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Accelerating Clustering Coefficient Calculations on a GPU Using OPENCL

Leonid Djinevski, Igor Mishkovski, Dimitar Trajanov
Ss Cyril and Methodius University, Faculty of Electrical Engineering and Information, Technologies, ul. Rugjer Boshkovikj bb, PO Box 574, 1000 Skopje, Macedonia
ICT Innovations 2010, Communications in Computer and Information Science, 2011, Volume 83, Part 2, 276-285

@article{djinevski2011accelerating,

   title={Accelerating Clustering Coefficient Calculations on a GPU Using OPENCL},

   author={Djinevski, L. and Mishkovski, I. and Trajanov, D.},

   journal={ICT Innovations 2010},

   pages={276–285},

   year={2011},

   publisher={Springer}

}

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The growth in multicore CPUs and the emergence of powerful manycore GPUs has led to proliferation of parallel applications. Many applications are not straight forward to be parallelized. This paper examines the performance of a parallelized implementation for calculating measurements of Complex Networks. We present an algorithm for calculating complex networks topological feature clustering coefficient, and conducted an execution of the serial, parallel and parallel GPU implementations. A hash-table based structure was used for encoding the complex network’s data, which is different than the standard representation, and also speedups the parallel GPU implementations. Our results demonstrate that the parallelization of the sequential implementations on a multicore CPU, using OpenMP produces a significant speedup. Using OpenCL on a GPU produces even larger speedup depending of the volume of data being processed.
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