CTuning foundation explained

The cTuning Foundation
Type:Non-profit research and development organization, Engineering organization
Founder:Grigori Fursin
Location:Cachan
Registration Id:W943003814
Origins:Collective Tuning Initiative & Milepost GCC
Area Served:Worldwide
Focus:Collaborative software, Open Science, Open Source Software, Reproducibility, Computer Science, Machine learning, Artifact Evaluation, Performance tuning, Knowledge management
Method:Develop open-source tools, a public repository of knowledge, and a common methodology for collaborative and reproducible experimentation

The cTuning Foundation is a global non-profit organization developing a common methodology and open-source tools to support sustainable, collaborative and reproducible research in Computer science and organize and automate artifact evaluation and reproducibility inititiaves at machine learning and systems conferences and journals.[1]

Notable projects

History

Grigori Fursin developed cTuning.org at the end of the Milepost project in 2009to continue his research on machine learning based program and architecture optimization as a community effort.[7] [8]

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 the Collective Knowledge Frameworkand prepare reproducible research methodology for ACM and IEEE conferences.[9]

In 2020, cTuning Foundation joined MLCommons as a founding member to accelerate innovation in ML.[10]

In 2023, cTuning Foundation joined the new initiative by the Autonomous Vehicle Computing Consortium and MLCommons to develop an automotive industry standard machine learning benchmark suite.[11]

Since 2024, cTuning Foundation supports the MLCommons Croissant Metadata Format to help standardize ML Datasets.[12]

Funding

Current funding comes from the European Union research and development funding programme, Microsoft, and other organizations.[13]

Notes and References

  1. Web site: ACM TechTalk "Reproducing 150 Research Papers and Testing Them in the Real World: Challenges and Solutions with Grigori Fursin" . 11 February 2021.
  2. Grigori . Fursin . Grigori Fursin . Toward a common language to facilitate reproducible research and technology transfer: challenges and solutions . keynote at the 1st ACM Conference on Reproducibility and Replicability . June 2023 . 10.5281/zenodo.8105339 .
  3. Grigori . Fursin . Grigori Fursin . Collective Knowledge: organizing research projects as a database of reusable components and portable workflows with common interfaces . . October 2020 . 10.1098/rsta.2020.0211 . 22 October 2020. 2011.01149 .
  4. Grigori . Fursin . Grigori Fursin . Bruce Childers . Alex K. Jones . Daniel Mosse . TRUST'14 . 10.1145/2618137 . . June 2014.
  5. Grigori . Fursin . Grigori Fursin . Christophe Dubach . Community-driven reviewing and validation of publications . 10.1145/2618137.2618142 . Proceedings of TRUST'14 at PLDI'14 . June 2014. 1406.4020 .
  6. Bruce R . Childers . Grigori Fursin . Shriram Krishnamurthi . Andreas Zeller . Artifact evaluation for publications . 10.4230/DagRep.5.11.29 . Dagstuhl Perspectives Workshop 15452 . March 2016. free .
  7. World's First Intelligent, Open Source Compiler Provides Automated Advice on Software Code Optimization, IBM press-release, June 2009 (link)
  8. 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)
  9. Article on TTP project "COLLECTIVE KNOWLEDGE: A FRAMEWORK FOR SYSTEMATIC PERFORMANCE ANALYSIS AND OPTIMIZATION", HiPEACinfo, July 2015(link)
  10. MLCommons press-release: "MLCommons Launches and Unites 50+ Global Technology and Academic Leaders in AI and Machine Learning to Accelerate Innovation in ML" (link)
  11. AVCC press-release: "AVCC and MLCommons Join Forces to Develop an Automotive Industry Standard Machine Learning Benchmark Suite" (link)
  12. MLCommons press-release: "New Croissant Metadata Format helps Standardize ML Datasets. Support from Hugging Face, Google Dataset Search, Kaggle, and Open ML, makes datasets easily discoverable and usable." (link)
  13. cTuning foundation partners