Efficient and Scalable Graph Pattern Mining on GPUs

Abstract

We describe G 2 Miner, the first Graph Pattern Mining (GPM) framework that runs on multiple GPUs. G 2 Miner uses pattern-aware, input-aware and architecture-aware search strategies to achieve high efficiency on GPUs. To simplify programming, it provides a code generator that automatically generates pattern-aware CUDA code. G 2 Miner flexibly supports both breadth-first search (BFS) and depth-first search (DFS) to maximize memory utilization and generate sufficient parallelism for GPUs. For the scalability of G 2 Miner, we propose a customized scheduling policy to balance work among multiple GPUs. Experiments on a V100 GPU show that G 2 Miner is 5.4× and 7.2× faster than the two state-ofthe-art single-GPU systems, Pangolin and PBE, respectively. In the multi-GPU setting, G 2 Miner achieves linear speedups from 1 to 8 GPUs, for various patterns and data graphs. We also show that G 2 Miner on a V100 GPU is 48.3× and 15.2× faster than the state-of-the-art CPU-based systems, Peregrine and GraphZero, on a 56-core CPU machine.