Parallel Graph Mining with GPUs
Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, PMLR 36:1-16, 2014.
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.