CQTwin: A Digital-Twin Toolkit for Operational Analytics of HPC Schedulers

HPC facilities collect years of job scheduler records, yet much of this operational data remains underused because accessible tools for turning it into actionable insight are scarce. We present CQTwin, an open-source digital-twin toolkit for operational analytics of HPC scheduling data. CQTwin addresses this gap through two complementary modes: offline trace replay and online event-driven simulation. Built on the Python-based CQSim discrete-event simulator, CQTwin combines a fidelity-validated, high-performance C++ engine with an interactive browser-based dashboard and a Snapshot Lab, enabling facility staff, operators, and researchers to characterize workloads, compare scheduling policies, and explore what-if scenarios. As an offline twin, CQTwin replays archived workload traces under alternative policies to show how the same workload would have unfolded under different scheduling decisions. As an online twin, it consumes job events as they arrive and projects how alternative policies would process the currently queued jobs, providing guidance for real-time decision-making. The dashboard supports the complete analysis workflow, including workload characterization, experiment configuration and monitoring, and multidimensional comparison of simulation results, allowing users to determine visually which policies best suit a workload and what trade-offs they entail. We validate CQTwin using production traces from the Argonne Leadership Computing Facility spanning 2016-2023.

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Authors

Kanglin Xu; Yash Kurkure; Yihe Zhang; Michael E. Papka; Zhiling Lan

Published

Oct 19, 2026

Paper type

preprint

Source

Arxiv