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.