Teaching parallel and distributed computing using data-intensive computing modules

Abstract

Parallel and distributed computing (PDC) courses are useful for computer science (CS) and domain science students. For CS students, PDC is a fundamental field that examines concepts relating to a range of CS subfields, such as algorithms, architecture, simulation, software, systems, among others. Students with domain science backgrounds also require PDC to carry out their research objectives, and the ongoing data revolution has exacerbated this necessity. Given the rise of data science and other data-enabled computational fields, we propose several data-intensive pedagogic modules that are used to teach PDC using message-passing programming with the Message Passing Interface (MPI). These modules employ activities that are interesting, relevant, and accessible to both computer and domain science students enrolled in graduate level programs.

Using pre- and post-module completion quizzes and anonymous free response surveys, we evaluated the efficacy of the pedagogic modules across four cohorts of students enrolled in a graduate level High Performance Computing (HPC) course at Northern Arizona University. The students have diverse educational backgrounds as some students were enrolled in programs outside of CS. These programs include electrical and computer engineering, mechanical engineering, astronomy & planetary science, bioinformatics, and ecoinformatics. Despite the multi-disciplinary backgrounds of the students, we find that the hands-on application-driven approach to teaching PDC was successful at helping students learn core PDC concepts, and that the modules are useful for facilitating online learning which was required during the COVID-19 pandemic.