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）已经将深度神经网络的成功从独立数据点扩展到关系数据点，例如从环境传感器现场收集的观察结果（例如，湿度、温度、PM2.5等）广泛分布在不同的空间位置。虽然大多数现有的工作都集中在相对较小的概念验证，精心策划的数据，离线设置，现实世界的科学研究和应用程序需要更强大的GNN模型，可以有效地从大规模，实时，地理分布（地理分布）和不同（异构）的数据中学习。该项目旨在制定一个全新的网络基础设施解决方案，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设施，克服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在地理分布范围内设计大型机器学习系统的更深入理解，教学和培训学生和同行，并为研究生和本科生提供新课程，研究和实习机会。该项目旨在开发一套全面的图形构建和分区方法，分布式学习算法和网络基础设施设计，以支持地理空间科学研究和应用中真实世界时空数据的大规模GNN。该项目将解决重大的研究挑战，包括（1）在地理启发图深度学习框架内制定时空预测，（2）在地理上分散的庞大数据集上实现高度准确，高效和具有成本效益的时空预测任务，以及（3）集成空间相关性，空间异构性，空间计算并行性和地理通信效率。本研究围绕以下几个关键研究主题展开：（1）建立一个通用的时空数据图构建框架，确定空间关系，并填充缺失的节点属性。(2)开发集中式时空图学习基础设施，利用多个边缘微处理器进行协作GNN模型学习。(3)建立一个分散的时空图学习基础设施，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的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。