Chapman, Barbara M.2013-08-052013-08-05May 20132013-05http://hdl.handle.net/10657/453OpenMP, a directive-based API supports multithreading programming on shared memory systems. Since OpenMP pragmas, directives, function calls, and environment variables are platform-independent, the API is highly portable. OpenMP provides necessary hints to the compiler in order to parallelize the given code, instead of focusing on the low-level details of the hardware. Performance prediction methodologies enable estimation of performance factors (execution time, cache misses, e ect of a compiler's optimizations) prior to the actual execution process. Existing approaches involve mathematical modeling of these performance factors. In order to achieve the best performance using OpenMP, it is critical to analyze cases such as the e cient cache utilization, optimal distribution of the workload among the CPUs. We attempt to solve the problem of e cient per-thread workload distribution by predicting an optimal combination of an OpenMP scheduling policy and a chunk size (we call this combination a \class"). We employed PAPI hardware counters, R statistical package, machine learning software WEKA, TAU toolkit, and the OpenMP collector API. A set of heuristics were applied to analyze the data to nd out the similarities between snippets of code pertaining to the same class. We developed a framework for taking measurements to gather the training data for the predictive model being constructed. We evaluate our approach using several case studies from application domains such as Dense Linear Algebra, Structured, and Unstructured Grids. The results demonstrate that there is a set of parameters that in uences the choice of the "class" for performance prediction. vapplication/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Thesisborn digital