PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining - Qi, Hao; Zhang, Yu; He, Ligang; Luo, Kang; Huang, Jun; Lu, Haoyu; Zhao, Jin; Jin, Hai (2023 60th ACM/IEEE Design Automation Conference (DAC), 2023)
Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.