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.

INTRODUCTION

This article presents results of ongoing work on developing novel teaching methodologies in the areas of High Performance Computing (HPC) and Advanced Signal Processing Algorithms (ASPA) and the integration of these methodologies, along with teaching developing tools and Information Technology (IT) resources into graduate level courses for the Computer Engineering and Computer Science and Engineering curricula. The paper starts by providing a basic example of how the areas of HPC and ASPA closely interact in many engineering applications, creating in this manner the motivation and rational for addressing the issue of course integration. It continues with description of aspects in the areas HPC and ASPA as they are used in this work and proceeds by describing the novel teaching methodologies introduced in these areas. It then concludes with a set of guidelines of how to integrate developing tools and IT resources to strengthen the academic interaction between these courses and providing some recommendations for teachers.