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Web shortcut: http://cTuning.org/predictMachine learning based optimization prediction using program similarities based on static and dynamic program features using continously collected knowledge in the cDatabase as described in [FKMP2011].
In 2012, we also hope to add program analysis, parallelization and run-time adaptation techniques for performance vs power based on [FT2010], [LCWP2009], [JGVP2009], [FCOP2005] and [FOPT2004]. If you have any questions or comments, don't hesitate to contact Grigori Fursin (cTuning/MILEPOST Framework research and development coordinator) or cTuning discussions mailing list.
cTuning Web-service is now available to predict multi-objective optimizations (execution time vs code size vs compilation time) on the fly during compilation. It is now connected with MILEPOST GCC (with ICI plugin system and feature extractor) and CCC framework to optimize programs for better execution time and code size on the fly during compilation. This is a part of our statistical collective optimization concept to convert compilers into modular, self-tuning intelligent adaptive infrastructure. It allows substitution of the compiler default optimization heuristic with the external plugin that communicates with our machine learning based online predictor and improves optimization predictions continuosly for a given architecture, program and a dataset leveraging experience of multiple users.