Vincent Freeh, North Carolina State University - August 2014

Our research improves the understanding of profile-based optimization. The research will create tools to extract information and conduct studies using these tools to understand how source and trace data are interacting and affecting optimizations. This research classifies program workloads in order to develop benchmarking workloads that yield better overall performance. This research enables one to identify workloads that have similar profiles and performance improvements. As a result, it will shed light into the black box that is profile-based optimization in order to more effectively use it. This research is an extension of research begun during an independent study in the Spring Semester of 2014. Our previous results, although unfinished, indicate that the performance improvements to the optimized program are highly dependent on the benchmarking workload and vary significantly.