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Volume 40 ,  Issue 7  (July 2005) table of contents
Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
SESSION: Code optimization table of contents
Pages: 69 - 77  
Year of Publication: 2005
ISSN:0362-1340
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Authors
Keith D. Cooper  Rice University, Houston, TX
Alexander Grosul  Rice University, Houston, TX
Timothy J. Harvey  Rice University, Houston, TX
Steven Reeves  Rice University, Houston, TX
Devika Subramanian  Rice University, Houston, TX
Linda Torczon  Rice University, Houston, TX
Todd Waterman  Rice University, Houston, TX
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 9,   Downloads (12 Months): 61,   Citation Count: 13
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ABSTRACT

Research over the past five years has shown significant performance improvements using a technique called adaptive compilation. An adaptive compiler uses a compile-execute-analyze feedback loop to find the combination of optimizations and parameters that minimizes some performance goal, such as code size or execution time.Despite its ability to improve performance, adaptive compilation has not seen widespread use because of two obstacles: the large amounts of time that such systems have used to perform the many compilations and executions prohibits most users from adopting these systems, and the complexity inherent in a feedback-driven adaptive system has made it difficult to build and hard to use.A significant portion of the adaptive compilation process is devoted to multiple executions of the code being compiled. We have developed a technique called virtual execution to address this problem. Virtual execution runs the program a single time and preserves information that allows us to accurately predict the performance of different optimization sequences without running the code again. Our prototype implementation of this technique significantly reduces the time required by our adaptive compiler.In conjunction with this performance boost, we have developed a graphical-user interface (GUI) that provides a controlled view of the compilation process. By providing appropriate defaults, the interface limits the amount of information that the user must provide to get started. At the same time, it lets the experienced user exert fine-grained control over the parameters that control the system.


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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Preston Briggs. The Massively Scalar Compiler Project. Appendix B of a nuweb document that forms part of the Mscp infrastructure. Available online as www.cs.rice.edu/~keith/EAC/iloc.pdf., July 1994.
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Keith D. Cooper, Alexander Grosul, Timothy J. Harvey, Steve Reeves, Devika Subramanian, Linda Torczon, and Todd Waterman. Searching for compilation sequences. 2005. Submitted for publication.
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Keith D. Cooper, Devika Subramanian, and Linda Torczon. Adaptive optimizing compilers for the 21st century. In Proceedings of the 2001 LACSI Symposium. Los Alamos Computer Science Institute, Santa Fe, NM, October 2001.
 
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Keith D. Cooper and Linda Torczon. Engineering a Compiler. Morgan-Kaufmann Publishers, 2003.
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Elana Granston and Anne Holler. Automatic recommendation of compiler options. In Proceedings of the 4th Feedback Directed Optimization Workshop, December 2001.
 
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Jin-Kao Hao, Frédéric Lardeux, and Frédéric Saubion. A hybrid genetic algorithm for the satisfiability problem. In Proceedings of the First International Workshop on Heuristics, Beijing, China, July 2002.
 
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CITED BY  13

Collaborative Colleagues:
Keith D. Cooper: colleagues
Alexander Grosul: colleagues
Timothy J. Harvey: colleagues
Steven Reeves: colleagues
Devika Subramanian: colleagues
Linda Torczon: colleagues
Todd Waterman: colleagues