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CFP: SMART'09 - submission deadline extended until Nov 21 - 3rdworkshop on statistical and machine l

Discussion in 'Embedded' started by Grigori Fursin, Nov 3, 2008.

  1. Apologies if you receive multiple copies of this call.

    ******* DEADLINE EXTENDED UNTIL NOVEMBER 21, 2008 *******

    ********************************************************************************
    CALL FOR PAPERS

    3rd Workshop on
    Statistical and Machine learning
    approaches
    to ARchitecture and compilaTion
    (SMART'09)

    http://www.hipeac.net/smart-workshop.html

    January 25, 2009, Paphos, Cyprus

    (co-located with HiPEAC 2009 Conference)

    **** NEW PANEL INFORMATION ****
    Can machine learning help to solve the multicore code generation
    issues?

    **** NEW PUBLICATION INFORMATION ****
    Selected papers will be considered for publication in a special
    issue
    of the International Journal of Parallel Programming.
    ********************************************************************************

    The rapid rate of architectural change and the large diversity
    of architecture features has made it increasingly difficult
    for compiler writers to keep pace with microprocessor evolution.
    This problem has been compounded by the introduction of multicores.
    Thus, compiler writers have an intractably complex problem to solve.
    A similar situation arises in processor design where new approaches
    are needed to help computer architects make the best use of new
    underlying
    technologies and to design systems well adapted to futureapplication
    domains.

    Recent studies have shown the great potential of statistical machine
    learning and search strategies for compilation and machine design.
    The purpose of this workshop is to help consolidate and advance the
    state
    of the art in this emerging area of research. The workshop is a forum
    for the presentation of recent developments in compiler techniques
    and machine design methodologies based on space exploration
    and statistical machine learning approaches with the objective
    of improving performance, parallelism, scalability, and adaptability.

    Topics of interest include (but are not limited to):

    Machine Learning, Statistical Approaches, or Search applied to

    * Feedback-Directed Compilation
    * Auto-tuning Programs + Language Extensions
    * Library Generators
    * Iterative Compilation
    * Dynamic Compilation/Adaptive Execution
    * Parallel Compiler Optimizations
    * Low-power Optimizations
    * Simulation
    * Performance Models
    * Adaptive Processor and System Architecture
    * Design Space Exploration
    * Other Topics relevant to Intelligent and Adaptive Compilers/
    Architectures

    **** Paper Submission Guidelines ****

    Paper length - maximum 15 pages. Papers must be submitted in the PDF
    (preferably) or postscript formats using the workshop submission
    website:
    http://unidapt.org/dissemination/workshops/smart09

    We suggest to use LNCS LaTeX templates that can be found at
    http://www.springeronline.com/lncs (go to "For Authors"
    and then "Information for LNCS Editors/Authors").

    An informal collection of the papers to be presented will be
    distributed at
    the workshop. All accepted papers will appear on the workshop website.

    **** Important Dates ****

    Final deadline for submission: November 21, 2008
    Decision notification: December 19, 2008
    Workshop: January 25, 2009

    Program Chair:
    David Padua, University of Illinois at Urbana-Champaign, USA

    Organizers:
    Grigori Fursin, INRIA Saclay, France
    John Cavazos, University of Delaware, USA

    Program Committee:
    Saman Amarasinghe, MIT, USA
    Francois Bodin, CAPS Enterprise, France
    Calin Cascaval, IBM T.J. Watson Research Center, USA
    John Cavazos, University of Delaware, USA
    Franz Franchetti, Carnegie Mellon University, USA
    Ari Freund, IBM Haifa Research Lab, Israel
    Grigori Fursin, INRIA Saclay, France
    Mary Hall, USC/ISI, USA
    Robert Hundt, Google, USA
    Michael O'Boyle, University of Edinburgh, UK
    David Padua, University of Illinois at Urbana-Champaign, USA
    Richard Vuduc, Georgia Institute of Technology, USA
    David Whalley, Florida State University, USA

    Panel: Can machine learning help to solve the multicore code
    generation issues?
    Chair:
    Francois Bodin, CAPS-Enterprise, France

    Participants:
    Marcelo Cintra, University of Edinburgh, UK
    Bilha Mendelson, IBM, Israel
    Lawrence Rauchwerger, Texas A&M University, USA
    Per Stenstrom, Chalmers University of Technology, Sweden



    ====================================================
    Grigori Fursin, PhD
    INRIA, France
    http://unidapt.org
     
    Grigori Fursin, Nov 3, 2008
    #1
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