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

Conference paper (2015)

Authors

Y. Guo Data-Intensive Systems - EEMCS
A.L. Varbanescu Universiteit van Amsterdam
A. Iosup Data-Intensive Systems - EEMCS
D.H.J. Epema Data-Intensive Systems - EEMCS
Research Group
Data-Intensive Systems (EEMCS) (TU Delft)
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Published Date

2015

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Data-Intensive Systems

Abstract

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.