Interactive and Tool-Agnostic ML-Driven Workflow for Automated HPC Performance Modeling

This work presents an automated, reproducible, ML-based performance modeling workflow for HPC systems. The proposed workflow fully automates data generation, preprocessing, ML model training and validation. The protoype implementation is based on the JUBE workflow environment, through which a user-friendly interactive console is realized. The effectiveness of the automated workflow is demonstrated with a case study on I/O bandwidth modeling and prediction.