OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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Test Total Weighted
Condition Performance Average
Change (s) (Percent)
Base 0.00 0.00
Load 0.63 0.46
Store -0.43 -0.31
Load Store -0.02 -0.01
Branch -1.14 -0.83
Constant -1.11 -0.80
Constant Widening -0.83 -0.60
LSC -0.44 -0.32
LSCB -2.02 -1.46
Complete -1.99 -1.44
Table 4.9: Sun Performance
marks that suffered the greatest performance loss under Complete despecialization
was 227 mtrt which executed almost 12.7 percent more slowly. Examining this
figure further reveals that each of the despecialization conditions that included the
despecialization of branch bytecodes had a similar impact on the performance of the
227 mtrt benchmark, leading to the conclusion that 227 mtrt likely makes heavy
use of those bytecodes and that their despecialization may negatively impact the
effectiveness of the RVM’s optimizing compiler.
4.2.5 Sun Performance
The final virtual machine considered in this study was developed by Sun. It makes
use of both an interpreter and a JIT compiler. However code generated with the
JIT compiler accounts for most of the application’s execution. Consequently, it was
anticipated that this virtual machine would show similar performance results to those
observed for IBM’s research virtual machine.
When its performance was tested for complete despecialization the overall change
in performance that was observed was a loss of approximately 1.4 percent. The
benchmark that was most responsible for this difference was 228 jack which showed
a performance loss of over 5.0 percent when complete despecialization was performed.
Summary results across all of the benchmarks for each despecialization condition
considered are presented in Table 4.9. The results observed for each benchmark and
despecialization condition are shown graphically in Figure 4.10.