OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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bytecodes have surprisingly little impact on Java execution performance. The average
was computed as the average of the benchmark-specific average results.
Many of the benchmarks tested during this study support differing input sizes.
This provides flexibility to the benchmark’s users, allowing computationally similar
problems to be tested in differing amounts of time. This was of particular use during
this study because the performance of the platforms tested varied considerably.
Consequently, it was not possible to run the benchmarks using the same input size
on every platform.
The SPEC JVM98 benchmark suite supports three standard input sizes referred
to as size 1, which executes in the least amount of time, size 10, and size 100 which
takes the most time to complete. In all cases, both the profiling and execution were
conducted using input size 100. The Java Grande Forum benchmark suite uses the
letters A, B and C to denote the size of the input set considered by the benchmark
where A denotes the smallest input set and C denotes the largest input set. All
profiling work involving the Java Grande Forum benchmarks was conducted using
input size A. When the benchmarks were tested for performance differing input sizes
were used with the goal of having run times that were sufficiently large to minimize
the impact of a single interruption by a background process and the granularity of the
system timer while maintaining run times that were short enough to keep the amount
of time required for performance testing manageable. As a result some benchmarks
were executed using their smallest input size on a slower platform and executed with
larger input sizes on other platforms. The average results presented in Figure 5.1
show the results achieved for each benchmark independent of the input size selected
when it was executed.
Numerous additional performance results are presented in Figures 5.2 through 5.9.
These graphs make use of the same histogram structure, showing the performance of
each benchmark for each despecialization condition, followed by an overall average
result for all of the benchmarks for each despecialization condition. Examining these
graphs suggests that the results achieved for each benchmark can be classified into
one of the following three categories:
Category 1: This category includes those benchmarks that showed only
a negligible difference in performance when any number of despecializations
were performed. In this case, a negligible difference in performance
is defined to be any change that is less than two percent of the benchmark’s
baseline runtime. Including this two percent buffer allowed minor
variations in benchmark performance that occurred as a result of differ-