An LLM-enabled Workflow for Understanding and Evolving HPC Scheduling Practices

Emerging AI-enhanced and near real-time scientific workloads are challenging the traditional assumptions of HPC job scheduling systems. In this work, we present an LLM-enabled, portable workflow for analyzing a subset of Slurm job trace data from leadership-class supercomputers, exemplified through over 1.5 million jobs and 18 million job-steps on OLCF's Frontier system. Our hybrid workflow integrates a static data analysis pipeline with dynamic, AI-powered components to generate interactive dashboards and automated, interpretable insights into scheduling behavior, efficiency, and system usage patterns. We demonstrate the workflow's portability across HPC systems and show how AI-driven interpretations augment traditional visualization to uncover inefficiencies, guide policy evolution, and support more responsive scheduling strategies, enabling consistent analytics across HPC system architectures. This approach enables computer science researchers and HPC sysadmins to systematically evaluate workload characteristics and adapt HPC resource management to meet the evolving demands of modern scientific discovery.