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A dynamic optimization framework for a Java just-in-time compiler
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Source Conference on Object Oriented Programming Systems Languages and Applications archive
Proceedings of the 16th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications table of contents
Tampa Bay, FL, USA
Pages: 180 - 195  
Year of Publication: 2001
ISBN:1-58113-335-9
Also published in ...
Authors
Toshio Suganuma  IBM Tokyo Research Laboratory, 1623-14 Shimoturuma, Yamato-shi, Kanagawa 242-8502, Japan
Toshiaki Yasue  IBM Tokyo Research Laboratory, 1623-14 Shimoturuma, Yamato-shi, Kanagawa 242-8502, Japan
Motohiro Kawahito  IBM Tokyo Research Laboratory, 1623-14 Shimoturuma, Yamato-shi, Kanagawa 242-8502, Japan
Hideaki Komatsu  IBM Tokyo Research Laboratory, 1623-14 Shimoturuma, Yamato-shi, Kanagawa 242-8502, Japan
Toshio Nakatani  IBM Tokyo Research Laboratory, 1623-14 Shimoturuma, Yamato-shi, Kanagawa 242-8502, Japan
Sponsor
SIGPLAN: ACM Special Interest Group on Programming Languages
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 20,   Downloads (12 Months): 150,   Citation Count: 47
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ABSTRACT

The high performance implementation of Java Virtual Machines (JVM) and just-in-time (JIT) compilers is directed toward adaptive compilation optimizations on the basis of online runtime profile information. This paper describes the design and implementation of a dynamic optimization framework in a production-level Java JIT compiler. Our approach is to employ a mixed mode interpreter and a three level optimizing compiler, supporting quick, full, and special optimization, each of which has a different set of tradeoffs between compilation overhead and execution speed. a lightweight sampling profiler operates continuously during the entire program's exectuion. When necessary, detailed information on runtime behavior is collected by dynmiacally generating instrumentation code which can be installed to and uninstalled from the specified recompilation target code. Value profiling with this instrumentation mechanism allows fully automatic code specialization to be performed on the basis of specific parameter values or global data at the highest optimization level. The experimental results show that our approach offers high performance and a low code expansion ratio in both program startup and steady state measurements in comparison to the compile-only approach, and that the code specialization can also contribute modest performance improvement


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  47

Collaborative Colleagues:
Toshio Suganuma: colleagues
Toshiaki Yasue: colleagues
Motohiro Kawahito: colleagues
Hideaki Komatsu: colleagues
Toshio Nakatani: colleagues