Date of Award
2025-12-01
Degree Name
Master of Science
Department
Computer Science
Advisor(s)
Shirley V. Moore
Abstract
This thesis presents a tool to profile deep learning (DL) and machine learning (ML) models by collecting FLOPs, memory movement, and timing data through cyPAPI to generate roofline performance models. The tool is containerized for portability and reproducibility, integrates directly with PyTorch workflows, and provides fine grained insights into computational bottlenecks across model components. Unlike prior system-level or benchmarking-centric tools, this project empowers developers and researchers with an accessible, modular framework for performance analysis and optimization.
Language
en
Provenance
Received from ProQuest
Copyright Date
2025-12
File Size
61 p.
File Format
application/pdf
Rights Holder
Irvin Lopez-Audetat
Recommended Citation
Lopez-Audetat, Irvin, "Facilitating Deep Learning Performance Analysis Through Automated Roofline Model Generation" (2025). Open Access Theses & Dissertations. 4566.
https://scholarworks.utep.edu/open_etd/4566