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Implicitly parallel programming models for thousand-core microprocessors
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 44th annual Design Automation Conference table of contents
San Diego, California
SESSION: Thousand-core chips table of contents
Pages: 754 - 759  
Year of Publication: 2007
ISBN ~ ISSN:0738-100X , 978-1-59593-627-1
Authors
Wen-mei Hwu  University of Illinois at Urbana-Champaign
Shane Ryoo  University of Illinois at Urbana-Champaign
Sain-Zee Ueng  University of Illinois at Urbana-Champaign
John H. Kelm  University of Illinois at Urbana-Champaign
Isaac Gelado  Universitat Politecnica de Catalunya (UPC)
Sam S. Stone  University of Illinois at Urbana-Champaign
Robert E. Kidd  University of Illinois at Urbana-Champaign
Sara S. Baghsorkhi  University of Illinois at Urbana-Champaign
Aqeel A. Mahesri  University of Illinois at Urbana-Champaign
Stephanie C. Tsao  University of Illinois at Urbana-Champaign
Nacho Navarro  Universitat Politecnica de Catalunya (UPC)
Steve S. Lumetta  University of Illinois at Urbana-Champaign
Matthew I. Frank  University of Illinois at Urbana-Champaign
Sanjay J. Patel  University of Illinois at Urbana-Champaign
Sponsors
: The EDA Consortium
: IEEE/CASS/CANDE/CEDA
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper argues for an implicitly parallel programming model for many-core microprocessors, and provides initial technical approaches towards this goal. In an implicitly parallel programming model, programmers maximize algorithm-level parallelism, express their parallel algorithms by asserting high-level properties on top of a traditional sequential programming language, and rely on parallelizing compilers and hardware support to perform parallel execution under the hood. In such a model, compilers and related tools require much more advanced program analysis capabilities and programmer assertions than what are currently available so that a comprehensive understanding of the input program's concurrency can be derived. Such an understanding is then used to drive automatic or interactive parallel code generation tools for a diverse set of parallel hardware organizations. The chip-level architecture and hardware should maintain parallel execution state in such a way that a strictly sequential execution state can always be derived for the purpose of verifying and debugging the program. We argue that implicitly parallel programming models are critical for addressing the software development crises and software scalability challenges for many-core microprocessors.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Wen-mei Hwu: colleagues
Shane Ryoo: colleagues
Sain-Zee Ueng: colleagues
John H. Kelm: colleagues
Isaac Gelado: colleagues
Sam S. Stone: colleagues
Robert E. Kidd: colleagues
Sara S. Baghsorkhi: colleagues
Aqeel A. Mahesri: colleagues
Stephanie C. Tsao: colleagues
Nacho Navarro: colleagues
Steve S. Lumetta: colleagues
Matthew I. Frank: colleagues
Sanjay J. Patel: colleagues