Conference Proceeding Article
As graph analytics often involves compute-intensive operations,GPUs have been extensively used to accelerate theprocessing. However, in many applications such as socialnetworks, cyber security, and fraud detection, their representativegraphs evolve frequently and one has to perform arebuild of the graph structure on GPUs to incorporate theupdates. Hence, rebuilding the graphs becomes the bottleneckof processing high-speed graph streams. In this paper,we propose a GPU-based dynamic graph storage schemeto support existing graph algorithms easily. Furthermore,we propose parallel update algorithms to support ecientstream updates so that the maintained graph is immediatelyavailable for high-speed analytic processing on GPUs. Ourextensive experiments with three streaming applications onlarge-scale real and synthetic datasets demonstrate the superiorperformance of our proposed approach.
Computer Engineering | Data Storage Systems
Proceedings of the 44th International Conference on Very Large Data Bases, Rio de Janeiro, Brazil, 2017 October
City or Country
SHAN, Mo; LI, Yuchen; HE, Bingsheng; and TAN, Kian-Lee.
Accelerating dynamic graph analytics on GPUs. (2017). Proceedings of the 44th International Conference on Very Large Data Bases, Rio de Janeiro, Brazil, 2017 October. 11,. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3905
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