OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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Chapter 5
Profiling and Despecialization
Chapter 4 described despecialization – the process of replacing specialized bytecodes
with semantically equivalent general purpose bytecodes. This chapter presents additional
despecialization work that builds on that material. In Chapter 4, the specialized
bytecodes were grouped based on the similarity of the despecialization process. This
chapter presents results achieved when the specialized bytecodes were ordered based
on their execution frequency. It further extends the work presented in the previous
chapter by considering additional benchmarks, platforms and virtual machines. Additional
insight is also provided into why despecialization has only a minor impact on
application performance.
5.1 Profiling
All of the work presented in this chapter relies on profiling information being available.
The purpose of profiling the applications that were executed during performance
testing was to determine the frequency with which each bytecode was executed. The
work was conducted under the assumption that the frequency of execution of the
specialized bytecode should be a good predictor of the impact despecialization has
on the performance of the application. If the bytecode is rarely executed, performing
despecialization should have almost no impact on application performance. This
behaviour is expected because the difference in performance between the specialized
and general purpose bytecodes would have to be huge in order for a rarely executed
bytecode to have a large impact on application performance. Specialized bytecodes
that execute with greater frequency have a larger ability to impact the performance
of the application. Since the most frequently executed bytecodes can account for
several percent of the total bytecodes executed by an application, a relatively small