We continue accepting sponsorship to:
* continue developing free, public and open-source repository of optimization knowledge and universal performance/energy/size/faults/cost autotuning/benchmarking infrastructure;
* combine it with crowdsourcing and big data predictive analytics;
* support our initiatives on a new publication model, reproducible research and artifact evaluation.

News and upcoming events:

Grigori Fursin, PhD

Grigori Fursin is a British-Russian scientist with an interdisciplinary background in computer engineering, electronics, physics, mathematics, and machine learning. To be able to continue his original research on designing neural network accelerators for bio-inspired self-adaptive computers and AI systems (1994-1997), Grigori desperately needed faster, cheaper, more power efficient and reliable computer systems as well as unified mechanisms for sharing knowledge in a reproducible way across colleagues. Eventually, Grigori became one of the first researchers to radically change ad-hoc, error prone and time consuming benchmarking, optimization and co-design of computer systems across all their software and hardware layers (heterogeneous multi-core architectures, compilers, run-time libraries, applications) into a unified physics-based "big data" problem. He then effectively tackled it using statistical analysis, machine learning based autotuning (classification and predictive analytics), data mining (finding missing features), multi-versioning combined with run-time adaptation and online learning as a dynamic reaction to behavior changes, his public cTuning.org and Collective Mind repository of knowledge, infrastructure for experiment crowdsourcing using commodity mobile phones, and collective intelligence [P23, P1, M2, P2, P14, P16, P5]. All these techniques, open source research SDK and shared artifacts were used and extended in industry with IBM, ARM, Synopsys, Intel, STMicroelectronics and other companies to improve utilization of computer systems while dramatically reducing their development costs and time to market, acknowledged by IBM and Fujitsu, received international awards, and included in the EU HiPEAC long-term research vision (2012-2020). At the same time, Grigori spent considerable effort promoting sharing of all research artifacts in a reusable and reproducible way along with publications. His practical cTuning.org experience gradually helped initiate 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]. Grigori hopes that his enabling and open source technology will eventually help improve knowledge sharing as well as the quality of our research, experimentation and education while boosting innovation in science and technology. Grigori is always interested to lead related, innovative, international and interdisciplinary projects based on his long-term version.

Professional Career:
Education:
  • 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
Main achievements:
  • 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 and ADAPT (reproducible R&D).
  • 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
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, autotuning, 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
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.

Grigori's favorite story about Rutherford and Bohr: (in English, in Russian).