OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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length n (the entire multicode block itself). By counting the number of times each
candidate sequence occurs across all multicode blocks, one can obtain the complete
set of candidate sequences executed, as well as the number of times each sequence
executed.
Performing multicode identification on the blocks shown in Figure 7.2 began by
determining all subsequences of length 2. This revealed that a total of 7 sequences
with length 2 are present. Three of these sequences are contributed by Block 1:
aload 0 → getfield, getfield → aload 0, and aload 0 → getfield. Four additional
sequences with length 2 are contributed by Block 2. However, while a total of
7 sequences with length 2 are present, only four of the sequences are distinct. For
example, the sequence aload 0 → getfield occurs three times. The identification
process went on to consider all sequences of length 3, 4 and 5 in the same manner.
Table 7.1 presents some of the information needed to perform multicode identification.
In addition to enumerating the set of unique candidate sequences, the table
maintains the number of times each sequence appears. This count, in conjunction
with an analysis of a specific candidate sequence, can be used to establish a score
representing how much optimization potential the candidate sequence has. In the
table, a very simple scoring procedure was used; the number of times the candidate
sequence appears multiplied by the length of the candidate sequence. Other scoring
algorithms will be presented later.
Using maximum score as the basis for selection, the table indicates that aload 0
getfield should be the first multicode selected. However, attempting to use the
table to determine the second multicode poses problems. To see why, assume that
the first substitution is performed, using mc to indicate the multicode aload 0 →
getfield. The two multicode blocks will change to the following:
Block 1:
Block 2:
mc → mc →
getfield → aload 0 → getstatic → mc →
From the blocks shown above, it is obvious that Table 7.1 cannot be used to
establish the second multicode. Almost all of the candidate sequences in the table no
longer exist, and most of the others will have their occurrence count (and thus their
score) changed because of the first multicode substitution.
This issue suggests an iterative algorithm for multicode identification. The profiling
work provides an initial stream of bytecodes, which is broken into a collection
of multicode blocks, and then used to obtain a set of candidate sequences and associated
scores. The candidate sequence with the best score is chosen as the first