EAGER: The Next Generation of Smart Cyberinfrastructure: Efficiency and Productivity Through Artificial Intelligence

EAGER：下一代智能网络基础设施：通过人工智能提高效率和生产力

• 批准号: 1941085
• 负责人: Valerio Pascucci
• 金额: $ 29.97万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941085&HistoricalAwards=false
• 关键词: EAGER; Next; Generation; Smart; Cyberinfrastructure

Efficient cyberinfrastructure (advanced computing, data, software and networking infrastructure) is a critical component of the support that NSF provides for new discoveries in science and engineering. Cyberinfrastructure is complex and traditionally requires years of human hand-tuning to fully achieve maximal performance for scientific users. We propose to introduce Artificial Intelligence (AI) as a way to automatically and quickly optimize the performance and broadest use of recent NSF-supported advanced computing resources. Through this pilot effort our ultimate aim is to enable and accelerate scientific advances in widely diverse fields such as biology, chemistry, oceanography, materials science, climate modeling, and cosmology.As the research cyberinfrastructure grows rapidly in scale and complexity, it is essential to integrate new technologies based on Machine Learning (ML) and AI to ensure that the investments in new hardware and software components result in proportional improvements in performance and capability. This project will undertake a transformative research activity targeting: (1) scaling ML algorithms to make them easily available to the scientific community; and (2) improving cyberinfrastructure efficiency through AI-based predictive models. This technical work will be complemented and informed by a community engagement effort to jointly catalog the state of the art and identify future challenges and opportunities in enabling a new smart cyberinfrastructure.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.

高效的网络基础设施(先进的计算、数据、软件和网络基础设施)是NSF为科学和工程领域的新发现提供支持的关键组成部分。网络基础设施是复杂的，传统上需要多年的人工调整才能完全为科学用户实现最大性能。我们建议引入人工智能(AI)，作为一种自动、快速地优化性能和最广泛使用NSF支持的最新高级计算资源的方法。通过这项试点工作，我们的最终目标是推动和加速生物、化学、海洋学、材料科学、气候建模和宇宙学等广泛领域的科学进步。随着研究网络基础设施的规模和复杂性迅速增长，整合基于机器学习(ML)和人工智能的新技术至关重要，以确保在新硬件和软件组件上的投资导致性能和能力的比例提高。该项目将开展一项变革性的研究活动，目标是：(1)扩展ML算法，使其易于为科学界所用；以及(2)通过基于人工智能的预测模型提高网络基础设施的效率。这项技术工作将得到社区参与工作的补充和通报，该工作旨在联合编目最先进的技术，并确定在启用新的智能网络基础设施方面的未来挑战和机会。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。

项目成果

High-Quality Progressive Alignment of Large 3D Microscopy Data

大型 3D 显微镜数据的高质量渐进对齐

• DOI: 10.1109/ldav57265.2022.9966406
• 发表时间: 2022
• 期刊: 2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV
• 作者: Venkat, Aniketh;Hoang, Duong;Gyulassy, Attila;Bremer, Peer-Timo;Federer, Frederick;Angelucci, Alessandra;Pascucci, Valerio
• 通讯作者: Pascucci, Valerio

Advancing Fusion with Machine Learning Research Needs Workshop Report

• DOI: 10.1007/s10894-020-00258-1
• 发表时间: 2019-09
• 期刊: Journal of Fusion Energy
• 影响因子: 1.1
• 作者: D. Humphreys;A. Kupresanin;D. Boyer;J. Canik;E. Cyr;R. Granetz;J. Hittinger;E. Kolemen;E. Lawrence;Valerio Pascucci

Vector Field Decompositions using Multiscale Poisson Kernel

使用多尺度泊松核的矢量场分解

• DOI: 10.1109/tvcg.2020.2984413
• 发表时间: 2020
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Bhatia, Harsh;Kirby, Robert M.;Pascucci, Valerio;Bremer, Peer-Timo
• 通讯作者: Bremer, Peer-Timo

The Materials Commons Data Repository

材料共享数据库

• DOI: 10.1109/escience55777.2022.00060
• 发表时间: 2022
• 期刊: 2022 IEEE 18th International Conference on e-Science (e-Science
• 作者: Tarcea, Glenn;Puchala, Brian;Berman, Tracy;Scorzelli, Giorgio;Pascucci, Valerio;Taufer, Michela;Allison, John
• 通讯作者: Allison, John

SpotSDC: Revealing the Silent Data Corruption Propagation in High-Performance Computing Systems

• DOI: 10.1109/tvcg.2020.2994954
• 发表时间: 2020-05
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Zhimin Li;Harshitha Menon;D. Maljovec;Y. Livnat;Shusen Liu;K. Mohror;P. Bremer;Valerio Pascucci