Lin Hu’s paper of graph pattern mining “GAMMA: A Graph Pattern Mining Framework for Large Graphs on GPU” has been accepted by ICDE 2023.

Graph pattern mining (GPM) is getting increasingly important recently. There are many parallel frameworks for GPM, many of which suffer from performance. GPU is a powerful option for graph processing, which has excellent potential for performance improvement; however, 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, and it makes full use of host memory to process large graphs. Specifically, GAMMA adopts a self-adaptive implicit host memory access manner to achieve high bandwidth, which is transparent to users. GAMMA 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. Experimental results show that GAMMA has scalability advantages in graph size over the state-of-the-art by an order of magnitude, and is also faster than existing GPM systems.