9508

Novel Multi-Layer Network Decomposition Boosting Acceleration of Multi-core Algorithms

Athanasios K. Grivas, Terrence Maky, Alex Yakovlev, Jonny Wrayz
School of Electrical and Electronic Engineering, Newcastle University, UK
24th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP 2013), 2013

@article{grivas2013novel,

   title={Novel Multi-Layer Network Decomposition Boosting Acceleration of Multi-core Algorithms},

   author={Grivas, Athanasios K and Mak, Terrence and Yakovlev, Alex and Wray, Jonny},

   year={2013}

}

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Complex networks are a technique for the modeling and analysis of large data sets in many scientific and engineering disciplines. Due to their excessive size conventional algorithms and single core processors struggle with the efficient processing of such networks. Employing multi-core graphic processing units (GPUs) could provide sufficient processing power for the analysis of such networks. However, commonly designed algorithms cannot exploit these massively parallel processing power for the analysis of such networks. In this paper, we present the Multi Layer Network Decomposition (MLND) approach which provides a general approach for parallel network analysis using multi-core processors via efficient partitioning and mapping of networks onto GPU architectures. Evaluation using a 336 core GPU graphic card demonstrated a 16x speed-up in complex network analysis relative to a CPU based approach.
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