Network motif discovery: A GPU approach

The identification of network motifs has important applications in numerous domains, such as pattern detection in biological networks and graph analysis in digital circuits. However, mining network motifs is computationally challenging, as it requires enumerating subgraphs from a real-life graph, an...

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Bibliographic Details
Published in2015 IEEE 31st International Conference on Data Engineering pp. 831 - 842
Main Authors Wenqing Lin, Xiaokui Xiao, Xing Xie, Xiao-Li Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
Central Processing Unit
Frequency estimation
Graphics processing units
Indexes
Instruction sets
Labeling
Parallel processing
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Summary:The identification of network motifs has important applications in numerous domains, such as pattern detection in biological networks and graph analysis in digital circuits. However, mining network motifs is computationally challenging, as it requires enumerating subgraphs from a real-life graph, and computing the frequency of each subgraph in a large number of random graphs. In particular, existing solutions often require days to derive network motifs from biological networks with only a few thousand vertices. To address this problem, this paper presents a novel study on network motif discovery using Graphical Processing Units (GPUs). The basic idea is to employ GPUs to parallelize a large number of subgraph matching tasks in computing subgraph frequencies from random graphs, so as to reduce the overall computation time of network motif discovery. We explore the design space of GPU-based subgraph matching algorithms, with careful analysis of several crucial factors that affect the performance of GPU programs. Based on our analysis, we develop a GPU-based solution that (i) considerably differs from existing CPU-based methods, and (ii) exploits the strengths of GPUs in terms of parallelism while mitigating their limitations in terms of the computation power per GPU core. With extensive experiments on a variety of biological networks, we show that our solution is up to two orders of magnitude faster than the best CPU-based approach, and is around 20 times more cost-effective than the latter, when taking into account the monetary costs of the CPU and GPUs used.
ISSN:1063-6382
2375-026X
DOI:10.1109/ICDE.2015.7113337