CFP: SMART'10 - 4th Workshop on Statistical and Machine learning approaches to ARchitecture and compilaTion (Pisa IT, Jan 10)

Grigori Fursin <>
Sun, 27 Sep 2009 08:32:51 -0700 (PDT)

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CFP: SMART'10 - 4th Workshop on Statistical and Machine learning appro (Grigori Fursin) (2009-09-27)
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From: Grigori Fursin <>
Newsgroups: comp.compilers
Date: Sun, 27 Sep 2009 08:32:51 -0700 (PDT)
Organization: Compilers Central
Keywords: conference, CFP
Posted-Date: 30 Sep 2009 18:53:07 EDT

Apologies if you receive multiple copies of this call.

                                                            CALL FOR PAPERS

                                                            4th Workshop on
                                  Statistical and Machine learning
                                              to ARchitecture and compilaTion


                                            January 24, 2010, Pisa, Italy

                                        Keynote speaker: Prof. Keith Cooper
                                                    Rice University, USA

                                    (co-located with HiPEAC 2010 Conference)

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 future application

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

* Empirical Automatic Performance Tuning
* Iterative Feedback-Directed Compilation
* Self-tuning Programs, Libraries and Language Extensions
* Dynamic Optimization/Split Compilation/Adaptive Execution
* Adaptive Parallelization
* Low-power Optimizations
* Adaptive Virtualization
* Performance Modeling
* Performance Portability
* Adaptive Processor and System Architecture
* Architecture Simulation and Design Space Exploration
* Collective Optimization
* Self-tuning Computing Systems
* Other Topics relevant to Intelligent and Adaptive Compilers/

**** Paper Submission Guidelines ****

Submitted papers should be original and not published or submitted for
publication elsewhere. Papers should use the LNCS format and should be
15 pages maximum. Manuscript preparation guidelines can be found at
the LNCS website (, go to "For
Authors" and then "Information for LNCS Authors"). Papers must be
submitted in the PDF using the workshop submission website.

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 7, 2009
Decision notification: December 14, 2009
Workshop: January 24, 2010 (half-day)

Program Chair:
  David Whalley, Florida State University, USA

  Grigori Fursin, INRIA, France
  John Cavazos, University of Delaware, USA

Steering Committee:
  Francois Bodin, CAPS Entreprise, France
  John Cavazos, University of Delaware, USA
  Lieven Eeckhout, Ghent University, Belgium
  Grigori Fursin, INRIA, France
  Michael O'Boyle, University of Edinburgh, UK
  David Padua, UIUC, USA
  Olivier Temam, INRIA, France
  Richard Vuduc, Georgia Tech, USA

Program Committee:
  Denis Barthou, University of Versailles, France
  Chun Chen, University of Utah, USA
  Bruce Childers, University of Pittsburgh, USA
  Rudolf Eigenmann, Purdue University, USA
  Bjorn Franke, University of Edinburgh, UK
  Maria Garzaran, UIUC, USA
  Sabine Glesner, TU Berlin, Germany
  Engin Ipek, Microsoft Research, USA
  Prasad Kulkarni, University of Kansas, USA
  Xiaoming Li, University of Delaware, USA
  Peter Marwedel, TU Dortmund, Germany
  Bilha Mendelson, IBM Haifa, Israel
  Kathryn McKinley, University of Texas, USA
  Boyana Norris, Argonne National Laboratory, USA
  Yunheung Paek, Seoul National University, Korea
  Markus Puschel, Carnegie Mellon University, USA
  Apan Qasem, Texas State University, USA
  Martin Schulz, LLNL, USA
  Xipeng Shen, College of William & Mary, USA
  Linda Torczon, Rice University, USA
  R. Clint Whaley, UTSA, USA
  Chengyong Wu, ICT, China
  Qing Yi, UTSA, USA

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