|Type||Non-profit research and development organization, Engineering organization|
|Focus||Collaborative software, Open Science, Open Source Software, Reproducibility, Computer Science, Machine learning, Artifact Evaluation, Performance tuning, Knowledge management|
|Origins||Collective Tuning Initiative & Milepost GCC|
|Method||Develop open-source tools, public repository of knowledge and common methodology for collaborative and reproducible experimentation|
The cTuning Foundation is a global non-profit organization developing open-source tools and common methodology to enable sustainable, collaborative and reproducible research in Computer science, perform collaborative optimization of realistic workloads across devices provided by volunteers, enable self-optimizing computer systems , and automate artifact evaluation at machine learning and systems conferences and journals.
- Collective Knowledge - open-source framework to share artifacts as customizable and reusable components with unified JSON API, implement sustainable and portable research workflows and crowdsource experiments across diverse platforms provided by volunteers.
- ACM ReQuEST - Reproducible Quality-Efficient Systems Tournaments to co-design efficient software/hardware stack for deep learning algorithms in terms of speed, accuracy and costs across diverse platforms, environments, libraries, models and data sets
- MILEPOST GCC - open-source technology to build machine learning based self-optimizing compilers.
- CK autotuner - universal, customizable and multi-objective autotuner.
- CK package and environment manager - python CK API to detect, install and rebuild code and data dependencies for CK workflows.
- Artifact Evaluation - validation of experimental results from published papers at the computer systems conferences.
- Reproducible Papers - a public index of reproducible papers with available and reusable research components.
In 2014, cTuning Foundation was registered in France as a non-profit research and development organization. It received funding from the EU TETRACOM project and ARM to develop Collective Knowledge Framework and prepare reproducible research methodology for ACM and IEEE conferences.
Current funding comes from the European Union research and development funding programme, dividiti, Microsoft and other organizations.
- Fursin, Grigori; Anton Lokhmotov; Ed Plowman (January 2016). Collective Knowledge: Towards R&D Sustainability. Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE). Retrieved 14 September 2016.
- 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)
- Fursin, Grigori; Abdul Memon; Christophe Guillon; Anton Lokhmotov (January 2015). Collective Mind, Part II: Towards Performance- and Cost-Aware Software Engineering as a Natural Science. Proceedings of the CPC 2016. arXiv:1506.06256. Bibcode:2015arXiv150606256F.
- ACM ReQuEST'18 front matters and report (PDF)
- World's First Intelligent, Open Source Compiler Provides Automated Advice on Software Code Optimization, IBM press-release, June 2009 (link)
- Grigori Fursin. Collective Tuning Initiative: automating and accelerating development and optimization of computing systems. Proceedings of the GCC Summit'09, Montreal, Canada, June 2009 (link)
- cTuning foundation partners
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