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        menuc1.gif Summary menud1.gif menua1.gif PUBLICATIONS/Talks menub1.gif menuc1.gif Developments menud1.gif menuc1.gif Teaching menud1.gif menuc1.gif Resources menud1.gif  

Here you can find list of my publications, presentations, lectures and organized events:

Contents

Publications

[ Link to Microsoft Research ] [ Link to Google Scholar ] [ Link to DBLP ]

  1. [CFHP2012] Yang Chen, Shuangde Fang, Yuanjie Huang, Lieven Eeckhout, Grigori Fursin, Liang Peng, Olivier Temam, Chengyong Wu. Deconstructing Iterative Optimization..

    To appear in the next ACM TACO journal.

  2. [FKMP2011] Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon, Zbigniew Chamski, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Bilha Mendelson, Ayal Zaks, Eric Courtois, Francois Bodin, Phil Barnard, Elton Ashton, Edwin Bonilla, John Thomson, Chris Williams, Michael O'Boyle. MILEPOST GCC: machine learning enabled self-tuning compiler.
    International Journal of Parallel Programming (IJPP), June 2011, Volume 39, Issue 3, pages 296-327

    Concept is included in the HiPEAC 2012-2020 research roadmap.

    [bib] [Springer online final version] [author's pdf]

  3. [FHMP2011] Grigori Fursin, Robert Hundt, Jason Mars, Yuriy Kashnikov. Introducing ACM SIGPLAN International Workshop on Adaptive Self-Tuning Computing Systems for the Exaflop Era (http://exadapt.org).
    ACM International Conference Proceeding Series, co-located with PLDI, June 2011, San Jose, USA

    [ACM DL online version]

  4. [FT2010] Grigori Fursin and Olivier Temam. Collective Optimization: A Practical Collaborative Approach.
    ACM Transactions on Architecture and Code Optimization (TACO), December 2010, Volume 7, Number 4, pages 20-49

    Concept is included in the HiPEAC 2012-2020 research roadmap.

    [bib] [ACM DL final version] [author's pdf]

  5. [MCFP2010] Mircea Namolaru, Albert Cohen, Grigori Fursin, Ayal Zaks and Ari Freund. Practical Aggregation of Semantical Program Properties for Machine Learning Based Optimization.
    Proceedings of the International Conference on Compilers, Architecture, And Synthesis For Embedded Systems (CASES 2010), October 2010, Scottsdale, AZ, USA

    [bib] [pdf]

  6. [YYLP2010] Yang Chen, Yuanjie Huang, Lieven Eeckhout, Grigori Fursin, Liang Peng, Olivier Temam, Chengyong Wu. Evaluating Iterative Optimization across 1000 Data Sets.
    Proceedings of the ACM SIGPLAN 2010 Conference on Programming Language Design and Implementation (PLDI 2010), June 2010, Toronto, Canada (acceptance rate: 20%, 41/204)

    HiPEAC paper award.

    [bib] [pdf]

  7. [HPWP2010] Yuanjie Huang, Liang Peng, Chengyong Wu, Yuriy Kashnikov, Jörn Renneke, and Grigori Fursin. Transforming GCC into a research-friendly environment: plugins for optimization tuning and reordering, function cloning and program instrumentation.
    2nd International Workshop on GCC Research Opportunities (GROW’10) co-located with HiPEAC'10, Pisa, Italy, January 2010 (acceptance rate: 57%, 8/14)

    [bib] [pdf] [pdf backup]

  8. [FT2009] Grigori Fursin and Olivier Temam. Collective optimization.
    Proceedings of the International Conference on High Performance Embedded Architectures & Compilers (HiPEAC 2009), Paphos, Cyprus, January 2009 (acceptance rate: 28%, 27/97)

    Extended version is now published in ACM TACO (FT2010).

    [bib] [pdf]

  9. [DJBP2009] Christophe Dubach, Timothy M. Jones, Edwin V. Bonilla, Grigori Fursin, and Michael F.P. O'Boyle. Portable Compiler Optimization Across Embedded Programs and Microarchitectures using Machine Learning.
    Proceedings of the IEEE/ACM International Symposium on Microarchitecture (MICRO), New York, USA, December 2009 (acceptance rate: 25%, 52/209)

    HiPEAC paper award.

    Christophe Dubach received BCS/CPHC Distinguished Dissertation Award’09 for his related thesis "Using Machine-Learning to Efficiently Explore the Architecture/Compiler Co-Design Space" supervised by Prof. Michael O'Boyle.

    [bib] [pdf]

  10. [Fur2009] Grigori Fursin. Collective Tuning Initiative: automating and accelerating development and optimization of computing systems.
    Proceedings of the GCC Summit'09, Montreal, Canada, June 2009

    This paper introduces collective tuning infrastructure (http://cTuning.org) and repository (http://cTuning.org/cdatabase) to start continuous parameterization of all computing systems and to automate, simplify and systematize code and architecture design, characterization and optimization. Collecting enough data about various architectures, compilers, programs, benchmarks, kernels and datasets will help to quickly predict better program optimizations or architecture designs using machine learning techniques thus considerably reducing time to market and enabling self-tuning, adaptive computing systems.

    [bib] [pdf] [pdf backup]

  11. [TOFP2009] John Thomson, Michael O'Boyle, Grigori Fursin and Björn Franke. Reducing Training Time in a One-shot Machine Learning-based Compiler.
    Proceedings of the 22nd International Workshop on Languages and Compilers for Parallel Computing (LCPC'09), Newark, Delaware, USA, October 2009

    [bib] [pdf]

  12. [LCWP2009] Lianjie Luo, Yang Chen, Chengyong Wu, Shun Long and Grigori Fursin. Finding representative sets of optimizations for adaptive multiversioning applications.
    3rd International Workshop on Statistical and Machine Learning Approaches Applied to Architectures and Compilation (SMART'09) co-located with HiPEAC'09, Paphos, Cyprus, January 2009 (acceptance rate=62%, 8/13)

    [bib] [pdf] [pdf backup] [presentation]

  13. [JGVP2009] Victor Jimenez, Isaac Gelado, Lluis Vilanova, Marisa Gil, Grigori Fursin and Nacho Navarro. Predictive runtime code scheduling for heterogeneous architectures.
    Proceedings of the International Conference on High Performance Embedded Architectures & Compilers (HiPEAC 2009), Paphos, Cyprus, January 2009 (acceptance rate: 28%, 27/97)

    I obtained a HiPEAC collaborative grant to develop this technique with Victor Jimenez.

    Similar approaches for gluing/adapting applications for heterogeneous architectures are used in Intel’s Qilin and in CAPS Entreprise’s HMPP.

    [bib] [pdf] [presentation]

  14. [LF2009] Shun Long and Grigori Fursin. Systematic search within an optimisation space based on Unified Transformation Framework.
    International Journal of Computational Science and Engineering (IJCSE), Vol.4, No.2, pages 102-111, 2009 (submitted in 2005)

    [bib] [pdf]

  15. [FMTP2008] Grigori Fursin, Cupertino Miranda, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Ayal Zaks, Bilha Mendelson, Phil Barnard, Elton Ashton, Eric Courtois, Francois Bodin, Edwin Bonilla, John Thomson, Hugh Leather, Chris Williams, Michael O'Boyle. MILEPOST GCC: machine learning based research compiler.
    Proceedings of the GCC Developers' Summit, Ottawa, Canada, June 2008

    Extended version is now published in IJPP (FKMP2011).

    [bib] [pdf] [pdf backup]

  16. [DFGP2007] Veerle Desmet, Grigori Fursin, Sylvain Girbal and Olivier Temam. Leveraging Modular Simulation for Systematic Design Space Exploration.
    4th HiPEAC Industrial Workshop on Compilers and Architectures organized by ARM Ltd., Cambridge, UK, November 2007

    [bib]

  17. [LCFP2007] Piotr Lesnicki, Albert Cohen, Grigori Fursin, Marco Cornero, Andrea Ornstein and Erven Rohou. Split Compilation: an Application to Just-in-Time Vectorization.
    International Workshop on GCC for Research in Embedded and Parallel Systems (GREPS'07) in conjunction with PACT'07, Brasov, Romania, September 2007

    [bib] [pdf] [pdf backup]

  18. [LFF2007] Shun Long, Grigori Fursin, Björn Franke. A Cost-Aware Parallel Workload Allocation Approach based on Machine Learning Techniques.
    Proceedings of the IFIP International Conference on Network and Parallel Computing (NPC 2007), LNCS-4672, pages 506-515, Dalian, China, September 2007

    [bib] [pdf]

  19. [FMPP2007] Grigori Fursin, Cupertino Miranda, Sebastian Pop, Albert Cohen and Olivier Temam. Practical Run-time Adaptation with Procedure Cloning to Enable Continuous Collective Compilation.
    Proceedings of the GCC Developers' Summit, Ottawa, Canada, July 2007

    [bib] [pdf]

  20. [DCFP2007] Christophe Dubach, John Cavazos, Björn Franke, Grigori Fursin, Michael O'Boyle and Oliver Temam. Enabling fast compiler optimization evaluation via code-features based performance predictor.
    Proceedings of the ACM International Conference on Computing Frontiers, Ischia, Italy, May 2007 (acceptance rate=50%,28/56)

    [bib] [pdf]

  21. [CFAP2007] John Cavazos, Grigori Fursin, Felix Agakov, Edwin Bonilla, Michael F.P.O'Boyle and Olivier Temam. Rapidly Selecting Good Compiler Optimizations using Performance Counters.
    Proceedings of the 5th Annual International Symposium on Code Generation and Optimization (CGO), San Jose, USA, March 2007

    [bib] [pdf]

  22. [FC2007] Grigori Fursin and Albert Cohen. Building a Practical Iterative Interactive Compiler.
    1st International Workshop on Statistical and Machine Learning Approaches Applied to Architectures and Compilation (SMART'07) co-located with HiPEAC'07, Ghent, Belgium, January 2007 (acceptance rate=58%, 7/12)

    More info is now available in the extended version published in IJPP (FKMP2011).

    [bib] [pdf]

  23. [FCOP2007] Grigori Fursin, John Cavazos, Michael O'Boyle and Olivier Temam. MiDataSets: Creating The Conditions For A More Realistic Evaluation of Iterative Optimization.
    Proceedings of the International Conference on High Performance Embedded Architectures & Compilers (HiPEAC 2007), Ghent, Belgium, January 2007 (acceptance rate=29%)

    [bib] [pdf]

  24. [FCOP2006] Grigori Fursin, Albert Cohen, Michael O'Boyle and Oliver Temam. Quick and practical run-time evaluation of multiple program optimizations.
    Transactions on High-Performance Embedded Architectures and Compilers, 1(1), pages 13-31, 2006

    [bib] [pdf] [pdf backup]

  25. [CDAP2006] John Cavazos, Christophe Dubach, Felix Agakov, Edwin Bonilla, Michael F.P. O'Boyle, Grigori Fursin and Olivier Temam. Automatic Performance Model Construction for the Fast Software Exploration of New Hardware Designs.
    Proceedings of the International Conference on Compilers, Architecture, And Synthesis For Embedded Systems (CASES 2006), Seoul, Korea, October 2006 (acceptance rate=41%, 41/100)

    finalist best paper award

    [bib] [pdf]

  26. [ABCP2006] F. Agakov, E. Bonilla, J. Cavazos, B. Franke, G. Fursin, M.F.P. O'Boyle, J. Thomson, M. Toussaint and C.K.I. Williams. Using Machine Learning to Focus Iterative Optimization.
    Proceedings of the 4th Annual International Symposium on Code Generation and Optimization (CGO), New York, NY, USA, March 2006 (acceptance rate=36%, 29/80)

    best presentation award

    See also our publication on MILEPOST GCC in IJPP (FKMP2011).

    [bib] [pdf]

  27. [FCOP2005] Grigori Fursin, Albert Cohen, Michael O'Boyle and Oliver Temam. A Practical Method For Quickly Evaluating Program Optimizations.
    Proceedings of the 1st International Conference on High Performance Embedded Architectures & Compilers (HiPEAC 2005), number 3793 in LNCS, pages 29-46, Barcelona, Spain, November 2005

    highest ranked paper, acceptance rate=20%,17/84

    This paper presents a novel concept to statically enable run-time optimizations and self-tuning binaries through function cloning and integrated low-overhead program/system behaviour monitoring routines. It has been referenced in patents and extended in academia and industry. For the first time, we utilized Interactive Compilation Interface for PathScale compiler with loop vectorization, tiling, unrolling, interchange, fission/fusion, pipelining, prefetching and array padding to make static binaries adaptable and reactive to various environments and run-time behaviour that is important to improve efficiency and cost of various HPC systems. Since 2007 it is being extended by Google for data centers (cloud computing) and by Intel Exascale Lab (France) to build adaptive applications and reconfigure processors at run-time to save power.

    Extensions for transparent collective optimization is available in our ACM TACO publication (FT2010).

    [bib] [pdf] [pdf backup]

  28. [FOTP2005] B. Franke, M. O'Boyle, J. Thomson and G. Fursin. Probabilistic Source-Level Optimisation of Embedded Systems Software.
    Proceedings of the Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES'05), pages 78-86, Chicago, IL, USA, June 2005 (acceptance rate=26%,25/95)

    [bib] [pdf]

  29. [LF2005] Shun Long and Grigori Fursin. A heuristic search algorithm based on Unified Transformation Framework.
    Proceedings of the 7th International Workshop on High Performance Scientific and Engineering Computing (HPSEC-05), pages 137-144, Oslo, Norway, June 2005

    [bib] [pdf]

  30. [FOK2005] Grigori Fursin, Michael O'Boyle and Peter Knijnenburg. Evaluating Iterative Compilation. Lecture Notes in Computer Science, Volume 2481, pages 362-376, 2005

    [bib] [Springer Online version] [author's pdf]
  31. [FUR2004] Grigori Fursin. Iterative Compilation and Performance Prediction for Numerical Applications.
    Ph.D. thesis, University of Edinburgh, Edinburgh, UK, January 2004

    Based on FOTP2001, FOK2002, FOTP2004.

    In this thesis I introduced a novel and simple approach to quickly detect realistic lower bound of the execution time of memory intensive applications by modifying individual assembler instructions including converting array accesses to scalars in various ways without preserving the semantics of the code while avoiding code crashing to be able to directly compare original and transformed programs. I evaluated this technique on in-order Alpha and out-of-order Intel processors (I wrote my own tools for assembler analsys, instrumentation and modification for a range of platforms) and a variety of small kernels and large SPEC applications, and showed that this technique can be effectively used to detect parts of a program which can benefit from memory optimizations (and avoid wasting time for long iterative or manual optimizations on a code that do not have any potential for further optimizations). I also showed that focusing on code optimizations that reduce cache misses on out-of-order architecture using hardware counters can be totally misleading and my approach provides more realistic upper bound.

    I also wrote my own source-to-source Fortran and C transformation tool and showed how to automate program optimization using empirical iterative compilation based on array padding, loop tiling and unrolling combined with random and hill-climbing search strategies that beat state-of-the-art commercial COMPAQ/HP and Intel compilers on various SPEC2000 benchmarks. All these techniques and tools have been actively used and extended in academia and industry as well as motivating development of various R&D tools.

    [bib] [pdf] [pdf backup] [EOS software]

  32. [FOTP2004] Grigori Fursin, Mike O'Boyle, Olivier Temam, and Gregory Watts. Fast and Accurate Method for Determining a Lower Bound on Execution Time.
    Concurrency Practice and Experience, 16(2-3), pages 271-292, 2004

    In this paper, I introduced a novel concept to characterize programs through semantically non-equivalent changes (for example, converting arrays into scalars to determine computational and memory execution bounds). This work has been considerably extended in my thesis to focus optimizations and is now actively used in Intel Exascale Lab in France.

    [bib] [pdf]

  33. [FOK2002] G.G.Fursin, M.F.P.O'Boyle, and P.M.W. Knijnenburg. Evaluating Iterative Compilation.
    Proceedings of the 15th Workshop on Languages and Compilers for Parallel Computing (LCPC'02), College Park, MD, USA, pages 305-315, 2002

    This paper introduces a concept of empirical optimization (iterative compilation) of large applications to automatically adapt them to a given hardware using several basic search strategies. Our approach considerably outperformed state-of-art compilers on Intel, Alpha and several other popular architectures for several large SPEC applications. This technique has also laid the foundations for further research on focused optimizations using statistical techniques, machine learning and run-time adaptation.

    [bib] [pdf]

  34. [FOTP2001] Grigori Fursin, Mike O'Boyle, Olivier Temam, and Gregory Watts. Fast and Accurate Method for Determining a Lower Bound on Execution Time.
    Proceedings of the International Workshop on Compilers for Parallel Computers (CPC'01), pages 163-172, Edinburgh, 2001

    [bib] [pdf]

  35. [ATAP2000] Abella, J., S. A. Ali Touati, A. Anderson, C. Ciuraneta, J. M. Codina, Min Dai, C. Eisenbeis, G. Fursin, A. Gonzalez, J. Llosa, M. O'Boyle, A. Randrianatoavina, J. Sanchez, O. Temam, X. Vera, and G. Watts. MHAOTEU Tools for Memory Hierarchy Management.
    Proceedings of the 16th IMACS World Congress on Scientific Computation, Applied Mathematics and Simulation (IMACS'2000), Lausanne, Switzerland, August 2000.

Technical reports, national conferences and miscellaneous

  1. [FOTP2001] Grigori Fursin, Mike O’Boyle, Olivier Temam, and Gregory Watts. A Fast and Accurate Evaluation of a Memory Performance Upper-Bound.
    Report for the MHAOTEU ESPRIT project No 24942, February, 2001

  2. [ABBP2001] Jaume Abella, Cédric Bastoul, Jean-Luc Béchennec, Nathalie Drach, Christine Eisenbeis, Paul Feautrier, Björn Franke, Grigori Fursin, Antonio Gonzalez, Toru Kisku, Peter Knijnenburg, Josep Llosa, Michael O'Boyle, Julien Sébot, and Xavier Vera. Guided Transformations.
    Report M3.D2 for the MHAOTEU ESPRIT project No 24942, February 2001

  3. [AFGP2001] Jaume Abella, Grigori Fursin, Antonio Gonzalez, Josep Llosa, Michael O'Boyle, Abhishek Prabhat, Olivier Temam, Sid Ahmed Ali Touati, Xavier Vera, and Gregory Watts. Advanced Performance Analysis.
    Report M3.D2 for the MHAOTEU ESPRIT project No 24942, February, 2001.

  4. [FUR1997] Grigori Fursin. Simulation of processes of learning and recognition in modified neural network.
    Proceedings of the national conference on physical processes in devices of electronic and laser engineering, Moscow Institute of Physics & Technology, pages 102-111, Moscow, Russia, 1997

  5. [FUR1997] Grigori Fursin. Measurement of characteristics of neural elements with the aid of personal computer.
    Proceedings of the national conference on devices of electronic and laser engineering, Moscow Institute of Physics & Technology, pages 20-28, Moscow, Russia, 1997

  6. [FUR1995] Grigori Fursin. Restoration of symbols with noise by neural network.
    Proceedings of the national conference on physical processes in devices of electronic and laser engineering, Moscow Institute of Physics & Technology, pages 112-117, Moscow, Russia, 1995

Presentations

  • Collective Tuning Initiative: collective optimization, run-time adaptation and machine learning
    presented at University of Illinois at Urbana Champaign, USA, April 2009 (invited talk)

    [pdf]
  • Collective Optimization
    presented at HiPEAC'09, Cyprus, January 2009
  • Finding representative sets of optimizations for adaptive multiversioning applications.
    presented at SMART'09, Cyprus, January 2009
  • MILEPOST project - using machine learning to automate and speed up program optimization for reconfigurable processors
    presented at the Information and Brokerage Conference on Information and Communication Technologies in the EU's 7th Framework, Moscow, Russia, October 2008 (invited talk)
  • Enabling Dynamic Optimization and Adaptation for Statically Compiled Programs Using Function Multi-versioning
    presented at ScalPerf'08, Italy, September 2008 (invited talk)
  • MILEPOST GCC: machine learning based research compiler.
    presented at the GCC Developers' Summit, Ottawa, Canada, June 2008
  • Continuous Adaptive Program Optimizations
    presented at Imperial College, London, UK, February 2008; (invited talk)
    presented at the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, January 2008 (invited talk)
  • Practical Run-time Adaptation with Procedure Cloning to Enable Continuous Collective Compilation.
    presented at the GCC Developers' Summit, Ottawa, Canada, July 2007
  • Machine learning techniques for program optimizations, run-time adaptation and design space exploration
    presented at LRI, Paris South University, INRIA and CNRS, France, June 2007; (invited talk)
    presented at Intel Compiler Group, Moscow, Russia, February 2007 (invited talk);
    presented at the Institute for System Programming, Russian Academy of Sciences, Moscow, Russia, February 2007 (invited talk)
  • Continuous run-time adaptation and optimization of statically compiled programs
    presented at the UPC, Barcelona, Spain, January 2007 (invited talk)

    [pdf]
  • Continuous collective compilation
    presented at the ICSA seminar, University of Edinburgh, UK, December 2006 (invited talk);
    presented at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, March 2006 (invited talk);
    presented at the MilePost EC negotiation meeting, European Commission, Brussels, March 2006

Teaching/organizing courses

  • 2007-2008 = Continuous adaptive iterative compilation and machine learning techniques, part of postgraduate M2R course at LRI, Paris South University, France
  • 2006-2007 = Continuous adaptive iterative compilation and machine learning techniques, part of postgraduate M2R course at LRI, Paris South University, France
  • 2005-2006 = Adaptive and feedback driven compilation, part of postgraduate M2R course at LRI, Paris South University, France

Reviewing

  • HiPEAC long-term research vision 2010 (writing and reviewing)
  • Springer-Verlag book on autotuning techniques (on-going)
  • CGO, PACT, TPDS, CASES, ICS, DATES, HiPEAC, CPC, IJHPSA, ICPADS, JPDC, CLSS, iWAPT, Open64, SMART, GROW

PC member; event organizer

2013

  • CC 2013: PC member

2012

2011

  • EXADAPT 2011 (at PLDI 2011/FCRC 2011): Organizer together with Robert Hundt from Google (ACM SIGPLAN 1st International Workshop on Adaptive Self-Tuning Computing Systems for the Exaflop Era, Keynote on "Autotuning in the Exascale Era": Prof. Katherine Yelick (LBNL and UC Berkeley, USA) )
  • SMART 2011 (at CGO 2011): Organizer (5th Workshop on Statistical and Machine Learning Approaches applied to Architectures and Compilation, Chair: Prof. Francois Bodin (CAPS Entreprise, France), Keynote on "Automatic Performance Tuning and Machine Learning": Prof. Markus Püschel (ETH Zürich, Switzerland) )
  • CGO'11: PC member (International Symposium on Code Generation and Optimization) and session chair on dynamic compilation
  • GROW'11 (at CGO 2011): PC member (3rd International Workshop on GCC Research Opportunities)

2010

  • GROW'10 (at HiPEAC 2010): co-organizer and editor of informal proceedings (2nd International Workshop on GCC Research Opportunities)
  • SMART’10 (at HiPEAC 2010): co-organizer and editor of informal proceedings (4th Workshop on Statistical and Machine Learning Approaches applied to Architectures and Compilation, Chair: Prof. David Whalley, Keynote on "Moving adaptation into individual optimizations": Prof. Keith Cooper (Rice University, USA) )
  • Speedup-Test’10 tutorial (at HiPEAC 2010): co-organizer (Statistical Methodology to Evaluate Program Speedups and their Optimization Techniques)

2009

2008

  • IPDPS’08: PC member (IEEE International Parallel and Distributed Processing Symposium)
  • SMART’08 (at HiPEAC 2008): PC member (2nd Workshop on Statistical and Machine Learning Approaches applied to Architectures and Compilation, Chair: Prof. Michael O'Boyle)

2007

  • CASES’07: PC member (International Conference on Compilers, Architecture, and Synthesis for Embedded Systems)
  • 2nd GCC Tutorial (at HiPEAC 2007): organizer and presenter ("Interactive Compilation Interface and plugin system to transform GCC into research toolset")
  • SMART’07 (at HiPEAC 2007): chair and organizer (1st Workshop on Statistical and Machine Learning Approaches applied to Architectures and Compilation http://www.hipeac.net/smart-workshop.html).

    In recent years, we have shown that machine learning and statistical search techniques can be effectively used to create and optimize production compilers (such as MILEPOST GCC) and architectures. Therefore, together with my colleagues, I decided to organize this workshop to promote new ideas and to present recent developments in compiler and architecture design using machine learning, statistical approaches, and search in order to enhance their performance, scalability, and adaptability.

2001

  • CPC’01: local organizer (9th Workshop on Compilers for Parallel Computers)
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