From Mathematical Model to Parallel Execution to Performance Improvement: Introducing Students to a Workflow for Scientific Computing

Current courses in parallel and distributed computing (PDC) often focus on programming models and techniques. However, PDC is embedded in a scientific workflow that incorporates more than programming skills. The workflow spans from mathematical modeling to programming, data interpretation, and performance analysis. Especially the last task is covered insufficiently in educational courses. Often scientists from different fields of knowledge, each with individual expertise, collaborate to perform these tasks. In this work, the general design and the implementation of an exercise within the course “Supercomputers and their programming” at Technische Universität Dresden, Faculty of Computer Science is presented. In the exercise, the students pass through a complete workflow for scientific computing. The students gain or improve their knowledge about: (i) mathematical modeling of systems, (ii) transferring the mathematical model to a (parallel) program, (iii) visualization and interpretation of the experiment results, and (iv) performance analysis and improvements. The exercise exactly aims at bridging the gap between the individual tasks of a scientific workflow and equip students with wide knowledge.