CFP: SMART'09 - 3rd Workshop on Statistical and Machine learning approaches applied to ARchitectures and compilaTion (Cyprus, Jan 09)

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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)


                                          **** PUBLICATION INFORMATION ****


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


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 ****


Deadline for submission: November 7, 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




====================================================
Grigori Fursin, PhD
Research Scientist, INRIA, France
http://fursin.net/research - tackling the complexity
of future computing systems using machine learning

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