Integrating novel methodologies, tools, and IT resources for graduate level courses in high performance computing and advanced signal processing algorithms

This work presents an approach at integrating novel methodologies for teaching graduate level courses in the areas of high performance computing (HPC) and advanced signal processing algorithms (ASPA) for computer engineering and computer science and engineering curricula. The novel teaching methodology presented here in high performance computing centers on the use of innovative empirical methods, i.e., exploratory data analysis, experiment design, etc., for studying computer performance, whereas an operator signal algebra approach is considered a novel methodology for the studying of advanced signal processing algorithms. The work also discusses an on going concerted effort at utilizing common tools and IT resources in both courses to provide students a holistic learning experience.