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: 2403312
• 负责人: Liang Zhao
• 金额: $ 29.96万
• 依托单位: Emory University
• 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=2403312&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）已将深层神经网络的成功从独立的数据点扩展到关系数据点，例如从环境传感器（例如，湿度，温度，PM2.5等）收集的现场观测值，广泛分布在不同的空间位置。尽管大多数现有作品都集中在相对较小，曲线良好的数据上的概念验证上，但具有离线设置，现实世界的科学研究和应用需要更有能力的GNN模型，这些模型可以有效地从大规模，实时，地理上分布（地理分布）和多样化的（多样化）和多样化（异构）数据中学习。该项目旨在绘制一种激进的网络基础结构解决方案，用于训练大空间GNN，以填补这一空白。该项目的成功将提供一个网络基础结构，以克服依赖大量时空预测的广泛领域科学应用程序的基本计算和通信瓶颈。拟议的算法和系统将是对设计大型机器学习系统的更深入的理解，以地理分配的规模，教学和培训学生和同龄人，并为毕业生和本科生提供新的课程，研究和实习机会。该项目旨在开发一组全面的图形构建和分区方法，分布式学习算法以及网络基础设施设计，以支持大型GNNS用于现实世界上的空间科学研究和应用。该项目将应对重大的研究挑战，包括（1）在地理启发的图形深度学习框架内进行空间预测，（2）能够在广泛的，地理分散的数据集中高度准确，高效，具有成本效益的空间预测任务，以及（3）空间相关性，空间异质性，平面质量，spatial spatismiss和（3）该研究围绕几个关键研究主题进行组织：（1）创建一个通用框架，用于从空间时间数据中构造图形，确定空间关系并填充缺失的节点属性。 （2）开发一个集中的空间图学习基础架构，该基础架构利用多个边缘微型代表体进行协作GNN模型学习。 （3）建立一个分散的空间图图学习基础架构，该基础架构支持分散的地理多任务学习以解决空间异质性。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得的支持。