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: University of Colorado at Colorado Springs
• 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
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017917&HistoricalAwards=false
• 关键词: MRI; Acquisition; Performance; Computing; Cluster

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.

该项目将使高性能计算（HPC）集群（将称为CNOLINE）的采购，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。该仪器的大小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。将要进行的工作将深入了解生物医学应用，如微泡药物递送和骨折，军事应用，如增材制造能量学，以及民用应用，包括改进的结构材料。 除了研究应用之外，Cubine还将作为一个教育平台，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推广计划，以激发科罗拉多南部社区对HPC的热情和兴趣，该社区的人口和应用范围都很多样化。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。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节点结合使用，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的延迟，包括CFD和计算固体力学。高速SCRATCH存储文件系统将最大限度地减少具有大输出文件大小和I/O要求的应用程序的I/O延迟。该仪器的估计峰值性能约为90 TFLOPS。Slurm系统将用于管理不同用户群的帐户和分配。该仪器的稳健设计将使其能够满足UCCS和南科罗拉多对本地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NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Comparison of evolving interfaces, triple points, and quadruple points for discrete and diffuse interface methods

离散和扩散界面方法的演化界面、三点和四点比较

• 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

Automated determination of grain boundary energy and potential-dependence using the OpenKIM framework

• 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

Self-similar diffuse boundary method for phase boundary driven flow

• 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

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

A diffuse interface method for solid-phase modeling of regression behavior in solid composite propellants

固体复合推进剂回归行为固相建模的扩散界面方法

• DOI: 10.1016/j.combustflame.2022.112219
• 发表时间: 2022
• 期刊: Combustion and Flame
• 影响因子: 4.4
• 作者: Kanagarajan, Baburaj;Quinlan, John M.;Runnels, Brandon
• 通讯作者: Runnels, Brandon