Soon, we will be accepting academic and industrial (international) members and sponsors interested to build public repository of optimization knowledge, universal auto-tuning/benchmarking infrastructure, combine it with crowdsourcing and predictive analytics, and support initiatives on reproducible research and artifact evaluation. We are also looking for universities interested to host our lab together with advanced MS and PhD courses. Please, contact Grigori Fursin (CTO) for more details!

News and upcoming events:

Grigori Fursin, PhD

Grigori Fursin successfully used his interdisciplinary background in computer engineering, electronics, physics, mathematics, and machine learning to radically change ad-hoc, error prone, time consuming and costly process of benchmarking, optimizing and co-designing computer systems across all software and hardware layers (heterogeneous multi-core architectures, compilers, run-time libraries, applications) for minimal execution time, power consumption, failures and other costs into a unified big data problem [P23, P1, M2, P2, P14, P16, P5]. He then started tackling, systematizing and speeding it up using his public cTuning.org and Collective Mind repository of knowledge, common plugin-based application and compiler auto-tuning infrastructure, statistical analysis, machine learning (classification and predictive analytics), data mining (finding missing features), run-time adaptation, brain-inspired self-optimization, crowdsourcing using commodity mobile phones, social networking and collective intelligence. As a side effect, Grigori's research initiated new publication model in computer engineering where experimental results and all research artifacts are continuously shared, discussed, reproduced and improved by the community [E1,E2,P2]. This research was validated in industry, received several international and national awards, and helped to improve utilization of computer systems while dramatically reducing their development costs and time to market. We hope it will help improve our society by boosting innovation in science and technology.
Current position(s):
Main achievements:
  • 2012-2016: Received INRIA award and fellowship for "making an outstanding contribution to research".
  • 2014-cur.: Initiated Artifact Evaluation for CGO and PPoPP (reproducible R&D).
  • 2014-cur.: Registered non-profit cTuning foundation to continue collaborative and reproducible R&D in computer engineering.
  • 2008-2014: Established cTuning.org community-driven portal for machine-learning based program optimization.
  • 2007-2015: 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.
  • 2007-2014: Held Adjunct professor position at the University of Paris-Sud, France while regularly giving guest lectures in the UK, USA, Canada, Russia, China, and other countries.
  • 2006-2009: Led research and development of the machine-learning based self-tuning compiler (proposed to crowdsource plugn-based auto-tuning and combine it with predictive analytics and collective intelligence) in EU FP6 MILEPOST project considered by IBM to be the first in the world.
  • 1999-2006: Prepared foundations for big data driven and machine learning based optimization, run-time adaptation and co-design of computer systems.
  • 1993-1998: Designed novel semiconductor neural network accelerators for a possible brain-inspired computer.
Main interests and expertise: Research and development:
  • developing public framework and repository to preserve, organize, describe, cross-link, share and reuse any knowledge (code, data, experimental results)
  • developing adaptive, self-tuning computer systems that can automatically adapt all their software and hardware layers to any user task while minimizing for minimal execution time, power consumption, failures and other costs
  • developing new techniques to speed up multi-objective SW/HW optimization, dynamic adaptation and co-design using big data analytics (statistical analysis, data mining, machine learning) and crowdsourcing
  • evangelizing and enabling collaborative and reproducible research in computer engineering
  • promoting new community-driven reviewing of publications and artifacts via SlashDot, Reddit, etc
  • investigating biologically and brain-inspired systems (combining predictive analytics, neural networks, AI, physics, electronics)

Management:

  • preparing and leading challenging, long term, interdisciplinary R&D projects
  • building and leading teams of researchers and developers

Transfer to industry:

  • consulting companies on cTuning-related technology (knowledge management, reproducible experimentation, auto-tuning, machine learning)
  • moving technology to production design and optimization tool chains including open source GCC and LLVM compilers
  • setting up joint industrial and academic laboratories
Education: PhD in computer science with ORS award from the University of Edinburgh, UK (2004)
Professional memberships: ACM, HiPEAC, IEEE
Full academic CV: HTML; PDF
LinkedIn: Link
Research twitter: Link
EMail: grigori.fursin@cTuning.org

Quick access to cM-generated Curriculum Vitae:

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Grigori Fursin is a computer scientist, engineer and manager with an interdisciplinary background in computer engineering, electronics, physics, mathematics, and machine learning, and with more than 20 years of research and development experience. He is always interested to lead highly challenging, innovative and interdisciplinary research projects particularly related to a development of faster, smaller, cheaper, more power efficient and reliable self-tuning computer systems; collaborative discovery, systematization, sharing and reuse of knowledge; machine learning; big data analytics; artificial brain and collective intelligence.

In 1993, at the age of 16, Grigori joined his first official R&D project as a Research Assistant designing and optimizing semiconductor neural network accelerators for a possible brain-inspired supercomputer computer [M9, M8]. After tedious attempts to optimize and parallelize his neural network modeling software for several supercomputers, Grigori decided to switch to computer engineering to automate and systematize this process using his interdisciplinary knowledge. Eventually, Grigori was one of the first researchers to radically change ad-hoc, error prone, time consuming and costly process of designing, benchmarking and optimizing the next generation of computer systems across all software and hardware layers into a unified big data problem [P1,
P1, M2, P23, P14, P16, P5]. He then started tackling, systematizing and speeding it up using his open source plugin-based cTuning and Collective Mind multi-dimensional and multi-objective auto-tuning infrastructure and repository, statistical analysis, machine learning (classification and predictive analytics), data mining (finding missing features), run-time adaptation with static multi-versioning, adaptive exploration of large optimization spaces, online tuning, differential analysis, crowdsourcing using commodity mobile phones and collective intelligence.

Besides publishing in major conferences and journals including PLDI, MICRO, CGO, TACO, IJPP, CASES and HiPEAC, Grigori spends considerable effort to release all his code and data including tools, benchmarks, data sets and predictive models along with his articles at cTuning.org and c-mind.org/repo to ensure reproducibility. As a side effect, this approach initiated a new open publication model in computer engineering where experimental results and all research artifacts are continuously shared along with articles to be validated and improved by the community [P2, E1,E2,P2, M1,E9,P5].
Eventually, most of my techniques became a mainstream, have been included into mainline GCC, validated and extended by industry with IBM, ARC (Synopsys), Intel, Google, STMicroelectronics and ARM. These techniques enabled practical open-source machine learning based self-tuning compiler (MILEPOST GCC) [M3,R3,R1] considered by IBM to be the first in the world [P22]. They also dramatically reduced development costs and time to market of the new embedded reconfigurable devices from ARC (Synopsys) while improving their performance, power consumption, time, size and ROI [X8]. Finally, Grigori's techniques were included in the European Union's long-term IT research vision for 2012-2020 [X7, X2] In 2008, Grigori established international nonprofit cTuning.org foundation for community-driven systematization, automation and acceleration of the design, benchmarking and optimization of all existing computer systems across the whole software and hardware stack using his repository of knowledge, open source tools and big data analytics. My foundation and community-driven approach has been referenced in press-releases from IBM in 2008-2009 and from Fujitsu in 2014.

Grigori delivered more than 60 regular and invited talks, lectures and keynotes in the major international companies and universities in Europe, USA, China, Canada and Russia; founded SMART and ADAPT workshops that ran consecutively for 8 years sponsored by Google, NVidia, Intel and Microsoft; prepared and taught advanced MS course on future self-tuning computer systems in the Paris South University. In 2010-2011, he was on industrial leave invited to help establish Intel Exascale Lab in France while preparing long-term research directions and serving as the head of program optimization and characterization group [I1]. In 2012, Grigori rejoined INRIA and received a personal award and 4-year fellowship for "making an outstanding contribution to research" [A2]. Grigori hopes his research will help boost innovation in science and technology.

Keywords: Collective Mind, program and architecture crowdtuning, plugin-based auto-tuning, machine learning, data mining, public repository of knowledge, big data, crowdsourcing, compilers, online tuning, run-time adaptation, software and hardware co-design, electronic brain, neural networks, predictive analytics, statistical analysis, feature analysis, decremental analysis, decremental characterization, complexity reduction, reproducible research, crowdsourcing experimentation, cTuning.org, c-mind.org, agile research and development, knowledge systematization, emerging information technologies, knowledge transfer to industry, consulting, startups


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