2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (2015)
May 4, 2015 to May 7, 2015
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 Graph 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.
Graphics processing units, Algorithm design and analysis, Scalability, Electric breakdown, Performance evaluation, Optimization
Y. Guo, A. L. Varbanescu, A. Iosup and D. Epema, "An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems," 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)(CCGRID), Shenzhen, China, 2015, pp. 423-432.