PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining

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 thi...

Full description

Saved in:
Bibliographic Details
Published in:2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors: Qi, Hao, Zhang, Yu, He, Ligang, Luo, Kang, Huang, Jun, Lu, Haoyu, Zhao, Jin, Jin, Hai
Format: Conference Proceeding
Language:English
Published: IEEE 09.07.2023
Subjects:
Computational efficiency
Data mining
Design automation
Hardware
Optimization
Software
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
DOI:10.1109/DAC56929.2023.10247902