网络基础设施生产函数模型在R1机构中的应用。

IF 1.6
Frontiers in research metrics and analytics Pub Date : 2025-08-21 eCollection Date: 2025-01-01 DOI:10.3389/frma.2025.1449996
Preston M Smith, Jill Gemmill, David Y Hancock, Brian W O'Shea, Winona Snapp-Childs, James Wilgenbusch
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引用次数: 0

摘要

高性能计算(HPC)在高等教育中广泛应用于建模、仿真和人工智能应用。超级计算机是获得资金、吸引和留住教职员工以及教育学生的关键基础设施,但它的资本和运营成本很高,必须与其他竞争优先事项进行权衡。本研究运用经济学中的生产函数模型的概念,有两个重点:(1)评估先前建立研究计算投资价值量化模型的研究是否可推广到更广泛的大学;(2)定义一个基于制度生产反转生产函数的高性能计算投资容量计划模型。我们表明,生产函数模型似乎是泛化的,从计算资源和人员的投资中显示出正的制度回报。然而,我们确实发现,模型投入和产出之间的相对关系在不同的制度下有所不同,这通常可以归因于可理解的制度特定因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Application of the cyberinfrastructure production function model to R1 institutions.

Application of the cyberinfrastructure production function model to R1 institutions.

Application of the cyberinfrastructure production function model to R1 institutions.

Application of the cyberinfrastructure production function model to R1 institutions.

High-performance computing (HPC) is widely used in higher education for modeling, simulation, and AI applications. A critical piece of infrastructure with which to secure funding, attract and retain faculty, and teach students, supercomputers come with high capital and operating costs that must be considered against other competing priorities. This study applies the concepts of the production function model from economics with two thrusts: (1) to evaluate if previous research on building a model for quantifying the value of investment in research computing is generalizable to a wider set of universities, and (2) to define a model with which to capacity plan HPC investment, based on institutional production-inverting the production function. We show that the production function model does appear to generalize, showing positive institutional returns from the investment in computing resources and staff. We do, however, find that the relative relationships between model inputs and outputs vary across institutions, which can often be attributed to understandable institution-specific factors.

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CiteScore
3.50
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