OAC Core: Scalable Graph ML on Distributed Heterogeneous Systems

图机器学习（Graph ML）是机器学习中处理图数据的子学科，采用图机器学习的方法在许多关键科学和工程领域中变得越来越重要。例如，图嵌入的预测能力已被有效地用于社交媒体、生物学、药理学和知识理解等领域。然而，这样的方法通常伴随着昂贵的计算足迹，因为计算通常需要在具有数十亿不同类型的顶点和边的非常大且高度异构的静态和动态图上实时执行。该项目旨在进行多管齐下的研究，以创建网络基础设施（CI）工具包，从而在新兴的异构分布式系统上运行此类复杂的Graph ML应用程序。该项目的目标是为跨越多个科学和工程领域的关键图形工作流开发高性能Graph ML算法，目标是由多核处理器，图形处理单元（GPU），现场可编程门阵列（FPGA），加速器和高速缓存一致性接口互连的高带宽存储器组成的分布式异构系统。该项目开发了一个可扩展的，可部署的和强大的CI工具包，包括：（1）新颖的图采样算法和高效的Graph ML模型，用于静态和动态图上的低复杂度训练和推理计算;（2）异构感知硬件映射方法，以加速这些算法和模型;以及（3）用于自动设计生成的软件和硬件库。该项目为ML和数据科学社区开发概念验证软件，以促进各种大规模应用程序的端到端部署。鉴于图神经网络正日益成为分析许多不同领域数据的重要工具，该项目的成果将对广泛的学科产生重大影响，包括依赖边缘计算的领域，如自动驾驶汽车和智能城市。该项目有一个强有力的计划，将研究纳入教育计划，并侧重于促进少数民族和经济困难背景的学生参与研究的活动。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Methods that employ graph machine learning (Graph ML), which is a sub-discipline within machine learning that deals with graph data, are becoming important in many key science and engineering domains. For example, the predictive power of graph embedding has been effectively utilized in domains such as social media, biology, pharmacology, and knowledge understanding. However, such methods typically come with an expensive computational footprint, as the computations often need to be performed in real-time on very large and highly heterogeneous static and dynamic graphs with billions of vertices and edges of different types. This project aims at conducting multi-pronged research to enable creation of a cyberinfrastructure (CI) toolkit to run such complex Graph ML applications on emerging heterogeneous distributed systems. The objective of this project is to develop high-performance Graph ML algorithms for key graph workflows spanning multiple scientific and engineering domains targeting distributed heterogeneous systems composed of multi-core processors, Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), accelerators and high bandwidth memory interconnected with cache coherent interfaces. The project develops a scalable, deployable, and robust CI toolkit consisting of: (1) novel graph sampling algorithms and efficient Graph ML models for low complexity training and inference computation on static and dynamic graphs; (2) a heterogeneity-aware hardware mapping methodology to accelerate these algorithms and models; and (3) software and hardware libraries for automatic design generation. The project develops proof of concept software for the ML and Data Science communities to facilitate end-to-end deployment of various large-scale applications. Given that graph neural networks are increasingly becoming an important tool for analyzing data in many diverse domains, the outcomes of this project will have a strong impact across a broad range of disciplines, including domains that rely on edge computing, such as autonomous vehicles and smart cities. The project has a robust plan to integrate research into education programs and focuses on activities that promote involvement of students from minority and economically disadvantaged backgrounds into the research.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.