CC* Compute: Shared Computing Infrastructure for Large-scale Science Problems
CC* Compute: Shared Computing Infrastructure for Large-scale Science Problems
批准号:
1925716
负责人:
Richard Jones
金额:
$40.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-08-01 至 2022-07-31
中文摘要
在过去十年中,科学仪器的进步导致了各种科学领域数据的规模和复杂性的显着增长。康涅狄格大学的研究人员正在扩展现有共享高性能计算机设施的能力,以应对这一挑战,他们增加了一个28节点的集群,每个节点具有40个英特尔核心和192 GB内存,以及1000 TB的共享存储。这种资源平衡已被选择用于粒子物理学,天体物理学,电子物理学,康州大学研究人员使用的数据和统计数据,以满足康州大学所有科学和工程领域对数据密集型计算日益增长的需求。此外,该集群将被配置为开放科学网格(OSG)上的共享科学计算资源。OSG是一个由大学和国家研究机构组成的联盟,他们正在使用网格技术将各自的计算机集群聚合到一个统一的国家科学计算基础设施中。通过将这个集群加入OSG,康州大学的研究人员将看到比他们单独在本地资源上运行的吞吐量高得多的吞吐量,使他们能够更快地周转和访问比本地存储和处理的更大的数据。该集群将使研究生一级现有的科学计算课程能够引入一个新的大数据部分。该奖项反映了NSF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
Advances in scientific instrumentation over the past decade have led to phenomenal growth in both the size and complexity of data across a wide variety of scientific domains. Researchers at the University of Connecticut are expanding the capabilities of the existing shared high-performance computer facility to meet this challenge by the addition of a 28-node cluster, each node with 40 Intel cores and 192 GB of memory, and 1000 TB of shared storage.This balance of resources has been chosen to accommodate applications in the areas of particle physics, astrophysics, geophysics, and statistics in use by UConn researchers, with a view to meet a rising need for data-intensive computing across all of science and engineering at the University. Furthermore, the cluster will be configured as a shared scientific computing resource on the Open Science Grid (OSG). OSG is a consortium of universities and national research facilities who are using grid technology to aggregate their individual computer clusters into a single unified national compute infrastructure for science. In joining this cluster to the OSG, UConn researchers will see a much higher throughput than they would see running on local resources alone, granting them faster turn-around and access to bigger data than could be stored and processed locally. The cluster will enable the introduction of a new big data component within an existing course on scientific computing at the graduate level. It will also be used to produce visualizations of astronomical observations that will be used in K-12 outreach.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.
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