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Toward Strategic Demand Management of Public Supercomputing Resources: Policy Gaps and Institutional Improvements

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초록

This study aims to propose strategic improvements to Korea’s supercomputer demand management policy. Through a comprehensive review of the existing legal framework and the operational plans of national and specialized centers, key structural limitations were identified and categorized into three areas: demand forecasting and planning, resource construction, and ecosystem development. To address these challenges, the study proposes a set of policy measures, including the establishment of mid- to long-term supply-demand planning mechanisms, the incorporation of resource quality metrics, resource provisioning based on reserve ratios, and the formalization of supercomputing within the national science and technology classification system. These recommendations collectively provide a foundation for transitioning from a supply-centric to a demand-responsive governance model in high-performance computing policy.

Abstract

This study aims to propose strategic improvements to Korea’s supercomputer demand management policy. Through a comprehensive review of the existing legal framework and the operational plans of national and specialized centers, key structural limitations were identified and categorized into three areas: demand forecasting and planning, resource construction, and ecosystem development. To address these challenges, the study proposes a set of policy measures, including the establishment of mid- to long-term supply-demand planning mechanisms, the incorporation of resource quality metrics, resource provisioning based on reserve ratios, and the formalization of supercomputing within the national science and technology classification system. These recommendations collectively provide a foundation for transitioning from a supply-centric to a demand-responsive governance model in high-performance computing policy.

참고문헌 (13)

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  8. Okazaki, Ryohei, "Supercomputer Fugaku Cpu A64fx realizing high performance, high-density packaging, and low power consumption", Fujitsu Technical Review (2020)
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  12. Tomčala, Jiří, "Supercomputer power consumption prediction using machine learning, nonlinear algorithms, and statistical methods", Journal of Physics: Conference Series, 2090(1) (2021), IOP Publishing

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