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Using Machine Learning to Focus Iterative Optimization F. Agakov, E. Bonilla, J.Cavazos, B.Franke, G. Fursin,
 

Summary: Using Machine Learning to Focus Iterative Optimization
F. Agakov, E. Bonilla, J.Cavazos, B.Franke, G. Fursin,
M.F.P. O'Boyle, J. Thomson, M. Toussaint, C.K.I. Williams
School of Informatics
University of Edinburgh
UK
Abstract
Iterative compiler optimization has been shown to out-
perform static approaches. This, however, is at the cost of
large numbers of evaluations of the program. This paper de-
velops a new methodology to reduce this number and hence
speed up iterative optimization. It uses predictive modelling
from the domain of machine learning to automatically focus
search on those areas likely to give greatest performance.
This approach is independent of search algorithm, search
space or compiler infrastructure and scales gracefully with
the compiler optimization space size. Off-line, a training
set of programs is iteratively evaluated and the shape of the
spaces and program features are modelled. These models
are learnt and used to focus the iterative optimization of a

  

Source: Agakov, Felix - Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh
Edinburgh, University of - Division of Informatics, Institute for Adaptive and Neural Computation
Franke, Björn - School of Informatics, University of Edinburgh
Toussaint, Marc - Institute for Adaptive and Neural Computation, University of Edinburgh
Williams, Chris - Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh

 

Collections: Computer Technologies and Information Sciences