Parallel Graph Mining with GPUs

Robest Kessl, Nilothpal Talukder, Pranay Anchuri, Mohammed J. Zaki
Czech Technical University, Prague, Czech Republic
JMLR: Workshop and Conference Proceedings 36:1-16, 2014

   title={A Clustering Algorithm Merging MCMC and EM Methods Using SQL Queries},

   author={Matusevich, David and Ordonez, Carlos},

   booktitle={Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications},




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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.
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