MILEPOST GCC

MILEPOST GCC is a free, community-driven, open-source, adaptive, self-tuning compiler that combines stable production-quality GCC, Interactive Compilation Interface and machine learning plugins to adapt to any given architecture and program automatically and predict profitable optimizations to improve program execution time, code size and compilation time.[1][2] It is currently used and supported by academia and industry[3] and is intended to open up research opportunities to automate compiler and architecture design and optimization.[4]

MILEPOST GCC
Developer(s)cTuning foundation / MILEPOST consortium
Initial release2009
Stable release
4.4.x / May 21, 2010 (2010-05-21)
Repository
Operating systemCross-platform
TypeCompiler
LicenseGNU General Public License (version 3 or later)
WebsiteGitHub, online API, cTuning.org/ctuning-cc, cTuning.org/milepost-gcc

MILEPOST GCC is currently a part of the community-driven Collective Tuning Initiative (cTuning) to enable self-tuning computing systems based on collaborative open-source R&D infrastructure with unified interfaces and to improve the quality and reproducibility of research on code and architecture optimization. MILEPOST GCC is connected with the Collective Optimization Database to collect and reuse profitable optimization cases from the community and predict high-quality optimizations based on statistical analysis of past optimization data.

In January 2018, the cTuning foundation and the Raspberry Pi Foundation published an interactive article featuring MILEPOST GCC and Collective Knowledge framework "for collaborative research into multi-objective autotuning and machine learning techniques."[5]

References
  1. Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon, Zbigniew Chamski, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Bilha Mendelson, Ayal Zaks, Eric Courtois, Francois Bodin, Phil Barnard, Elton Ashton, Edwin Bonilla, John Thomson, Chris Williams, Michael O'Boyle. Milepost gcc: Machine learning enabled self-tuning compiler International journal of parallel programming, Volume 39, Issue 3, pp. 296-327, June 2011 (link)
  2. Grigori Fursin, Cupertino Miranda, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Ayal Zaks, Bilha Mendelson, Phil Barnard, Elton Ashton, Eric Courtois, Francois Bodin, Edwin Bonilla, John Thomson, Hugh Leather, Chris Williams, Michael O'Boyle. MILEPOST GCC: machine learning based research compiler. Proceedings of the GCC Developers' Summit, Ottawa, Canada, June 2008 (link)
  3. IBM Releases Open Source Machine Learning Compiler, Slashdot, July 2009 (link)
  4. Rethinking code optimization for mobile and multicore, InfoWorld, July 2009 (link)
  5. Grigori Fursin, Anton Lokhmotov, Dmitry Savenko, Eben Upton. A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques, arXiv:1801.08024, January 2018 (arXiv link, interactive report with reproducible experiments)
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.