

Selected Publications:

* Automatic Construction of Inlining Heuristics using Machine Learning
  Sameer Kulkarni, John Cavazos, Christian Wimmer, and Douglas Simon.
CGO 2013 

* Mitigating the Compiler Optimization Phase-Ordering Problem Using Machine Learning.
Sameer Kulkarni and John Cavazos. Acceptance: 57/228 (25%)
OOPSLA 2012  [PDF]

* Auto-tuning a High-Level Language Targeted to GPU Codes.
Scott Grauer-Gray, Lifan Xu, Robert Searles Sudhee Ayalasomayajula, John Cavazos.
INPAR 2012 [PDF]

* Using Graph-Based Program Characterization for Predictive Modeling.
Eunjung Park, John Cavazos, and Marco A. Alvarez.
CGO 2012 [PDF]

* A Transactional Memory with Automatic Performance Tuning.
  Qingping Wang, Sameer Kulkarni, John Cavazos, and Michael Spear.
HiPEAC 2012  [PDF]

New Book

* Software Automatic Tuning: From Concepts to State-of-the-Art Results.
Editors : Ken Naono, Keita Teranishi, John Cavazos, and Reiji Suda.
Springer 2011

This research was generously sponsored by the National Science Foundation, DARPA, JP Morgan, and Google.



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