OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

167
7.6.2 Overall Performance Across All Benchmarks
The previous section considered each of the benchmarks individually. The results
presented in this section consider the development of a single set of multicodes that
offer a performance benefit for all of the benchmarks under consideration.
Each of the benchmarks considered in this study executes a different number of
bytecodes. While 201 compress executes a total of more than 950 million bytecodes,
213 javac executes less than 75 million, a difference of approximately 875 million
bytecodes. Consequently, it was necessary to normalize the number of bytecodes
executed by each benchmark in order to develop a single set of multicodes for all of the
applications. If this process was not performed, the multicodes selected would have
been heavily skewed in favour of the 201 compress benchmark because it executes
far more bytecodes than any of the other benchmarks under consideration.
In order to accomplish this goal, the compressed multicode block files for each
benchmark were processed and merged into a single file. As each compressed multicode
block file was read, the total number of multicode blocks executed by the
benchmark was recorded. The count for each multicode block was expressed as a percentage
of the total number of blocks executed by the benchmark. Then the results
for the six benchmarks were merged together. By performing this normalization process,
each benchmark contributed an equal number of bytecodes to the newly created
compressed multicode block file representing the total set of blocks executed by all of
the benchmarks.
Once the new file was created, it was used to determine the 50 best multicodes.
Examining the performance results presented in the previous section revealed that
on average, using Transfer Reduction Scoring and a maximum multicode length of 25
bytecodes offered the best performance. Consequently, this combination of scoring
technique and maximum multicode length was employed when determining the set of
multicodes to test across all of the benchmarks. The specific multicodes utilized are
listed in Table A.7, located in Appendix 11.
Figure 7.22 shows the performance results achieved when a single set of multicodes
was identified for all six benchmarks. The performance achieved for each benchmark
individually is shown using a broken line while the average performance across all
benchmarks is shown using a solid line. Interestingly, this graph reveals that the
best performance is achieved when 30 multicode substitutions were performed. This
result indicates that the cost associated with introducing additional multicodes has
begun to outweigh the benefit at this point. This behaviour is not observed when
multicodes are determined for a single benchmark because each multicode identified