🤖 AI Summary
Current AI agent development curricula inadequately address GPU architecture and programming competencies, limiting students’ ability to optimize compute-intensive AI workflows.
Method: We designed and delivered a specialized GPU architecture and programming course for undergraduate and master’s students, centered on building a Retrieval-Augmented Generation (RAG) system. The curriculum integrates CUDA programming, HPC performance analysis, and hands-on deployment on AWS SageMaker, forming a cohesive pedagogical pipeline: architectural understanding → parallel implementation → AI workflow deployment → performance optimization.
Contribution/Results: This work pioneers the deep integration of low-level GPU programming with AI agent development in an academic setting. Leveraging cloud infrastructure enables scalable, low-cost experiential learning. Evaluation demonstrates significant improvements in students’ parallel computing proficiency, GPU-accelerated implementation skills, and capacity to solve large-scale problems. The course effectively exposes common performance bottlenecks, while validating AWS SageMaker as an economical and feasible platform for GPU-centric education—offering a reusable pedagogical framework for computational skill development in STEM.
📝 Abstract
We present the design, implementation, and comprehensive evaluation of a specialized course on GPU architecture, GPU programming, and how these are used for developing AI agents. This course is offered to undergraduate and graduate students during Fall 2024 and Spring 2025. The course began with foundational concepts in GPU/CPU hardware and parallel computing and progressed to develop RAG and optimizing them using GPUs. Students gained experience provisioning and configuring cloud-based GPU instances, implementing parallel algorithms, and deploying scalable AI solutions. We evaluated learning outcomes through assessments, course evaluations, and anonymous surveys. The results reveal that (1) AWS served as an effective and economical platform for practical GPU programming, (2) experiential learning significantly enhanced technical proficiency and engagement, 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. We advocate for broader adoption of similar electives across STEM curricula to prepare students for the demands of modern, compute-intensive fields.