CC* Compute: GROWTH - Gateway for Increased Research Output at a West Texas Higher-education Campus

西德克萨斯农工大学（WTAMU）正在扩展其计算资源，以满足其校园内四个学院教师、研究人员和学生快速增长的需求。该大学目前的目标是获得博士学位，并成为一所区域研究型大学，以解决与德克萨斯狭长地带相关的复杂和苛刻的问题。该项目旨在解决影响该地区的主要问题：水和能源。计算集群可以实现科学和工程高性能计算（HPC）和数据密集型人工智能（AI）。这些都是引领校园内水资源管理、风能收集和能源储存等多个科学驱动因素的关键范例。该项目通过最近建立的工程研究生项目，拓宽和加强了地区西班牙裔服务机构的教育机会，有助于培养具有高性能计算和人工智能知识的劳动力。该集群对WTAMU、潘汉德尔地区的社区学院和工业以及位于阿马里洛的德克萨斯农工大学农业生物研究站相关的研究和教育社区产生了强烈影响。除了提供本地计算资源外，该集群还为开放科学网格做出了贡献，该网格在全国范围内提供高吞吐量的计算资源。该计算集群配备1024个计算CPU内核和10240个GPU内核，可在计算流体力学、计算材料科学、计算力学、机器学习和数据可视化等领域实现大规模并行HPC工作流。它通过计算-经验界面补充了校园材料表征技术，并吸引本科生和研究生参与高性能计算和大数据应用。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。

West Texas A & M University (WTAMU) is expanding its computing resources to meet the rapidly growing needs of faculty, researchers, and students across four colleges on its campus. The university’s current objective is to attain doctoral status and become a Regional Research University to solve the complex and demanding problems associated with the Texas Panhandle region. This project is aimed at tackling the leading issues affecting the region: water and energy. The computing cluster enables scientific and engineering high performance computing (HPC) and data-intensive artificial intelligence (AI). These are key paradigms that are spearheading multiple science drivers across campus in water management, wind energy harvesting and energy storage. This program broadens and fortifies educational opportunities for a regional Hispanic Serving Institution with a recently established engineering graduate program, contributing to the development of an HPC & AI literate workforce. This cluster strongly impacts research and education communities associated with WTAMU, community colleges and industries in the Panhandle region, and Texas A&M Agrilife Research station in Amarillo. In addition to providing local computing resources, this cluster contributes towards the Open Science Grid, which provides high throughput computing resources across the nation. The compute cluster is equipped with 1024 compute CPU cores and 10240 GPU cores to enable massively parallel HPC workflows in computational fluid dynamics, computational materials science computational mechanics, machine learning and data visualization. It complements on campus materials characterization techniques via a computational-empirical interface and engages undergraduate and graduate students in high performance computing and big data applications.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.