CC* Compute: Interactive Data Analysis Platform

CC* 计算：交互式数据分析平台

• 批准号: 2019007
• 负责人: Paul Padley
• 金额: $ 39.76万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019007&HistoricalAwards=false
• 关键词: CC; Compute; Interactive; Data; Analysis

Rice University researchers engaged in groundbreaking data-intensive science and engineering increasingly depend on access to real-time data analysis facilities required for their research. These research activities include image processing, computer vision, and machine learning, spanning multiple fields, such as geological sciences, statistics, computer science, and physics. Each of these problems areas or use cases can be addressed by shared computational infrastructure leveraging GPU accelerators for interactive computing. The system provides a significant resource for enabling science but also for educating the next generation of computational scientists in the latest GPU-computing techniques through the outreach of the Center for Research Computing. The resource includes nine compute nodes, each with 40 cores, 384GB RAM, 4TB NVMe storage, and 8 NVIDIA Quadro RTX 6000 GPUs. The systems are interconnected via high-performance networking and hosted on a Science DMZ integrating them with the Open Science Grid as well as commercial cloud allowing both increased utilization as part of national OSG efforts and the ability to utilize cloud resources for load bursting. The system leverages an open-source software stack designed to support containerization, enabling each researcher to utilize their own unique set of software and toolkits while sharing common hardware and a common cloud access platform. Moreover, the infrastructure is part of a larger technology ecosystem that leverages federated identity and access management as part of InCommon, advanced networking with science DMZ, and Information Security Office that supports not only university data and technology security but includes targeted outreach for research data and protocol security.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.

莱斯大学从事开创性数据密集型科学和工程的研究人员越来越依赖于他们的研究所需的实时数据分析设施。这些研究活动包括图像处理、计算机视觉和机器学习，涉及地质科学、统计学、计算机科学和物理等多个领域。这些问题、领域或用例都可以通过利用GPU加速器进行交互计算的共享计算基础设施来解决。该系统不仅为推动科学研究提供了重要资源，而且还通过研究计算中心的推广，为下一代计算科学家提供了最新的图形处理单元计算技术方面的教育。该资源包括9个计算节点，每个节点具有40核、384 GB RAM、4TB NVMe存储和8个NVIDIA Quadro RTX 6000 GPU。这些系统通过高性能网络互连，并托管在科学DMZ上，将它们与开放科学网格和商业云集成在一起，从而既可以作为国家OSG工作的一部分提高利用率，又可以利用云资源来应对负载激增。该系统利用旨在支持集装化的开源软件堆栈，使每个研究人员能够利用自己独特的一套软件和工具包，同时共享共同的硬件和共同的云访问平台。此外，该基础设施是更大的技术生态系统的一部分，该生态系统利用联合身份和访问管理作为与科学DMZ和信息安全办公室的不常见的高级网络的一部分，该办公室不仅支持大学数据和技术安全，还包括针对研究数据和协议安全的有针对性的扩展。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。