What Influences Students' Understanding of Scalability Issues in Parallel Computing?

Volume 12, Issue 2 (February 2021), pp. 58–65

Download PDF

@article{jocse-12-2-12,
  author={Juan Chen and Brett A. Becker and Youwen Ouyang and Li Shen},
  title={What Influences Students' Understanding of Scalability Issues in Parallel Computing?},
  journal={The Journal of Computational Science Education},
  year=2021,
  month=feb,
  volume=12,
  issue=2,
  pages={58--65},
  doi={https://doi.org/10.22369/issn.2153-4136/12/2/12}
}

Graduates with high performance computing (HPC) skills are more in demand than ever before, most recently fueled by the rise of artificial intelligence and big data technologies. However, students often find it challenging to grasp key HPC issues such as parallel scalability. The increased demand for processing large-scale scientific computing data makes more essential the importance of mastering parallelism, with scalability often being a crucial factor. This is even more challenging when non-computing majors require HPC skills. This paper presents the design of a parallel computing course offered to atmospheric science majors. It discusses how the design addressed challenges presented by non-computer science majors who lack a background in fundamental computer architecture, systems, and algorithms. The content of the course focuses on the concepts and methods of parallelization, testing, and the analysis of scalability. Considering all students have to confront many (non-HPC) scalability issues in the real world, and there may be similarities between real-world scalability and parallel computing scalability, the course design explores this similarity in an effort to improve students' understanding of scalability issues in parallel computing. The authors present a set of assignments and projects that leverage the Tianhe-2A supercomputer, ranked #6 in the TOP500 list of supercomputers, for testing. We present pre- and post-questionnaires to explore the effectiveness of the class design and find an 11.7% improvement in correct answers and a decrease of 36.8% in obvious, but wrong, answers. The authors also find that students are in favor of this approach.