ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy

Zihan Jiang, Fubing Mao, Yapu Guo, Xu Liu, Haikun Liu, Xiaofei Liao, Hai Jin, Wei Zhang

Abstract: Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.