OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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very large. These large scores occur because each sequence executes many times. Each
of the benchmarks considered in this study execute more than 70 million multicode
blocks. For some benchmarks, a single multicode block executes more than 1 million
times. Consequently, the score for a sequence easily reaches into the millions when it
is computed as the product of the sequence’s length (or its length minus 1) and the
number of times it is executed.
Recall that during multicode identification, each candidate sequence is assigned a
score representing its potential to improve application performance. The cumulative
score of a set of n candidate sequences is simply the sum of the scores assigned to each
individual candidate sequence. Each line in Figure 8.1 plots the number of candidate
sequences selected against the cumulative score for that set of sequences. The individual
lines themselves are not particularly interesting, although they do effectively
demonstrate the problem of diminishing returns associated with candidate sequence
identification. However, comparing the two lines within each pair (one representing
the cumulative score before despecialization, the other after despecialization), makes
it possible to compare the relative expected optimization potential of multicode substitution,
with and without despecialization.
First, consider the bottom pair of lines, which are for the 213 javac benchmark.
This pair is representative of all of the benchmarks except 201 compress
and 202 jess. Note that the line for despecialization is consistently higher than for
multicode substitution alone, indicating a higher optimization potential after despecialization
than before. This result was expected because the amount of variability
in the code stream has been reduced and because the number of bytecodes executed
in the despecialized version is between 3.0 and 7.9 percent larger, depending on the
benchmark being tested.
Note however, that the top pair of lines show a different pattern. These lines were
drawn using the data from the 202 jess benchmark. The 201 compress benchmark
shows the same behaviour. In this case, selecting multicodes in the presence of
despecialization provides better results initially. However, after a specific number of
multicodes have been identified, the cumulative score with despecialization falls below
the cumulative score without despecialization. Thus, whether multicode identification
yields better results in the face of despecialization depends on how many multicodes
are substituted. This result was not expected, but is understandable if one considers
the fact that the multicode identification algorithm is not globally optimal. While
each multicode identified is the best available at the time it is selected, each selection