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

协作研究：SHF：小型：LEGAS：大规模学习演化图

• 批准号: 2331302
• 负责人: Binghui Wang
• 金额: $ 29.13万
• 依托单位: Illinois Institute of Technology
• 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: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331302&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; LEGAS

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.

图形神经网络（GNN）是图形上的深度学习模型的新兴类别，具有许多成功的应用，例如，建议系统，药物发现，社交网络分析和代码脆弱性检测。但是，GNNS的计算涉及复杂的矩阵和矢量操作时面临低效率问题。此外，当应用于动态变化的图表时，效率发出的问题加剧了。该项目开创了为静态和动态图开发有效的GNN算法和计算系统的努力，这些算法和计算系统可以利用世界一流的图形处理单元（GPU）计算设施。该项目有助于国家学习和计算系统中对专业人员的日益增长的需求。该项目生产了一个高性能的软件库，该库是学术界，国家实验室和行业的科学和工程从业人员的基础工具。此外，还进行了教育工作，以将研究结果纳入研究生和本科课程发展。进行外展和教育活动是为了促进K-12，本科，女性和代表性不足的少数群体的参与。该项目的总体目标是通过算法和静态图和动态图设计有效的GNN框架。在此方面，该项目设计了三个协同研究。具体而言，推力1通过设计有效的新型GNN算法，允许保留整个图并提供收敛保证，从而提高了算法水平的效率。推力2通过通过有效的工作负载调度和减少同步开销来设计有效的GPU上的有效计算技术来提高系统性能。此外，Thrust 3将技术的技术结合在推力1和2中，并设计了解决动态图中独特算法和计算挑战的新颖策略。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的影响审查标准来通过评估来通过评估来支持的。