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An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems

Yong Guo, Ana Lucia Varbanescu, Alexandru Iosup, Dick Epema
TU Delft, The Netherlands
15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2015

@article{guo2015empirical,

   title={An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems},

   author={Guo, Yong and Varbanescu, Ana Lucia and Iosup, Alexandru and Epema, Dick},

   year={2015}

}

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Graph processing is increasingly used in knowledge economies and in science, in advanced marketing, social networking, bioinformatics, etc. A number of graph-processing systems, including the GPU-enabled Medusa and Totem, have been developed recently. Understanding their performance is key to system selection, tuning, and improvement. Previous performance evaluation studies have been conducted for CPU-based graph-processing systems, such as Giraph and GraphX. Unlike them, the performance of GPU-enabled systems is still not thoroughly evaluated and compared. To address this gap, we propose an empirical method for evaluating GPU-enabled graph-processing systems, which includes new performance metrics and a selection of new datasets and algorithms. By selecting 9 diverse graphs and 3 typical graph-processing algorithms, we conduct a comparative performance study of 3 GPU-enabled systems, Medusa, Totem, and MapGraph. We present the first comprehensive evaluation of GPU-enabled systems with results giving insight into raw processing power, performance breakdown into core components, scalability, and the impact on performance of system-specific optimization techniques and of the GPU generation. We present and discuss many findings that would benefit users and developers interested in GPU acceleration for graph processing.
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