Leveraging HPC in University Courses: A Cross-Disciplinary Workforce Development Model

Volume 17, Issue 2 (November 2026), pp. 57–64

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@article{jocse-17-2-9,
  author={F. Alex Feltus and Jon C. Calhoun and Rong Ge and Jill Gemmill and Carlos Toxtli Hernandez and Bing Li and Todd Allen Price and Justin Talbot and Minjae Woo},
  title={Leveraging HPC in University Courses: A Cross-Disciplinary Workforce Development Model},
  journal={The Journal of Computational Science Education},
  year=2026,
  month=nov,
  volume=17,
  issue=2,
  pages={57--64},
  doi={https://doi.org/10.22369/issn.2153-4136/17/2/9}
}

This paper presents a cross-disciplinary workforce development model in which nine faculty members at Clemson University integrated the Palmetto2 supercomputer into seven courses spanning Bioinformatics and Computational Biology, Business Data Analytics, Computational Chemistry, Computer Science and Parallel Architectures, Automotive Engineering and AI Applications, Healthcare Data Science, and HPC Systems Hardware. A democratized access policy—removing departmental and financial barriers to cluster accounts-enabled broad adoption across student populations ranging from wet-lab biologists to working business professionals. Common pedagogical challenges included HPC onboarding, software dependency management, and large-dataset transfer; shared solutions included priority course scheduling, scaffolded desktop-to-cluster exercises, and modular training materials developed by Clemson's Research Computing and Data Engagement (RCDE) team. Discipline-specific computational profiles varied from large-dataset I/O in bioinformatics and healthcare to GPU-intensive deep learning in automotive AI and intensive floating-point computation in chemistry and parallel architectures. Three transferable principles emerge: democratized access, guaranteed priority scheduling for courses, and domain-contextualized scaffolded exercises. Students completing these courses gain Linux proficiency, job scheduling, parallel programming, and largescale data analysis skills directly applicable to research and industry careers in an economy increasingly dependent on dataintensive computation.