EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU

Sengupta, Dipanjan; Song, Shuaiwen

doi:10.1007/978-3-319-58667-0_6

EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU

Conference · Fri May 12 00:00:00 EDT 2017

DOI:https://doi.org/10.1007/978-3-319-58667-0_6· OSTI ID:1440679

Sengupta, Dipanjan; Song, Shuaiwen

With the prevalence of the World Wide Web and social networks, there has been a growing interest in high performance analytics for constantly-evolving dynamic graphs. Modern GPUs provide massive AQ1 amount of parallelism for efficient graph processing, but the challenges remain due to their lack of support for the near real-time streaming nature of dynamic graphs. Specifically, due to the current high volume and velocity of graph data combined with the complexity of user queries, traditional processing methods by first storing the updates and then repeatedly running static graph analytics on a sequence of versions or snapshots are deemed undesirable and computational infeasible on GPU. We present EvoGraph, a highly efficient and scalable GPU- based dynamic graph analytics framework.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-76RL01830

OSTI ID:: 1440679

Report Number(s):: PNNL-SA-125934

Country of Publication:: United States

Language:: English

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