OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

63
Test Total Weighted
Condition Performance Average
Change (s) (Percent)
Base 0.00 0.00
Load 0.75 0.24
Store 0.51 0.17
Load Store -0.43 -0.14
Branch -3.86 -1.26
Constant -0.13 -0.04
Constant Widening -1.04 -0.34
LSC -0.70 -0.23
LSCB -4.46 -1.45
Complete -7.27 -2.37
Table 4.7: Kaffe Performance
is also presented as a percentage of the baseline execution time. When the values
in this table are examined, it is found that performing complete despecialization
has the largest impact on application performance, slowing it by approximately 2.37
percent. Smaller performance losses were observed for six of the other despecialization
conditions tested. A small performance gain was observed for the remaining two
despecialization conditions. These gains may be the result of the fact that once
despecialization is performed, the number of distinct cases within the interpreter
loop is reduced. This improves instruction cache performance at the expensive of the
additional cost associated with executing the despecialized forms. It is also important
to notice that each of the performance gains is small, representing a change of less
than 0.25 percent of the baseline runtime.
The single largest performance loss occurred when 202 jess was executed. In
this case, the performance loss was approximately 4 percent of the baseline execution
time. While any performance loss is undesirable, an average loss of only 2.37 percent
and a maximum loss of approximately 4 percent are smaller than what was initially
anticipated. Figure 4.8 shows the observed performance for every combination of
benchmark and despecialization condition. Note that the scales used on the graphs
vary in order to show as much detail as possible.
4.2.4 RVM Performance
The second virtual machine considered in this study was IBM’s Research Virtual
Machine (RVM). It was expected that the performance losses observed for this vir-