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MICRO
2009
IEEE

Portable compiler optimisation across embedded programs and microarchitectures using machine learning

9 years 10 months ago
Portable compiler optimisation across embedded programs and microarchitectures using machine learning
Building an optimising compiler is a difficult and time consuming task which must be repeated for each generation of a microprocessor. As the underlying microarchitecture changes from one generation to the next, the compiler must be retuned to optimise specifically for that new system. It may take several releases of the compiler to effectively exploit a processor’s performance potential, by which time a new generation has appeared and the process starts again. We address this challenge by developing a portable optimising compiler. Our approach employs machine learning to automatically learn the best optimisations to apply for any new program on a new microarchitectural configuration. It achieves this by learning a model off-line which maps a microarchitecture description plus the hardware counters from a single run of the program to the best compiler optimisation passes. Our compiler gains 67% of the maximum speedup obtainable by an iterative compiler search using
Christophe Dubach, Timothy M. Jones, Edwin V. Boni
Added 24 May 2010
Updated 24 May 2010
Type Conference
Year 2009
Where MICRO
Authors Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, Grigori Fursin, Michael F. P. O'Boyle
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