Self-paced Learning in HPC Lab Courses

Volume 11, Issue 1 (January 2020), pp. 61–67

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@article{jocse-11-1-10,
  author={Christian Terboven and Julian Miller and Sandra Wienke and Matthias S. M\"{u}ller},
  title={Self-paced Learning in HPC Lab Courses},
  journal={The Journal of Computational Science Education},
  year=2020,
  month=jan,
  volume=11,
  issue=1,
  pages={61--67},
  doi={https://doi.org/10.22369/issn.2153-4136/11/1/10}
}

In a software lab, groups of students develop parallel code using modern tools, document the results and present their solutions. The learning objectives include the foundations of High-Performance Computing (HPC), such as the understanding of modern architectures, the development of parallel programming skills, and coursespecific topics, like accelerator programming or cluster set up. In order to execute the labs successfully with limited personnel resources and still provide students with access to world-class HPC architectures, we developed a set of concepts to motivate students and to track their progress. This includes the learning status survey and the developer diary, which are presented in this work. We also report on our experiences with using innovative teaching concepts to incentivize students to optimize their codes, such as using competition among the groups. Our concepts enable us to track the effectiveness of our labs and to steer them for increasing sizes of diverse students. We conclude that software labs are effective in adding practical experiences to HPC education. Our approach to hand out open tasks and to leave creative freedom in implementing the solutions enables the students to self-pace their learning process and to vary their investment of effort during the semester. Our effort and progress tracking ensures the achieving of the extensive learning objectives and enables our research on HPC programming productivity.