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Adaptive optimization in the Jalapeño JVM
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Source Conference on Object Oriented Programming Systems Languages and Applications archive
Proceedings of the 15th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications table of contents
Minneapolis, Minnesota, United States
Pages: 47 - 65  
Year of Publication: 2000
ISBN:1-58113-200-X
Also published in ...
Authors
Matthew Arnold  IBM T.J. Watson Research Center and Rutgers University
Stephen Fink  IBM T.J. Watson Research Center
David Grove  IBM T.J. Watson Research Center
Michael Hind  IBM T.J. Watson Research Center
Peter F. Sweeney  IBM T.J. Watson Research Center
Sponsor
SIGPLAN: ACM Special Interest Group on Programming Languages
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 24,   Downloads (12 Months): 86,   Citation Count: 147
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ABSTRACT

Future high-performance virtual machines will improve performance through sophisticated online feedback-directed optimizations. this paper presents the architecture of the Jalapeño Adaptive Optimization System, a system to support leading-edge virtual machine technology and enable ongoing research on online feedback-directed optimizations. We describe the extensible system architecture, based on a federation of threads with asynchronous communication. We present an implementation of the general architecture that supports adaptive multi-level optimization based purely on statistical sampling. We empirically demonstrate that this profiling technique has low overhead and can improve startup and steady-state performance, even without the presence of online feedback-directed optimizations. The paper also describes and evaluates an online feedback-directed inlining optimization based on statistical edge sampling. The system is written completely in Java, applying the described techniques not only to application code and standard libraries, but also to the virtual machine itself.


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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CITED BY  147

Collaborative Colleagues:
Matthew Arnold: colleagues
Stephen Fink: colleagues
David Grove: colleagues
Michael Hind: colleagues
Peter F. Sweeney: colleagues