MRI: Acquisition of a Shared High Performance Computational (HPC) Cluster for Research, Training and Institutional Use

南密西西比大学(USM)将购买一台超级计算机，以支持各种科学、技术、工程和数学(STEM)学科在计算和数据支持科学与工程(CDS&amp；E)方面的研究和培训。有了这台超级计算机，USM和地区社区学院和高中的学生、教职员工和教职员工都可以使用计算资源，从而增强培训和教育能力。该仪器还将包括一个机构储存库，用于存档数据集和学术成果，并将这些资源传播给科学界和公众。该仪器将建立关键的网络基础设施，以促进学生在四个主要领域的学习和研究，这些主要领域的共同点是计算：(I)材料科学，(Ii)生物科学，(Iii)沿海和海洋科学，以及(Iv)数据挖掘、生物信息学和地理信息学。该工具将包括标准计算节点、高核心计算节点、大内存节点和配备图形处理单元(GPU)的节点。该仪器将使院士、研究人员和学生能够在具有国家和国际重要性的领域进行研究和教育，如气候变化、沿海减灾和抗灾能力建设努力、先进制造、自然资源管理、材料基因组倡议和大数据科学。该仪器将减少研究人员对外部网络基础设施的依赖，并将帮助他们过渡到国家用户设施，如NSF的极端科学和工程发现环境(XSEDE)。

The University of Southern Mississippi (USM) will acquire a supercomputer to support research and training in computational and data-enabled science and engineering (CDS&E) in a variety of Science, Technology, Engineering and Mathematics (STEM) disciplines. With this supercomputer, computational resources will be made available to students, staff, and faculty at both USM and regional community colleges and high schools, thereby providing enhanced capabilities for training and education. This instrumentation will also include an institutional repository for archiving data sets and scholarly output, and disseminating these resources to the scientific community and to the public.This instrumentation will establish critical cyberinfrastructure needed to facilitate student learning and research in four main thrusts whose common denominator is computing: (i) Materials Science, (ii) Biological Sciences, (iii) Coastal and Marine Sciences, and (iv) Data Mining, Bioinformatics, and Geoinformatics. The instrumentation will comprise standard compute nodes, high-core compute nodes, large memory nodes, and nodes equipped with graphics processing units (GPUs). The instrumentation will enable academicians, researchers, and students to pursue research and education in areas of national and international importance, such as climate change, coastal hazard mitigation and resilience building efforts, advanced manufacturing, natural resource management, the materials genome initiative, and big data science. The instrumentation will reduce researchers' reliance on external cyberinfrastructure, and will aid their transition to national user facilities such as the NSF's eXtreme Science and Engineering Discovery Environment (XSEDE).