🤖 AI Summary
This work addresses the technical and behavioral challenges of transitioning from node-exclusive to resource-aware scheduling in production-grade heterogeneous HPC systems, a shift that risks disrupting established scientific workflows. To enable seamless, non-disruptive migration, the authors propose a collaborative operational framework integrating a time-bound compatibility layer, observability-driven feedback mechanisms, and targeted user guidance. Built upon Slurm’s TRES resource model, the approach combines runtime compatibility support, job queue monitoring, and user behavior analysis to preserve workflow continuity while substantially improving scheduling efficiency. Empirical results demonstrate dramatic reductions in median queue wait times—from 277 minutes to under 3 minutes for CPU jobs and from 81 minutes to 3.4 minutes for GPU jobs—alongside high long-term adoption rates among users who embraced the new submission paradigm.
📝 Abstract
Migrating heterogeneous high-performance computing (HPC) systems to resource-aware scheduling introduces both technical and behavioral challenges, particularly in production environments with established user workflows. This paper presents a case study of transitioning a production academic HPC cluster from node-exclusive to consumable resource scheduling mid-lifecycle, without disrupting active workloads. We describe an operational strategy combining a time-bounded compatibility layer, observability-driven feedback, and targeted user engagement to guide adoption of explicit resource declaration. This approach protected active research workflows throughout the transition, avoiding the disruption that a direct cut-over would have imposed on the user community. Following deployment, median queue wait times fell from 277 minutes to under 3 minutes for CPU workloads and from 81 minutes to 3.4 minutes for GPU workloads. Users who adopted TRES-based submission exhibited strong long-term retention. These results demonstrate that successful scheduling transitions depend not only on system configuration, but on aligning observability, user engagement, and operational design.