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MRI: Acquisition of a High Performance Computing Cluster for Next-Generation Computational Science in Southern Colorado

MRI: Acquisition of a High Performance Computing Cluster for Next-Generation Computational Science in Southern Colorado
MRI:在南科罗拉多州收购下一代计算科学的高性能计算集群
批准号:
2017917
负责人:
Brandon Runnels
金额:
$43.52万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-10-01 至 2023-09-30

项目摘要

项目成果

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中文摘要
翻译
该项目将使一个高性能计算(HPC)集群(被称为INCLINE)的获取、部署和维护成为可能。该仪器将为UCCS校园和南科罗拉多科学和学术界提供急需的计算资源。该仪器的尺寸和功能将弥合工作站级机器和500强超级计算机之间日益扩大的差距,使研究人员能够在HPC平台上测试和利用代码可扩展性,加快结果处理,并获得本地HPC环境的专业知识。这项工作将深入了解生物医学应用,如微泡药物输送和骨折,军事应用,如增材制造能量学,以及民用应用,包括改进结构材料。除了研究应用之外,INCLINE还将被用作一个教育平台,向本科生和研究生教授HPC的基础知识,并允许学生利用HPC提供的计算能力更深入地研究课堂问题。它还将用于补充现有的外展计划,以激发南科罗拉多州社区对HPC的热情和兴趣,该社区在人口和应用范围上都是多样化的。该仪器最先进的硬件旨在支持广泛的高性能科学应用,从编译器设计到计算流体动力学。该仪器将包含CPU计算(包括标准和高内存)和GPU节点。计算节点将支持标准的大规模并行计算,如计算固体力学和流体动力学。GPU节点将允许加速计算物理和机器学习项目,并将与CPU节点一起使用,以测试异构架构上的最佳负载平衡。所有节点将使用InfiniBand高速互连连接,以最大限度地减少通信密集型应用程序的延迟,包括CFD和计算固体力学。高速SCRATCH存储文件系统将最小化具有大输出文件大小和I/O需求的应用程序的I/O延迟。该仪器的估计峰值性能约为90 TFLOPS。Slurm排队系统将用于管理不同用户群的帐户和分配。该仪器的坚固设计将使其能够满足UCCS和南科罗拉多州对当地HPC研究设施日益增长的需求,并将促进下一代计算科学家的培训。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
This project will enable the acquisition, deployment, and maintenance of a high performance computing (HPC) cluster (to be called INCLINE). This instrument will provide much-needed computational resources to the UCCS campus and the Southern Colorado scientific and academic communities. The size and power of the instrument will bridge the growing gap between workstation-level machines and Top 500 supercomputers, allowing researchers to test and leverage code scalability on an HPC platform, to expedite result processing, and to gain expertise on a local HPC environment. The work to be performed will yield insight into biomedical applications, such as microbubble drug delivery and bone fracture, military applications, such as additively manufactured energetics, and civil applications, including improved structural materials. In addition to research applications, INCLINE will be used as an educational platform for teaching the fundamentals of an HPC to undergraduate and graduate students and allow students to investigate classroom problems more deeply with the computational power provided by an HPC. It will also be used to supplement existing outreach programs to spark enthusiasm and interest in HPC in the Southern Colorado community, which is diverse in both population and the range of applications. The instrument’s state-of-the-art hardware is designed to support a broad range of high-performance scientific applications, ranging from compiler design to computational fluid dynamics. The instrument will contain both CPU compute (including standard and high memory), and GPU nodes. The compute nodes will enable standard massively parallel computations such as computational solid mechanics and fluid dynamics. The GPU nodes will allow acceleration on computational physics and machine learning projects and will be used in conjunction with CPU nodes to test optimal load balancing on heterogeneous architectures. All nodes will be connected using InfiniBand high speed interconnects to minimize latency for communication-intense applications, including CFD and computational solid mechanics. A high speed SCRATCH storage file system will minimize I/O latency for applications with large output file sizes and I/O requirements. The estimated peak performance of the instrument is approximately 90 TFLOPS. The Slurm queueing system will be used to manage accounts and allocations across the diverse user base. The robust design of this instrument will allow it to fill the growing need for a local HPC research facility at UCCS and in Southern Colorado and will facilitate the training of the next generation of computational scientists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(8)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1016/j.commatsci.2022.111632
发表时间: 2022
期刊: Computational Materials Science
影响因子: 3.3
作者: [Eren, Erdem, Runnels, Brandon, Mason, Jeremy]
通讯作者: Mason, Jeremy
DOI: 10.1016/j.commatsci.2023.112057
发表时间: 2022-12
期刊: Computational Materials Science
影响因子: 3.3
作者: [Brendon Waters;Daniel S. Karls;I. Nikiforov;R. Elliott;E. Tadmor;B. Runnels]
通讯作者: Brendon Waters;Daniel S. Karls;I. Nikiforov;R. Elliott;E. Tadmor;B. Runnels
DOI: 10.1063/5.0107739
发表时间: 2022-05
期刊: Physics of Fluids
影响因子: 4.6
作者: [E. Schmidt;J. M. Quinlan;B. Runnels]
通讯作者: E. Schmidt;J. M. Quinlan;B. Runnels
Diffuse interface method for solid composite propellant ignition and regression
固体复合推进剂点火与回归的扩散界面法
DOI: 10.1016/j.combustflame.2023.113120
发表时间: 2024
期刊: Combustion and Flame
影响因子: 4.4
作者: [Meier, Maycon, Schmidt, Emma, Martinez, Patrick, Quinlan, J. Matt, Runnels, Brandon]
通讯作者: Runnels, Brandon
共 7 条
    CAREER: A Multichannel Convolutional Neural Network Framework for Prediction of Damage Nucleation Sites in Microstructure
    • 批准号:
      2341922
    • 项目类别:
      Standard Grant
    • 资助金额:
      $50.51万
    • 财政年份:
      2023
    • 负责人:
      Brandon Runnels
    • 依托单位:
    CAREER: A Multichannel Convolutional Neural Network Framework for Prediction of Damage Nucleation Sites in Microstructure
    海外基金