G-AIMD: A Memory-Efficient Subgraph-Centric Framework for Efficient Subgraph Finding on GPUs

Lyuheng Yuan, Akhlaque Ahmad, Da Yan, Jiao Han, Saugat Adhikari, Xiaodong Yu, Yang Zhou

Abstract: Finding all those subgraphs of a big graph that satisfy certain conditions (aka. subgraph finding) is useful in many applications such as community detection and subgraph matching. These problems often generate a search-space tree with size exponential to the size of the input graph. GPUs with thousands of cores are a natural choice to speed up subgraph finding, but existing GPU solutions either conduct BFS on the search-space tree which leads to memory overflow due to intermediate subgraph-size explosion, or they conduct DFS on the search-space tree which is memory-efficient but can be 2 orders of magnitude slower than a BFS solution. In this paper, we present $\mathbf{G}^{2}$ -AIMD, a subgraph-centric framework for efficient subgraph Search on GPUs, which enjoys the efficiency of BFS on the search-space tree, while avoids intermediate subgraph-size explosion with novel system designs such as adaptive chunk-size adjustment and host-memory subgraph buffering, inspired by the additive-increase/multiplicative-decrease (AIMD) algorithm in TCP congestion control. $\mathrm{G}^{2}$ -AIMD provides a convenient subgraph-centric programming interface to facilitate the implementation of subgraph finding algorithms on top, so as to enjoy the above performance merits. $\mathbf{G}^{2}{-}$ AIMD also supports multi-GPU execution where each GPU only needs to load a fraction of the input graph. To demonstrate the efficiency and scalability of $\mathbf{G}^{2}$ -AIMD, two algorithms were implemented on top with additional optimization techniques, and they significantly outperform the existing GPU solutions.