Enhancing Parallel Programming Education with High-Performance Clusters Utilizing Performance Analysis

Abstract

The use of high-performance clusters in parallel programming education is not trivial. Not only working on a cluster can be a challenge. Using clusters requires the development of complex parallel programs that are executed with many processes. This increases the necessity for performance analysis to determine bottlenecks, identify optimization opportunities, and evaluate scalability. For learners, performance analysis is a crucial aspect of understanding parallel programs and systems. However, due to its complexity and the prior knowledge required, learners are often overwhelmed by the analysis and interpretation of performance metrics. We conducted a usability test to identify barriers and issues of using a high-performance cluster in parallel programming education in conjunction with professional performance analysis using the tools Score-P, Scalasca, and Cube. At the same time, we tested EduMPI, a novel learning support tool that simplifies the process of executing parallel programs and automates performance analysis at runtime.