OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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Such bytecodes have a variable impact on the stack pointer making collapsing all
stack adjustments into a single adjustment difficult or impossible. This complicates
the process of determining which stack offsets actually refer to the same value, making
it more difficult to perform copy propagation and unnecessary assignment removal.
The optimization process is also complicated by the many bytecodes that read
operands from the code stream. In Kaffe, the codelets that make use of these operands
commonly store them into a local variable named idx on one line and then use the
local variable as part of an assignment. Consequently, it is necessary to track all reads
and writes of this local variable in order to know if the expression local vars[idx]
refers to the same memory location at different points within the method.
These complications have prevented the author from developing a general purpose
optimizer for the codelets. Instead, a pattern matching approach was taken
similar to the bytecode idioms used in IBM’s virtual machine [103]. The general idea
was to select patterns that occurred in the 50 most frequently executed bytecodes
for 222 mpegaudio that had optimization opportunities that the compiler would be
unable to make use of. The patterns selected are listed in Table 7.5. The symbol
is used to represent an arithmetic or logical operation which removes two
integer values from the stack in order to compute an integer result which is pushed
onto the stack.
While the optimizations listed in Table 7.5 give the ability to improve the performance
of each individual codelet, they don’t provide a technique for determining
multicodes based on their optimization potential in addition to the number of occurrences
of the sequence and the number of transfers removed. As a result, it was
necessary to develop a new technique for evaluating candidate sequences, which is
presented in the following section.
7.8 Scoring that Considers the Complete Impact
of a Multicode
Each of the scoring techniques considered previously failed to examine the optimization
potential of the multicode sequence. While Equation 7.13 presents a closed
form that is easily computed, determining the value of ∆ x1 x 2 x 3 ...x n→ is non-trivial.
The difficulty arises from the fact that it is necessary to have a measure of the difference
in performance between the optimized multicode and the sequence of bytecodes
that it represents. This difference can be separated into the difference between