CC* Compute: Augmenting a 2,560-core EPYC2 Computational Cluster with GPUs for AI, Machine Learning, and other GPU-Accelerated HPC Applications

该项目为田纳西查塔努加大学(UTC)的现有计算集群增加了36个NVIDIA A100 80 GB图形处理器(GPU)，用于18台现有服务器。这一升级为这些计算机在许多工作负载上提供了三倍以上的性能提升，并在某些计算领域(如人工智能问题)提供了更快的速度改进。目前，GPU是在当今的服务器、工作站和桌面系统上经济高效地实现极高计算性能的最佳方式。将GPU添加到现有服务器是一个简单的升级过程。此次升级使13个科学驱动程序或子项目能够跨越一系列领域和专业，包括UTC和全国范围内合作机构的研究人员。本科生和研究生受益于使用通过该项目实施的升级后的计算机系。除了本科生和研究生的教学活动外，该项目中追求的13个科学驱动因素还支持目前有资金和无资金支持的研究。此外，阿拉巴马大学、新墨西哥大学和伍斯特理工学院的外部合作者将利用这种可扩展计算资源的很大一部分，通常是与UTC研究人员合作。预计的用户包括UTC的40多名计算科学博士生，以及博士后、硕士和本科生。科学驱动项目补充了集群的现有用途，同时强调图形处理器加速的研究和创意活动，这具体是由这笔资金支持的图形处理器升级实现的。这些科学驱动因素包括：司机和行人预测意图模型的数据驱动方法；查塔努加使用地理信息系统和遥感的地面碳封存；非常规通信的便携式性能优化；从串联质谱仪数据中识别代谢物；以及活性纤维的分子动力学模拟。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。

This project augments an existing computing cluster at the University of Tennessee Chattanooga (UTC) with 36 NVIDIA A100 80GB Graphical Processing Units (GPUs) for 18 of the existing servers. This upgrade provides over a threefold increase in performance for those computers on many workloads, and even higher speed improvement for certain computational areas, such as artificial intelligence problems. GPUs are the best way, at present, to achieve extremely high computational performance cost-effectively on today's servers, workstations, and desktop systems. Adding GPUs to the existing servers is a straightforward upgrade process. The upgrade enables 13 science drivers, or subprojects, spanning a range of domains and specialties, including researchers at UTC and among collaborating institutions nationwide. Undergraduate and graduate students benefit from using the upgraded computing faculty implemented through this project. The 13 science drivers pursued in this project support current funded and unfunded research in addition to teaching activities for both undergraduate and graduate students. Also, external collaborators at the University of Alabama, University of New Mexico, and Worcester Polytechnic Institute will utilize a significant fraction of this scalable computing resource, usually in collaboration with UTC researchers. Expected users include the more than 40 computational science PhD students at UTC, plus postdocs, Masters students, and undergraduate researchers. The science-driver projects complement existing uses of the cluster while emphasizing GPU-accelerated research and creative activities, which are specifically enabled by the GPU upgrade supported by this funding. These science drivers include: Data-driven Methods for Predictive Intention Models for Drivers and Pedestrians; Above Ground Carbon Sequestration Using GIS and Remote Sensing for Chattanooga; Portable Performance Optimizations for Irregular Communication; Identifying Metabolites from the Data of Tandem Mass Spectrometry; and Molecular Dynamics Simulations of Active Filaments.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.