OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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was developed which allowed such bytecode sequences to be identified automatically.
Because the identification process was automated, some long sequences of bytecodes
were replaced by multicodes. Such sequences could not be determined by human
intuition alone. Performance results showed that designing a set of multicodes for a
single application could decrease application runtime to approximately 70 percent of
the original runtime in the best case.
The performance gains achieved by performing multicode substitution come from
at least two sources. Some of the performance gain is achieved as a result of reducing
the total number of bytecode dispatches necessary in order to execute the application.
Additional performance gains were achieved by performing optimizations on
the codelet that implements the multicode. While some of these optimization can be
performed automatically by the compiler, other optimizations rely on knowledge of
the behaviour associated with internal virtual machine data structures. In particular,
it is possible to reduce the number of memory writes considerably by recognizing that
values which reside above the top of the stack once the multicode finishes executing
will not be utilized subsequently.
One of the disadvantages associated with performing multicode substitution is the
fact that those bytecodes that are not currently defined by the Java Virtual Machine
Specification are utilized. This means that performing multicode substitution prevents
other optimizations that rely on their availability from being performed and
limits the options for future expansion of the Java bytecode language. In order to
overcome this disadvantage, a study was performed that examined the impact of
performing both despecialization and multicode substitution at the same time.
It was necessary to profile the applications again in order to perform this study
due to four specialized bytecodes whose general purpose form depends on the context
in which the despecialization is performed. Performing 67 despecializations before
50 multicodes were identified showed an average performance loss of approximately
four percent compared to performing multicode substitution alone. This difference
in performance occurred for two reasons. Part of the difference can be attributed to
the fact that despecialization has already been shown to have a small detrimental
impact on application runtime. The remainder of the performance loss occurred because
performing 67 despecializations caused poorer overall multicode selections to be
made because the best initial candidate identified in the presence of despecialization
broke up many other sequences. Consequently, these sequences were not available for
consideration subsequently.
A second study was conducted where both despecialization and multicode sub-