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machine-learning enabled self-tuning compiler
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MILEPOST GCC is the first practical attept to build machine learning enabled open-source self-tuning production (and research) compiler that can adapt to any architecture using iterative feedback-directed compilation, machine learning and collective optimization. It is based on production quality GCC that supports more than 30 families of architectures and can compile real, large applications including Linux, and on Interactive Compilation Interface that provides plugin system to access internals of compilers. MILEPOST GCC attempts to correlate program features and program optimizations during empirical iterative compilation to predict good optimizations for unseen programs based on prior learning. MILEPOST and cTuning infrastructure automates code and architecture optimization to improve execution time, code size, compilation time and other characteristics at the same time. This technology is not GCC-dependent and can be used in any compiler using common Interactive Compilation Interface and compiler independent plugins. The first version of the MILEPOST GCC and MILEPOST framework has been created during the MILEPOST project. All public MILEPOST developments have been coordinated by Grigori Fursin. More information can be found in the following paper about MILEPOST GCC.

  • FAQs - Does MILEPOST GCC solve all optimization problems? How similar is it ACOVEA tool? etc...

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