A graph pattern mining framework for large graphs on GPU

Lin Hu, Yinnian Lin, Lei Zou, M. Tamer Özsu

Abstract: Graph pattern mining (GPM) is an important problem in graph processing. There are many parallel frameworks for GPM, many of which suffer from low performance. GPU is a powerful option for accelerating graph processing, but parallel GPM algorithms produce a large number of intermediate results, limiting GPM implementations on GPU. In this paper, we present GAMMA, an out-of-core GPM framework on GPU, that makes full use of host memory to process large graphs. GAMMA adopts a self-adaptive implicit host memory access approach to achieve high bandwidth, which is transparent to users. It provides flexible and effective interfaces for users to build their algorithms. We also propose several optimizations over primitives provided by GAMMA in the out-of-core GPU system, as well as optimizations to perform set intersections since they are widely used in GPM. Experimental results show that GAMMA scales better with graph size over the state-of-the-art approaches—by an order of magnitude—and is also faster than existing GPM systems.

External IDs:dblp:journals/vldb/HuLZO25