With parallel and distributed computing entering the mainstream of computer science,
it is important to ensure that parallel application codes are optimized to achieve the best
possible performance. This thesis describes the design and implementation of CHITRA92,
the second generation of a performance analysis system for parallel programs. CHITRA92 is
unique in that it uses visualization techniques to analyze the dynamic activity of a program
and produces a semi-Markov chain model of the program's behavior. This model can be
parameterized to predict behavior of a program and identify the performance bottlenecks
in the program. The important contributions of the CHITRA92 system are:
• The dynamic activity of a program is represented through a program execution sequence
(PES) and a set of program parameters. A PES description language has been
defined to allow users to describe the structure of a PES state vector and instances of
the state vector.
• A PES is reduced to a semi-Markov chain model via a set of transformations.
• Visualization is used to assist in selecting which transforms to apply to a PES.
• The presence of periodic behavior in PES's can be identified using spectral analysis
techniques.
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