Reimagine Application Performance as a Graph: Novel Graph-Based Method for Performance Anomaly Classification in High-Performance Computing
Published in 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), 2024
Recommended citation: Chase Phelps, Ankur Lahiry, Tanzima Z Islam, and Line C Pouchard. (2024). "Reimagine Application Performance as a Graph: Novel Graph-Based Method for Performance Anomaly Classification in High-Performance Computing." 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, pp. 240–245. https://ieeexplore.ieee.org/document/10633643/
A novel graph-based representation of application performance for performance anomaly classification using graph neural networks. The method captures complex relationships among tasks and resources to enable effective anomaly detection in large-scale HPC systems.
Authors: Chase Phelps, Ankur Lahiry, Tanzima Z Islam, and Line C Pouchard