This thesis introduces FlowGSP, a general-purpose sequence mining algorithm for flow graphs. FlowGSP ranks sequences according to the frequency with which they occur and according to their relative cost. This thesis also presents two parallel implementations of FlowGSP. The first implementation uses JavaTM threads and is designed for use on workstations equipped with multi-core CPUs. The second implementation is distributed in nature and intended for use on clusters. The thesis also presents results from an application of FlowGSP to mine program profiles in the context of the development of a dynamic optimizing compiler. Interpreting patterns within raw profiling data is extremely difficult and heavily reliant on human intuition. FlowGSP has been tested on performance-counter profiles collected from the IBM WebSphere Application Server. This investigation identifies a number of sequences which are known to be typical of WebSphere Application Server behavior, as well as some sequences which were previously unknown.

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