CNS: CORE: Small: Scaling Graph Machine Learning Workloads on Modern Storage

This project designs and develops GNNSuite, a novel unified framework for graph machine learning that leverages emerging storage technology and enables users to deploy large graph neural network (GNN) models on a single commodity machine with high efficiency and low cost. The project's core novelties include methods and software tools for training and serving GNN models on larger-than-memory graphs without affecting the accuracy of downstream tasks. GNNSuite targets classification and prediction use cases, such as chemical synthesis, recommender systems, fraud detection, and large-scale distributed service management. The project involves three sets of tasks that address challenges in large-scale GNN training and inference. First, the investigator designs a training module that employs single-pass in-situ neighborhood sampling on disk-resident data and prefetching optimizations that maximize resource utilization. Second, she develops a three-layered data organization approach that spans memory and secondary storage to facilitate scalable GNN inference on graph streams. Third, the investigator designs and implements a continual training module that leverages experience replay and modern storage capabilities to provide efficient incremental model updates. Project results have the potential to radically increase the accessibility of GNNs, accelerate the integration of graph machine learning tasks in online business analytics pipelines, and inform future research on the next generation of computational storage. 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.