CC* Regional Computing: Great Plains Extended Network of GPUs for Interactive Experimenters (GP-ENGINE)

CC* 区域计算：用于交互式实验者的 Great Plains GPU 扩展网络 (GP-ENGINE)

• 批准号: 2322218
• 负责人: Grant Scott
• 金额: $ 98.12万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322218&HistoricalAwards=false
• 关键词: CC; Regional; Computing; Great; Plains

The GP-ENGINE project advances the adoption of advanced computing and data resources in the Great Plains Network region. This project will increase the number of researchers and students served by both local and national computing resources, strengthen the capacity and capabilities of campus research computing professionals, and expand the regional capacity for research. Researchers will be able to transition nascent ideas and codes into advanced computing code using locally provisioned advanced computing resources. These codes can be later executed on national high-throughput computing resources. These successes will enhance institutional buy-in for sustainable regional and national research computing systems.The project leverages strong existing collaborations to provision and manage graphics processing unit resources in Missouri, North Dakota, South Dakota, Kansas, Oklahoma, and Arkansas. It trains and supports researchers to adapt their workbench codes into high-throughput computing codes that can be executed on national platforms such as the Open Science Grid and the National Research Platform. The project addresses computing needs of a diverse set of science drivers from across the consortium, including problems in 3D protein molecule generation, satellite image deep learning for wildfire burn area mapping, dark matter and neutrino detection, real-time monitoring of land surface phenology, cybersecurity attack graph generation, and intelligent manufacturing with digital twins. The project improves STEM research and education through adoption of JupyterLab computing notebooks. The deployed cyberinfrastructure supports research and education in a region whose sparse population and geographic size are ideal for developing advanced computing, data, and networking in under-resourced EPSCoR states.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.

GP-Ingine项目在大平原网络地区采用了高级计算和数据资源。 该项目将增加由本地和国家计算资源服务的研究人员和学生的数量，增强校园研究计算专业人员的能力和能力，并扩大区域研究能力。研究人员将能够使用本地提供的高级计算资源将新生的想法和代码转变为高级计算代码。 这些代码稍后可以在国家高通量计算资源上执行。这些成功将增强对可持续区域和国家研究计算系统的机构买入。该项目利用强大的现有合作来提供和管理密苏里州，北达科他州，南达科他州，堪萨斯州，堪萨斯州，俄克拉荷马州和阿肯色州的图形处理单元资源。它培训和支持研究人员将其工作台法规调整为高通量计算法规，这些计算代码可以在国家平台上执行，例如开放科学网格和国家研究平台。该项目解决了整个财团的一组科学驱动因素的计算需求，包括3D蛋白分子产生的问题，卫星图像深度学习，用于野火燃烧面积映射，暗物质和中微子检测，实时监测土地表面物候位，网络表面术，网络安全攻击图以及与数字孪晶的智能制造。 该项目通过采用Jupyterlab计算笔记本来改善STEM研究和教育。部署的网络基础设施在一个地区稀疏的人口和地理规模非常适合在资源不足的EPSCOR国家中开发高级计算，数据和网络的理想选择。该奖项反映了NSF的法定任务，并认为通过使用该基金会的智力和更广泛影响的评估来审查Criteria的评估，并被认为是值得通过评估的支持。