PlatoD2GL: An Efficient Dynamic Deep Graph Learning System for Graph Neural Network Training on Billion-Scale Graphs

Recently, huge interests in both academic and in-dustry have been posed to Graph Neural Network due to its power on revealing the topological information inside the data. To support the real-world applications, most of (if not all) which contain large-scale graphs with billions of edges, a number of graph-based deep learning systems have been proposed and implemented. However, all of them fail to efficiently process the dynamic graphs in terms of both memory and time cost. The state-of-the-art suffers from two issues: (1) expensive memory consumption due to the huge indexing overhead of numerous key-value pairs in traditional key-value topology storage and (2) inefficient dynamic updating due to the heavy updates on indexing structures for weighted neighbor sampling. In this paper, we proposed a Dynamic Graph-based Learning System PiatoD2GL to address above two issues. Specifically, we design a novel and effective non-key-value data structure, termed samtree, for dynamic topology storage, which largely reduces the memory cost. In addition, we propose an efficient sampling indexing structure FSTable by utilizing Fenwick tree, guaranteeing the efficiency of both dynamic updates and weighted sampling. Comprehensive experiments have demonstrated that, for dynamic updating the graph topology, the efficiency of our system by up to 79.8% and by up to 6.3 times in terms of memory and time cost, respectively. Now, our system serves the major traffic in WeChat Platform for training various GNN models.