Collaborative Research: OAC Core: Distributed Graph Learning Cyberinfrastructure for Large-scale Spatiotemporal Prediction

合作研究：OAC Core：用于大规模时空预测的分布式图学习网络基础设施

• 批准号: 2403313
• 负责人: Yue Cheng
• 金额: $ 30万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2403313&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; Core; Distributed

Graph Neural Networks (GNNs) have extended Deep Neural Networks’ success from independent data points to relational data points, such as observations collected on-site from environmental sensors (e.g., humidity, temperature, PM2.5, etc.) widely distributed in different spatial locations. While most existing works focus on proof-of-concept on relatively small, well-curated data, with offline settings, real-world scientific research, and applications need more capable GNN models, which can effectively learn from large-scale, real-time, geographically distributed (geo-distributed) and diversely different (heterogeneous) data. This project aims to chart a radically new cyberinfrastructure solution for training large-spatial GNNs to fill this gap. The success of this project will provide a cyberinfrastructure that overcomes the fundamental computational and communication bottlenecks for a broad range of domain science applications that rely on massive spatiotemporal prediction. The proposed algorithms and systems will be ideal for cultivating a deeper understanding of designing large machine-learning systems at a geo-distributed scale, teaching and training students and peers, and providing graduate and undergraduate students with new courses, research, and internship opportunities. This project aims to develop a comprehensive set of graph construction and partitioning methods, distributed learning algorithms, and cyberinfrastructure designs to support large-scale GNNs for real-world spatiotemporal data in geospatial scientific research and applications. The project will address significant research challenges, including (1) formulating spatiotemporal prediction within a geographically inspired graph deep learning framework, (2) enabling highly accurate, efficient, and cost-effective spatiotemporal prediction tasks across vast, geographically dispersed datasets, and (3) integrating spatial correlation, spatial heterogeneity, spatial computing parallelism, and geographic communication efficiency. The research is organized around several key research themes: (1) Creating a universal framework for constructing graphs from spatiotemporal data, determining spatial relationships, and filling in missing node attributes. (2) Developing a centralized spatiotemporal graph learning infrastructure that leverages multiple edge micro-datacenters for collaborative GNN model learning. (3) Establishing a decentralized spatiotemporal graph learning infrastructure that supports decentralized geographical multitask learning to address spatial heterogeneity.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.

图神经网络 (GNN) 将深度神经网络的成功从独立数据点扩展到了关系数据点，例如从广泛分布在不同地区的环境传感器（例如湿度、温度、PM2.5 等）现场收集的观测结果虽然大多数现有的工作都集中在相对较小、精心策划的数据上进行概念验证，并且具有离线设置，但现实世界的科学研究和应用需要更强大的 GNN 模型，这些模型可以有效地从大规模、该项目旨在制定一种全新的网络基础设施解决方案，用于训练大型空间 GNN，以填补这一空白。克服了依赖于大规模时空预测的广泛领域科学应用的基本计算和通信瓶颈，所提出的算法和系统将非常适合培养对大型设计的更深入理解。该项目旨在开发一套全面的分布式图构建和分区方法，为学生和同行提供教学和培训，并为研究生和本科生提供新的课程、研究和实习机会。该项目将解决重大研究挑战，包括（1）在受地理启发的图深度学习中制定时空预测。框架，（2）在巨大的、地理上分散的数据集上实现高精度、高效且具有成本效益的时空预测任务，以及（3）集成空间相关性、空间异质性、空间计算并行性和地理通信效率。几个关键研究主题：（1）创建一个通用框架，用于从时空数据构建图、确定空间关系并填充缺失的节点属性（2）开发利用多个边的集中式时空图学习基础设施。 (3) 建立去中心化的时空图学习基础设施，支持去中心化的地理多任务学习，以解决空间异质性。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进行评估，认为值得支持。以及更广泛的影响审查标准。