The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: EduHPC-25: Workshop on Education for High Performance Computing
Authors: Sriram Srinivasan, Hamdan Alabsi, Rand Obeidat, Nithisha Ponnala, and Azene Zenebe (Bowie State University)
Abstract: We present the design, implementation, and evaluation of an elective course on GPU architecture and programming, offered to undergraduate and graduate students during Fall 2024 and Spring 2025. Aimed at equipping students with skills to build AI agents and workflows using AWS GPUs and SageMaker, the course began with foundational GPU architecture and parallel computing and progressed to hands-on development using Python. Students gained experience configuring cloud-based GPU instances, implementing parallel algorithms, and deploying scalable AI solutions. Learning outcomes were evaluated via assessments, course evaluations, and anonymous surveys. The results reveal that (1) AWS is an effective and economical platform for practical GPU programming, (2) experiential learning significantly enhanced technical proficiency, and (3) the course strengthened students’ problem-solving and critical thinking skills through tools such as TensorBoard and HPC profilers, which exposed performance bottlenecks and scaling issues. Our findings underscore the pedagogical value of integrating parallel computing into STEM education.
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