Collaborative Research: SHF: Small: LEGAS: Learning Evolving Graphs At Scale

图神经网络（gnn）是一类新兴的基于图的深度学习模型，有许多成功的应用，如推荐系统、药物发现、社交网络分析和代码漏洞检测。然而，gnn的计算由于涉及到复杂的矩阵和向量运算而面临效率低下的问题。此外，当应用于动态变化的图形时，效率问题会加剧。该项目是开发高效GNN算法和静态和动态图形计算系统的先驱，可以利用世界一流的图形处理单元（GPU）计算设施。该项目有助于满足国家对机器学习和计算系统专业人员日益增长的需求。这个项目产生了一个高性能的软件库，作为来自学术界、国家实验室和工业的科学和工程实践者的基础工具。此外，教育方面也作出努力，将研究成果整合到研究生和本科生的课程发展中。开展外联和教育活动，以促进K-12、本科生、女性和代表性不足的少数民族的参与。该项目的总体目标是通过静态和动态图的算法和系统协同设计，设计一个高效的GNN框架。为此，本项目设计了三个协同研究重点。具体来说，Thrust 1通过设计新颖的GNN算法，从算法层面提高了效率，这些算法既高效，又允许保留整个图，并提供收敛保证。Thrust 2通过在gpu上设计高效的计算技术来提高系统性能，有效地调度工作负载并减少同步开销。此外，Thrust 3整合了Thrust 1和Thrust 2中的技术，并设计了新颖的策略来解决动态图中独特的算法和计算挑战。该奖项反映了美国国家科学基金会的法定使命，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。

Graph Neural Networks (GNNs) are an emerging class of deep learning models on graphs, with many successful applications, such as, recommendation systems, drug discovery, social network analysis, and code vulnerability detection. However, the computation for GNNs faces a low efficiency problem as they involve complex matrix and vector operations. Further, when applied to graphs that are dynamically changing, the efficiency issue exacerbates. This project pioneers the effort of developing efficient GNN algorithms and computation systems for both static and dynamic graphs that can take advantage of world-class Graphics Processing Unit (GPU) computing facilities. This project contributes to the growing national need for professionals in machine learning and computation systems. This project produces a high-performance software library that serves as a foundational tool for fellow science and engineering practitioners from academia, national laboratories, and industry. Additionally, educational efforts are made to integrate the research findings into graduate and undergraduate curriculum development. Outreach and educational activities are conducted to promote the participation of K-12, undergraduate, female, and underrepresented minorities. The overarching goal of this project is to design an efficient GNN framework via algorithm and system co-design for both static and dynamic graphs. Towards that, this project designs three synergistic research thrusts. Specifically, Thrust 1 improves the efficiency from the algorithm level by designing novel GNN algorithms that are efficient, allow the entire graph to be retained, and offer convergence guarantees. Thrust 2 advances the system performance by designing efficient computation techniques on GPUs with efficient workload scheduling and reduced synchronization overhead. In addition, Thrust 3 incorporates the techniques in Thrusts 1 and 2, and designs novel strategies to address the unique algorithm and computation challenges in dynamic graphs.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.