CFP: SMART'09 - submission deadline extended until Nov 21 - 3rd workshop on statistical and machine learning approaches to architecture and compilation

[Grigori Fursin]

Apologies if you receive multiple copies of this call.

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

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                              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