Collaborative Research: SHF: Small: Reimagining Communication Bottlenecks in GNN Acceleration through Collaborative Locality Enhancement and Compression Co-Design

协作研究：SHF：小型：通过协作局部性增强和压缩协同设计重新想象 GNN 加速中的通信瓶颈

• 批准号: 2326494
• 负责人: Tong Geng
• 金额: $ 30万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326494&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Reimagining

The digital revolution has generated a vast volume of interconnected data, often represented as graphs, which is pertinent to numerous critical real-world applications. This has led to the increasing prevalence of Graph Neural Networks (GNNs), a technique that extends the benefits of Artificial Intelligence (AI) to graph-based applications. GNNs hold promising potential to significantly impact society, from accelerating drug discovery and preventing supply chain disruptions, to averting cascading power grid failures and identifying misinformation on social media. However, the actualization of such potential is currently impeded by computational inefficiencies caused by the colossal size and intricate nature (such as extreme sparsity and irregularity) of graphs, which pose challenges to the practical deployment of GNNs. This project aims to bridge the gap between the computational efficiency required for GNNs and their current performance, primarily due to the uniquely heavy load of communication required in GNN computation. In addition, the project enriches the educational experience of undergraduate and graduate students in the US by enhancing the quality of AI and system-related courses and outreach activities at the University of Rochester and Indiana University. Successful completion of this research project can unlock the immense potential of GNNs to solve problems in fields of medicine, public infrastructure, and economic development, among many other issues critical to the well-functioning of the republic and the prosperity of its economy. This project aims to develop a revolutionary communication reduction method that organically integrates on-the-fly versatile graph locality enhancement and high-ratio compression through software-hardware co-design. The research is structured around three primary thrusts: (1) The development of an on-the-fly graph locality enhancer via hardware-software co-design, providing significant versatility and additional reductions in communication demands compared to current leading methods. (2) The creation of an efficient lossy compressor that enables high-ratio, error-bounded compression and decompression for graph data, including both graph embedding and topology information. (3) The investigation into methods for effectively combining the graph locality enhancer and graph compressor, allowing them to mutually benefit each other. These strategies together directly address the persistent communication bottlenecks in GNNs and unleash their potential for societal benefits. Moreover, this project aims to resolve the following query: whether a collaborative integration of locality enhancement and data compression, the two most prevalent communication optimization approaches, can provide a ground-breaking solution to general graph problems.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.

数字革命产生了大量相互关联的数据，通常以图表的形式表示，与众多关键的现实应用程序相关。这导致图神经网络 (GNN) 的日益普及，这种技术将人工智能 (AI) 的优势扩展到基于图的应用程序。 GNN 具有对社会产生重大影响的巨大潜力，从加速药物发现和防止供应链中断，到避免级联电网故障和识别社交媒体上的错误信息。然而，目前这种潜力的实现受到图的巨大尺寸和复杂性（例如极端稀疏性和不规则性）导致的计算效率低下的阻碍，这对 GNN 的实际部署提出了挑战。该项目旨在弥合 GNN 所需的计算效率与其当前性能之间的差距，这主要是由于 GNN 计算所需的独特的重通信负载。此外，该项目通过提高罗切斯特大学和印第安纳大学的人工智能和系统相关课程以及外展活动的质量，丰富了美国本科生和研究生的教育经验。该研究项目的成功完成可以释放 GNN 的巨大潜力，解决医学、公共基础设施和经济发展领域的问题，以及对共和国良好运作和经济繁荣至关重要的许多其他问题。该项目旨在开发一种革命性的通信减少方法，通过软硬件协同设计将动态通用图局部性增强和高比率压缩有机地集成在一起。该研究围绕三个主要目标进行：（1）通过硬件-软件协同设计开发动态图局部性增强器，与当前领先方法相比，提供显着的多功能性并进一步减少通信需求。 (2) 创建高效的有损压缩器，能够对图数据（包括图嵌入和拓扑信息）进行高比率、误差有限的压缩和解压缩。 （3）研究有效结合图局部性增强器和图压缩器的方法，使它们互惠互利。这些策略共同直接解决 GNN 中持续存在的通信瓶颈，并释放其社会效益的潜力。此外，该项目旨在解决以下问题：局部性增强和数据压缩这两种最流行的通信优化方法的协作集成是否可以为一般图问题提供突破性的解决方案。该奖项反映了 NSF 的法定使命，并具有通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。