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SoftSig: software-exposed hardware signatures for code analysis and optimization
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Architectural Support for Programming Languages and Operating Systems archive
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems table of contents
Seattle, WA, USA
SESSION: Microarchitecture table of contents
Pages 145-156  
Year of Publication: 2008
ISBN:978-1-59593-958-6
Also published in ...
Authors
James Tuck  North Carolina State University, Raleigh, NC
Wonsun Ahn  University of Illinois at Urbana-Champaign, Urbana-Champaign, IL
Luis Ceze  University of Washington, Seattle, WA
Josep Torrellas  University of Illinois at Urbana-Champaign, Urbana-Champaign, IL
Sponsors
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
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APPENDICES and SUPPLEMENTS
Supplemental material for SoftSig: software-exposed hardware signatures for code analysis and optimization


ABSTRACT

Many code analysis techniques for optimization, debugging, or parallelization need to perform runtime disambiguation of sets of addresses. Such operations can be supported efficiently and with low complexity with hardware signatures.

To enable flexible use of signatures, this paper proposes to expose a Signature Register File to the software through a rich ISA. The software has great flexibility to decide, for each signature,which addresses to collect and which addresses to disambiguate against. We call this architecture SoftSig. In addition, as an example of SoftSig use, we show how to detect redundant function calls efficiently and eliminate them dynamically. We call this algorithm MemoiSE. On average for five popular applications, MemoiSE reduces the number of dynamic instructions by 9.3%, thereby reducing the execution time of the applications by 9%.


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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Intel Corporation, Intel 64 and IA-32 Architectures Software Developer's Manual. Volume 3B: System Programming Guide, Part II, November 2007.
 
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D. Michie, ""Memo" Functions and Machine Learning," in Nature, April 1968.
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J. Renau, B. Fraguela, J. Tuck, W. Liu, M. Prvulovic, L. Ceze, S. Sarangi, P. Sack, K. Strauss, and P. Montesinos, "SESC Simulator," January 2005. http://sesc.sourceforge.net.
 
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S. Sastry, R. Bodik, and J. Smith, "Characterizing Coarse-Grained Reuse of Computation," in Workshop on Feedback-Directed and Dynamic Optmization, 2000.
 
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Collaborative Colleagues:
James Tuck: colleagues
Wonsun Ahn: colleagues
Luis Ceze: colleagues
Josep Torrellas: colleagues