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Join Collective Knowledge consortium to standardize APIs and artifact meta-information for collaborative, reproducible and multi-objective SW/HW co-design of emerging workloads such as deep learning using open research SDK (CK), adapt to a Cambrian SW/HW/AI chaos and accelerate AI research!
My name is Grigori Fursin. I am the CTO and co-founder of dividiti, Chief Scientist of the cTuning Foundation (non-profit research organization), a founding member of the ACM Task Force on Data, Software, and Reproducibility in Publication, and the reproducible research evangelist. I have an interdisciplinary background in computer engineering, physics, neural networks, quantum electronics and machine learning with a PhD in computer science from the University of Edinburgh.

I lead research and development for an open Collective Knowledge platform (CK) to co-design efficient software, hardware and model stack for emerging workloads including deep learning and quantum computing in terms of speed, accuracy, energy and various costs across diverse data sets and platforms from IoT to data centers and Exascale supercomputers. CK platform helps our partners (leading companies and universities) survive in a Cambrian AI/SW/HW explosion, quickly prototype and automate AI experiments from shared and optimized components available in the CK repository similar to LEGO(tm), perform competitive analysis of new AI solutions, reduce all R&D costs, accelerate AI research, and enable efficient brain-inspired computing.

[ My R&D background and short biography ]
Professional Career:
  • 2004: PhD in computer science with ORS award from the University of Edinburgh, UK.
  • 1999: MS in computer engineering with golden medal (summa cum laude) from Moscow Insitute of Physics and Technology, Russia.
  • 1997: BS in electronics, mathematics and machine learning (summa cum laude) from Moscow Institute of Physics and Technology, Russia.
Current collaborations: See academic and industrial partners of my cKnowledge.org project
Publications: Google Scholar, ResearchGate, Microsoft Academic
Main achievements:
  • 2018: Helped initiate 1st open ML-systems tournament on reproducible and Pareto-efficient SW/HW co-design for deep learning (speed, accuracy, costs) at ACM ASPLOS
  • 2017: Received ACM CGO test of time award for my research on machine-learning based optimization (CGO'07 publication).
  • 2012-2016: Received INRIA award and fellowship for "making an outstanding contribution to research".
  • 2014-2015: Received EU TETRACOM grant to develop 4th version of a univeral machine-learning based autotuning framework and public repository for artifact sharing (Collective Knowledge).
  • 2012-2014: Developed 3rd version of a universal machine-learning based pluginized autotuning framework supporting multiple objectives including performance,energy,size and cost for a variety of kernels, codelets and large applications with OpenCL, CUDA, OpenMP, and MPI.
  • 2014-cur.: Initiated Artifact Evaluation for CGO, PPoPP, ADAPT and PACT (follow-up to my initiative on collaborative and reproducible research).
  • 2008-cur.: Established cTuning.org community-driven portal and non-profit foundation to start sharing artifacts along with publications while reusing them to crowdsource software/hardware optimization and combine it with machine learning.
  • 2007-cur.: Transferred developed technology to industry and production tools such as mainline GCC; consulted major companies on systematic and reproducible program and architecture performance tuning, run-time adaptation and co-design.
  • 2007-2010: Prepared and tought guest MS course on machine learning based optimization and run-time adaptation at the University of Paris-Sud, France.
  • 2006-2009: Led research and development of the machine-learning based self-tuning compiler (proposed to crowdsource plugin-based autotuning and combine it with predictive analytics and collective intelligence) in EU FP6 MILEPOST project considered by IBM to be the first in the world.
  • 1999-2000: Led research and development of a polyhedral source-to-source compiler together with collaborative plugin-based autotuning infrastructure and repository for memory hierarchy optimization in supercomputers within the EU MHAOTEU project.
  • 1999-2006: Prepared foundations for big data driven and machine learning based optimization, run-time adaptation and co-design of computer systems.
  • 1998-cur.: Started designing infrastructure and repository for crowdsourcing experiments and sharing results (code, data, models, interactive graphs) in a reproducible way among colleagues and workgroups.
  • 1993-1998: Designed novel semiconductor neural network accelerators for a possible brain-inspired computer (served as a motivator for machine-learning based autotuning and collaborative R&D).
Main techical knowledge (continuously acquire new ones): Linux, Windows, Android, Python, scikit, neural networks, decision trees, SVM, agile development, large-scale project management, APIs, GCC, LLVM, polyhedral optimizations, ARM compilers, Intel Compilers, Intel VTUNE, C, C++, Java, Fortran, Basic, GPU, OpenCL, CUDA, MPI, OpenMP, PHP, R, MySQL, FPGAs, ElasticSearch, Hadoop, Jenkins, html, apache2, mediawiki, drupal, OpenOffice, Eclipse, SVN, GIT, GIMP2, Adobe Photoshop, Visual Studio, Microsoft Office, Android Studio
Full academic CV: HTML; PDF
Professional memberships: ACM, HiPEAC, IEEE
LinkedIn: Link
Research twitter: Link
My favourite story about Rutherford and a student in Englishin Russian
Hobbies: traveling, discovering new cultures, gardening, active sport (football, skiing, swimming, snorkeling, climbing, jogging, ...), photography, reading
Professional e-mail: Grigori.Fursin@cTuning.org or grigori@dividiti.com
Personal e-mail: gfursin@gmail.com

Quick access to full academic CV:

"It is not the strongest of the species that survives, or the most intelligent; it is the one most capable of change" (attributed to Charles Darwin)
(see our ADAPT workshop)

"All truth passes through three stages. First, it is ridiculed. Second, it is violently opposed. Third, it is accepted as being self-evident" (Arthur Schopenhauer)

Latest news (see archive):
  •  2017.November: Our 1st open tournament on reproducible AI/SW/HW co-design (speed, accuracy, costs) was accepted for ASPLOS'18!
  •  2017.November: We have won an Innovate UK grant to extend Collective Knowledge in 2018 - more details to follow!
  •  2017.November: We opened a beta repository to help you find reusable and customizable AI artifacts
  •  2017.September: I presented CK-powered Optimisation of AI Applications Across the Whole SW/HW Stack at the ARM Research Summit'17
  •  2017.September: Microsoft sponsors my non-profit cTuning foundation
  •  2017.August: ARM presented our technology at the Embedded Vision Summit
  •  2017.June: ACM evaluates our CK technology to share experimental workflows in Digital Libraries
  •  2017.March: Our CNRS webcast on "Enabling open and reproducible research at computer systems conferences: good, bad and ugly"
  •  2017.March: Together with General Motors we released a new version of our open research AI SDK to collaboratively optimize and co-design SW/HW/model stack of deep learning and AI across diverse devices from IoT to HPC!
  •  2017.February: Our CGO'07 research paper received "test of time" award!
  •  2017.February: The distinguished artifact at the CGO'17 was implemented using our CK framework - see it at GitHub!
  •  2017.February: We co-authored ACM's policy on Result and Artifact Review and Badging and prepared Artifact Appendices now used at SuperComputing'17!
  •  2016.October: We presented our collaborative approach to workloads benchmarking at ARM TechCon'16 (Oct.27, Santa Clara, USA)
  •  2016.October: We updated list of CK-powered open R&D challenges in computer engineering
  •  2016.June: Congratulations to Abdul Memon (my last PhD student) for successfully defending his thesis "Crowdtuning: Towards Practical and Reproducible Auto-tuning via Crowdsourcing and Predictive Analytics" in the University of Paris-Saclay. Most of the software, data sets and experiments are not only reproducible but also shared as reusable and extensible components via Collective Mind and CK!
  •  Recent publications with my long-term vision: [DATE'16 (with artifacts), CPC'15 (with artifacts), Scientific Programming'14 (with artifacts), TRUST@PLDI'14].
Check out our:
Success stories:
  • Our CGO'07 paper received "test of time award" at CGO'17! (2017)!
  • ARM and dividiti made a press-release about my Collective Knowledge Technology [ PDF (page 17) ] (2016)!
  • I started crowd-tuning campaign to crowd-benchmark deep learning algorithms and crowdsource GCC/LLVM tuning while combining it with active learning across diverse data sets and platforms including mobile devices and cloud services provided by volunteers using CK framework. You can see latest crowd-results in our live repository! (2016)!
  • We successfully initiated community-driven pre-reviewing and validation of publications and artifacts for workshops and conferences - see ADAPT 2016 workshop.
  • After sharing all my artifacts and promoting collaborative research since 2007, I helped initiate Artifact Evaluation for PPoPP, CGO, PACT, RTSS and SC conferences. Here is our motivation: (paper, wiki).
  • Ed Plowman (director of performance analysis strategy in ARM) suggests to contribute to Collective Knowledge and workload automation [ Slides ]!
  • I have received HiPEAC technology transfer award for my novel Collective Knowledge framework..
  • My cTuning technology was referenced by Fujitsu as closely related to their long-term initiative on "big data" driven optimization of Exascale computer systems (2014).
  • My cTuning technology demonstrated the possibility to fully automate construction of compiler optimization heuristics for multi-core reconfigurable systems using machine learning and crowdsourcing - it is considered by IBM to be the first in the world (2009)
  • I extended cTuning-based technology to develop customized "in house" repository of knowledge when helping to establish Intel Exascale Lab in France (2010-2011)
  • My plugin-based compiler interface and technique to enable autotuning and run-time adaptation for statically compiled programs was added to mainline GCC (4.6+ and 4.8+ respectively) sponsored by Google [ Details ] (2008-2010)
All success stories ]

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