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<p>The rapid expansion of AI, large-scale simulation, and data-intensive research has increased demand for GPU resources in national supercomputing systems. However, allocation mechanisms in many centers remain primarily workload-driven, which may not fully reflect evolving policy priorities across scientific domains. This study proposes a policy-integrated optimization framework that combines a target-aware static estimator with a dynamic runtime reallocation controller to incorporate policy objectives into operational allocation decisions. The framework internalizes a policy target vector <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(T\)</EquationSource> </InlineEquation>, constructed from stated priorities and historical usage, within both estimation and control stages. Using empirical demand curves under a rolling out-of-sample protocol, the proposed approach reduces policy-alignment error relative to uncontrolled and demand-driven baselines (MAE from 8.03 to 1.30% and RMSE from 9.59 to 1.66%), while maintaining GPU utilization above 92% and comparable throughput and queueing performance. Sensitivity analyses indicate stable behavior across tested parameter ranges. These findings suggest that policy-aware resource allocation can be implemented within existing scheduling environments without materially affecting core operational metrics, providing a structured approach to aligning supercomputing governance with strategic priorities.</p>

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Policy-aware GPU resource allocation for national supercomputing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Hyungwook Shim

摘要

The rapid expansion of AI, large-scale simulation, and data-intensive research has increased demand for GPU resources in national supercomputing systems. However, allocation mechanisms in many centers remain primarily workload-driven, which may not fully reflect evolving policy priorities across scientific domains. This study proposes a policy-integrated optimization framework that combines a target-aware static estimator with a dynamic runtime reallocation controller to incorporate policy objectives into operational allocation decisions. The framework internalizes a policy target vector \(T\) , constructed from stated priorities and historical usage, within both estimation and control stages. Using empirical demand curves under a rolling out-of-sample protocol, the proposed approach reduces policy-alignment error relative to uncontrolled and demand-driven baselines (MAE from 8.03 to 1.30% and RMSE from 9.59 to 1.66%), while maintaining GPU utilization above 92% and comparable throughput and queueing performance. Sensitivity analyses indicate stable behavior across tested parameter ranges. These findings suggest that policy-aware resource allocation can be implemented within existing scheduling environments without materially affecting core operational metrics, providing a structured approach to aligning supercomputing governance with strategic priorities.