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

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.