OPTIMIZING THE JAVA VIRTUAL MACHINE INSTRUCTION SET BY ...

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identify the branch targets and exception handler boundaries. A further analysis
would have determined how many times each sequence of bytecodes was present.
Unfortunately, such an analysis was unlikely to identify good candidates for multicodes
because it entirely fails to consider which bytecodes are executed once, which
bytecodes are executed many times and which bytecodes are never executed as the
application runs. As a result, it was necessary to consider another profiling technique
that would overcome these limitations.
7.3.3 An Initial Approach to Dynamic Profiling
Dynamic profiling records information about an application as it executes. It can
be used to overcome the limitations associated with static profiling. A copy of the
Kaffe virtual machine was modified in order to collect the profile information needed
to determine which candidate sequences were executed with greatest frequency. The
information collected included:
• Every bytecode executed, and the program counter value for the bytecode.
• The name of each method executed.
• A list of branch and exception targets for each method executed.
Each opcode was recorded as it was executed. The program counter value associated
with the opcode was needed in order to determine the multicode block boundaries
within the profile data.
The first time a method was invoked, its fully qualified name and signature was
recorded. In order to reduce the memory requirements of profiling, a unique integer
was assigned to each method. It was used to identify subsequent invocations of the
method so it was not necessary to store its fully qualified name multiple times.
The branch structure for each method was also computed and stored the first time
the method was invoked. This information was required in order to break the profile
data into multicode blocks once it was collected.
Once the profile data was collected, it was converted from a list of bytecodes
and method invocations into a list of multicode blocks. This involved reading every
bytecode executed and its program counter value from the profile data, and writing
the bytecode to the list of multicode blocks. Because the program counter value for
each bytecode, and the branch structure for each method was recorded, sufficient
information was available to determine which bytecodes represented the start of a