Abstract
In
order to deliver the promise of Moore's Law to the end user,
compilers must make decisions that are intimately tied to a
specific target architecture. As engineers add architectural
features to increase performance, systems become harder to model, and thus,
it becomes harder for a compiler to make effective decisions.
Machine-learning techniques may be able to help compiler writers
model modern architectures. Because learning techniques can effectively
make sense of high dimensional spaces, they can be a valuable tool for clarifying
and discerning complex decision boundaries. In our work we focus on
loop unrolling, a well-known optimization for exposing instruction level
parallelism. Using the Open Research Compiler (ORC) as a test bed, we
demonstrate how one can use supervised learning techniques to model the appropriateness
of loop unrolling.
Papers
M.
Stephenson, S. Amarasinghe.
Predicting Unroll Factors Using Supervised Classification. In Proceedings of International
Symposium on Code Generation and Optimization. San Jose, California. March 2005 (ps, pdf, ppt).
M.
Stephenson, S. Amarasinghe.
Predicting Unroll Factors Using Nearest Neighbors. MIT-TM-938.
March 2004. (ps, pdf).
Software
We will soon release ORC instrumentation code,
as well as some Matlab implementations of the
learning algorithms we used.
People
Mark Stephenson
Una-May O'Reilly
Saman Amarasinghe
Related Links
Eliot Moss and John Cavazos at the
University of Massachusetts are working on
applying supervised learning to compilation.
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