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: 2326495
• 负责人: Dingwen Tao
• 金额: $ 30万
• 依托单位: Indiana University
• 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=2326495&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联系的数据，通常表示为图形，这与许多关键的现实世界应用有关。这导致图形神经网络（GNN）的患病率不断提高，该技术将人工智能（AI）的好处扩展到基于图的应用程序。 GNN具有有希望的潜力，可以显着影响社会，从加速药物发现和防止供应链中断，避免级联电网故障并确定社交媒体上的错误信息。但是，目前，由于图形的巨大规模和复杂的性质（例如极端的稀疏性和不规则性）引起的计算效率低下，这种潜力的实现构成了挑战，这对GNN的实际部署构成了挑战。该项目旨在弥合GNN所需的计算效率与当前性能之间的差距，这主要是由于GNN计算中所需的沟通量很大。此外，该项目通过提高罗切斯特大学和印第安纳大学的AI和系统相关课程和外展活动的质量，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，以解决医学，公共基础设施和经济发展领域的问题，以及许多其他问题，对于共和国的运作良好和经济繁荣至关重要。该项目旨在开发一种革命性的交流减少方法，该方法通过软件硬件共同设计有机地集成了通用的图形局部性局部性增强和高比率压缩。这项研究的结构围绕三个主要推力：（1）与当前领先的主要方法相比，通过硬件软件共同设计通过硬件软件共同设计开发了在线图局部性增强器，从而提供了明显的多功能性和额外的沟通需求。 （2）创建有效的有损耗压缩机，该压缩机可实现图形数据的高比例，差异障碍的压缩和解压缩，包括图形嵌入和拓扑信息。 （3）对有效合并图形局部性增强子和图形压缩机的方法的研究，使它们可以相互受益。这些策略共同解决了GNN中持续的沟通瓶颈，并释放了其具有社会利益的潜力。此外，该项目旨在解决以下查询：局部增强和数据压缩的协作整合是否是否可以为通用图形问题提供开创性的解决方案。该奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛的影响来评估NSF的法定任务。