CC* Compute: Nevada Bridge to AI-enabled Scientific & Engineering Computing (NvBAISEC)

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。内华达大学里诺分校的目标是在现有的中央处理器（CPU）集群中添加一个基于10节点A100图形处理器（GPU）的集群，以创建一个更集中的设施来扩展整个校园的访问。这些项目旨在满足校园内各种研究小组目前和预期的人工智能（AI）/机器学习（ML）工作负载。21世纪的科学和工程正在被研究计算和数据规模的不断扩大所改变。尽管对大规模和以数据为中心的计算资源的需求有所增加，但在校园层面为领域科学家提供必要的工具和支持仍然是一场斗争。具体来说，分散式计算实践不仅因为缺乏规模和支持而成为研究的瓶颈，而且还将计算与更高的性能和更深的存储和网络脱钩。因此，需要改变机构网络基础设施战略，并确定以下优先指导事项：（1）改善用户友好的访问;（2）消除在使用可扩展基础设施方面的障碍;以及（3）为下一代工作流程建立多学科社区。内华达州大学，里诺（UNR）将在其现有的CPU集群中添加一个新的基于A100 GPU的集群，为跨学科计算基础设施引入一套新的范式，并扩大整个校园的访问。每个A100 OnDemand节点都配备了24核CPU和A100 GPU加速器，与Infiniband交换机互连，通过开放科学网格在UNR和外部提供对科学驱动程序的有效访问。该集群有可能通过满足三个主要需求来满足UNR校园内各种研究小组的当前和预期工作负载：（1）通过A100 GPU的多实例GPU（GPU）功能支持数十到数百个并发交互会话用户的能力;（2）支持现代张量核心架构，以促进机器学习工作流;以及（3）在UNR研究外围网络上的托管和与高性能存储的邻接。该项目是资源不足的机构在计算资源和能力方面迈出的重要一步。与开放科学网格（OSG）的整合计划将为校园内的研究人员打开一扇按需访问数百万小时异构计算周期的大门。该项目由高级网络基础设施办公室（OAC）和刺激竞争研究的既定计划（EPSCoR）共同资助。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The University of Nevada-Reno aims to add a 10 node A100 Graphics Processing Unit (GPU)-based cluster to their pre-existing Central Processing Unit (CPU) cluster with the expressed aim of creating a more central facility to expand access across campus. The projects aims to meet present and anticipated Artificial Intelligence (AI)/Machine Learning (ML) workloads in various research group on campus. Twenty-first century science and engineering is being transformed by the increasing scales of research computing and data. Despite the increase in the demand for large-scale and data-centric computational resources, it is still a struggle to provide domain scientists with the necessary tools and support at campus-levels. Specifically, decentralized computing practices not only bottleneck research because of lack of scale and support, but also decouple computing from higher performance and deeper storage and networks. Accordingly, shifts in institutional cyberinfrastructure strategies are required, with the following guiding priorities: (1) improving user friendly access; (2) removing perceived barriers in the use of scalable infrastructure; and (3) building multidisciplinary communities for next-generation workflows.The University of Nevada, Reno (UNR) will add a new A100 GPU-based cluster to its pre-existing CPU cluster to introduce a new set of paradigms for interdisciplinary computing infrastructure and expand access across campus. Each A100 OnDemand node is equipped with 24-core CPUs and A100 GPU accelerators, interconnected with Infiniband switches to provide effective access to science drivers at UNR and externally through Open Science Grid. This cluster has potential to meet UNR’s present and anticipated workloads of various research groups on campus by addressing three main requirements: (1) capability to support dozens to hundreds of concurrent interactive session users through the Multi Instance GPU (MIG) capabilities of the A100 GPUs; (2) support for modern tensor core architectures to facilitate machine learning workflows; and (3) colocation on the UNR research perimeter network and adjacency to high performance storage. This project is a significant step for the under-resourced institution in computing resources and capability. The planned integration with Open Science Grid (OSG) will open a door to on-demand access to millions of hours of heterogeneous compute cycles for researchers on campus.This project is funded through the collaborative efforts of the Office of Advanced Cyberinfrastructure (OAC) and the Established Program to Stimulate Competitive Research (EPSCoR).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.