Fast Compiler Optimisation Evaluation Using Code-feature Based Performance Prediction

Christophe Dubach, John Cavazos, Björn Franke, Grigori Fursin, Michael O'Boyle, Olivier Temam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Performance tuning is an important and time consuming task which may have to be repeated for each new application and platform. Although iterative optimisation can automate this process, it still requires many executions of different versions of the program. As execution time is frequently the limiting factor in the number of versions or transformed programs that can be considered, what is needed is a mechanism that can automatically predict the performance of a modified program without actually having to run it. This paper presents a new machine learning based technique to automatically predict the speedup of a modified program using a performance model based on the code features of the tuned programs. Unlike previous approaches it does not require any prior learning over a benchmark suite. Furthermore, it can be used to predict the performance of any tuning and is not restricted to a prior seen trans-formation space. We show that it can deliver predictions with a high correlation coefficient and can be used to dramatically reduce the cost of search.
Original languageEnglish
Title of host publicationCF '07 Proceedings of the 4th international conference on Computing Frontiers
Place of PublicationNew York, NY, USA
PublisherACM
Pages131-142
Number of pages12
ISBN (Print)978-1-59593-683-7
DOIs
Publication statusPublished - May 2007

Keywords

  • architecture, artificial neural networks, compiler optimisation, learning, machine, performance modelling

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