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CCGRID
2015
IEEE

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

4 years 2 months ago
An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems
—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 graphprocessing 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 w...
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where CCGRID
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