MRI: Acquisition of a High-Performance GPU Cluster for Research and Education

MRI：采购用于研究和教育的高性能 GPU 集群

• 批准号: 2018575
• 负责人: Jiayin Wang
• 金额: $ 30.01万
• 依托单位: Montclair State University
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018575&HistoricalAwards=false
• 关键词: MRI; Acquisition; Performance; GPU; Cluster

Graphics Processing Unit(GPU)-acceleration) has become a useful, if not essential, tool for computational tasks in support of investigations across a wide range of disciplines, from data science to biological sciences to physical and social-science areas. Therefore, the addition of GPU-accelerated computing capacity comprising a cluster of hybrid GPU/CPU compute nodes to the HPC system, Hawk at Montclair State University provides critical support for research and education activities, across the Institution, which share the need for this capacity. The impact of each of these investigations is magnified by users’ access to the GPU-accelerated computing capacity provided by this System. Moreover, by serving as a focal point for high-performance, GPU-accelerated computing, the system stimulates and supports collaboration across disciplines by investigators as well as their external collaborators. In the setting of the university, a designated Hispanic-Serving Institution with a diverse student body, PIs, as well as other major users utilize this instrument in both undergraduate and graduate education activities that enhance the STEM education for these students, including a significant proportion who are first-generation students in higher education. The instrument serves to both attract the interest of --and provide training in leading-edge computing to-- a diverse group of students in order to inspire and prepare them to be part of the future STEM workforce by providing `hands on’ access to GPU-accelerated HPC and the computing tools and techniques enabled by this platform.The cluster of hybrid GPU/CPU (Computer Processing Units) compute nodes acquired under this award are integrated to the Hawk system. These nodes constitute a substantial and transformative expansion of computing capacity for research and education at Montclair State, which previously had no generally accessible GPU-accelerated HPC. The utility of the cluster of compute nodes is significantly enhanced by leveraging the resources of the Hawk cluster. The System is configured to be accessible to not only PIs, but also to users across the Institution, as well as their external collaborators and supports multiple ongoing GPU-accelerated research activities including investigations in (1) computing and data science, (2) biology and genomics, (3) geomorphology and land use, (4) computational mathematics, (5) applied mathematics, (6) business analytics, and (7) linguistics. These collaborations include the implementation of new techniques and approaches (e.g., machine learning and deep neural networks) to both emerging and long-standing problems in these disciplines.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.

图形处理单元(GPU)加速)已成为支持从数据科学到生物科学再到物理和社会科学领域各种学科调查的计算任务的有用工具，如果不是必不可少的话。因此，蒙特克莱尔州立大学的霍克将GPU加速的计算能力(包括混合GPU/CPU计算节点的集群)添加到HPC系统中，为整个机构的研究和教育活动提供关键支持，这些活动共享这一能力的需求。每一项调查的影响都因用户获得该系统提供的GPU加速计算能力而放大。此外，通过作为高性能、GPU加速计算的焦点，该系统刺激和支持调查人员及其外部合作者进行的跨学科协作。在大学的背景下，指定的西班牙裔服务机构PIs以及其他主要用户在本科生和研究生教育活动中使用这一工具，以加强对这些学生的STEM教育，包括相当大比例的第一代高等教育学生。该仪器既吸引了不同群体的学生的兴趣，也为他们提供了尖端计算方面的培训，通过提供对GPU加速的HPC和该平台支持的计算工具和技术的亲身体验，激励他们并为他们成为未来STEM劳动力的一部分做好准备。根据本奖项获得的混合GPU/CPU(计算机处理单元)计算节点的集群被集成到HAWK系统中。这些节点构成了蒙特克莱尔州用于研究和教育的计算能力的实质性和变革性的扩展，该州以前没有普遍可访问的由GPU加速的HPC。通过利用HAWK集群的资源，显著提高了计算节点集群的效用。该系统不仅可供私人投资机构使用，而且可供全机构用户及其外部合作者使用，并支持正在进行的多项由图形处理器加速的研究活动，包括(1)计算和数据科学、(2)生物学和基因组学、(3)地貌学和土地利用、(4)计算数学、(5)应用数学、(6)商业分析和(7)语言学的研究。这些合作包括实施新的技术和方法(例如，机器学习和深度神经网络)，以解决这些学科中新出现的和长期存在的问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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• DOI: 10.1145/3502736
• 发表时间: 2022-03
• 期刊: ACM Transactions on Knowledge Discovery from Data (TKDD)
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• DOI: 10.1145/3552490.3552494
• 发表时间: 2022
• 期刊: ACM SIGMOD Record
• 作者: Dave, Dev;Celestino, Angelica;Varde, Aparna S.;Anu, Vaibhav
• 通讯作者: Anu, Vaibhav

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• 发表时间: 2022
• 期刊: Intelligent Systems and Applications
• 作者: Amrutha Kommu, Snehal Patel