Adaptive optimization in the Jalapeno JVM

被引:70
作者
Arnold, M [1 ]
Fink, S
Grove, D
Hind, M
Sweeney, PF
机构
[1] IBM Corp, TJ Watson Res Ctr, Armonk, NY 10504 USA
[2] Rutgers State Univ, Piscataway, NJ 08855 USA
关键词
D O I
10.1145/354222.353175
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Future high-performance virtual machines will improve performance through sophisticated online feedback-directed optimizations. This paper presents the architecture of the Jalapeno Adaptive Optimisation 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.
引用
收藏
页码:47 / 65
页数:19
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