GPU Programming for AI Workflow Development on AWS SageMaker: An Instructional Approach
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arXiv
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| Format: | Preprint |
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2025
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| author | Srinivasan, Sriram Alabsi, Hamdan Obeidat, Rand Ponnala, Nithisha Zenebe, Azene |
| author_facet | Srinivasan, Sriram Alabsi, Hamdan Obeidat, Rand Ponnala, Nithisha Zenebe, Azene |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13703 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU Programming for AI Workflow Development on AWS SageMaker: An Instructional Approach Srinivasan, Sriram Alabsi, Hamdan Obeidat, Rand Ponnala, Nithisha Zenebe, Azene Distributed, Parallel, and Cluster Computing 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. |
| title | GPU Programming for AI Workflow Development on AWS SageMaker: An Instructional Approach |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.13703 |