OAC Core: Scalable Graph ML on Distributed Heterogeneous Systems

University Of Southern California, Los Angeles CA

Investigators

Abstract

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.