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MultiGraph: Efficient Graph Processing on GPUs
- Authors
- Hong, C.; Sukumaran-Rajam, A.; Kim, J.; Sadayappan, P.
- Issue Date
- Sep-2017
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- GPU; Graph processing; High performance and productivity; Vertex-centric framework
- Citation
- Parallel Architectures and Compilation Techniques - Conference Proceedings, PACT, pp 27 - 40
- Pages
- 14
- Journal Title
- Parallel Architectures and Compilation Techniques - Conference Proceedings, PACT
- Start Page
- 27
- End Page
- 40
- ISSN
- 1089-795X
- Abstract
- High-level GPU graph processing frameworks are an attractive alternative for achieving both high productivity and high performance. Hence, several high-level frameworks for graph processing on GPUs have been developed. In this paper, we develop an approach to graph processing on GPUs that seeks to overcome some of the performance limitations of existing frameworks. It uses multiple data representation and execution strategies for dense versus sparse vertex frontiers, dependent on the fraction of active graph vertices. A two-phase edge processing approach trades off extra data movement for improved load balancing across GPU threads, by using a 2D blocked representation for edge data. Experimental results demonstrate performance improvement over current state-of-the-art GPU graph processing frameworks for many benchmark programs and data sets. © 2017 IEEE.
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