Parallel graph mining with GPUs

Abstract

Frequent graph mining is an important though computationally hard problem because it requires enumerating possibly an exponential number of candidate subgraph patterns, and checking their presence in a database of graphs. In this paper, we propose a novel approach for parallel graph mining on GPUs, which have emerged as a relatively cheap but powerful architecture for general purpose computing. However, the thread-model for GPUs is different from that of CPUs, which makes the parallelization of graph mining algorithms on GPUs a challenging task. We investigate the major challenges for GPU-based graph mining. We perform extensive experiments on several real-world and synthetic datasets, achieving speedups up to 9 over the sequential algorithm.