OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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Chapter 6
Operand Profiling
Chapter 5 described profiling that was conducted in order to identify which specialized
bytecodes were executed with greatest frequency. When this profiling work was
conducted, a great deal of information was collected beyond what was required for
that study.
Numerous previous studies have profiled the bytecodes executed by Java applications.
However, these studies have confined themselves to considering only the opcode
component of the bytecodes, completely ignoring the operands. This study extends
previous work in the area by profiling the complete bytecodes executed, including
both the opcodes and the operands, for 23 Java applications. The profile data is then
used to evaluate the appropriateness of current specialized bytecodes when compared
to other specialized bytecode options. Additional patterns identified from the profile
data are also presented. The existence of these patterns is explained by examining
the architecture of the Java Virtual Machine.
6.1 Profiling Environment and Benchmarks
The Kaffe virtual machine was used to collect the data evaluated during this study
as described in Section 5.1. The benchmarks profiled include the 18 profiled for the
despecialization study presented in Chapter 5. Five additional benchmarks were also
profiled for this study. They include four benchmarks from the Ashes Suite Collection
[1] and the Linpack numeric benchmark [42]. The amount of input supplied to the
benchmarks in the Ashes Suite Collection was reduced from its standard levels in
order to make the time required for profiling manageable. The Linpack benchmark
was executed using its standard input size. Each benchmark is briefly described in
the following paragraphs.