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SMART 2009 : SMART'09: 3rd Workshop on Statistical and Machine learning approaches to ARchitectures and compilaTion | |||||||||||||
Link: http://www.hipeac.net/smart-workshop.html | |||||||||||||
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Call For Papers | |||||||||||||
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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 |
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