OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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Popping any values left on the stack after the bytecode sequence finished executing
was a considerably easier undertaking than pushing the values need before the
sequence could execute. The pop bytecode removes one category 1 value (an integer,
float or object reference) from the stack and discards it. Similarly, the pop2 bytecode
is able to remove and discard one category 2 value (a long integer or double) from the
stack. Consequently, popping the remaining values was simply a matter of generating
a sequence of pop and pop2 bytecodes based on the types of the values present on
the stack when the sequence finished executing.
Because of the time involved in profiling, it was not possible to time every candidate
sequence. The transfer reduction scoring system was used to identify candidate
sequences that were likely to offer improved performance. The best sequences based
on transfer reduction were then timed and the sequence that offered the greatest performance
improvement was selected. Unfortunately using this technique did not offer
any performance gain over simple transfer reduction. This result occurred because
the micro-benchmarking technique employed was not an accurate representation of
the environment in which the bytecode sequence normally executes. Furthermore,
using this micro-benchmarking approach completely failed to consider the cumulative
impact of performing several multicode substitutions. These impacts included
the added cost of decoding because the switch statement contains additional cases,
the impact on cache performance due to decreased code locality and the impact on
register allocation when the virtual machine is compiled due to the increased amount
of code residing within the main interpretation engine.
A different timing strategy was developed in order to overcome these limitations.
Instead of using micro-benchmarking, testing was performed using the full benchmark.
This overcame the need to generate a new Java class file since the benchmark’s
application classes were used. It also overcame the problems associated with
the timing being performed in a contrived environment. Furthermore, instead of timing
each bytecode sequence individually, once the best multicode was identified, it
was included in all subsequent timings performed. Thus the cumulative impact of
performing several multicode substitutions was also considered. As was the case for
micro-benchmarking, it was too expensive to time every possible bytecode sequence.
Again, transfer reduction scoring was used as a predictor of the best bytecode sequences.
Then the five best sequences identified by transfer reduction scoring were
timed. The substitution that resulted in the best performance was selected and the
process was repeated until the desired number of multicodes were identified.
Using this technique has been shown to provide better performance than using