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

Abstract

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.

Authors:
;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1440679
Report Number(s):
PNNL-SA-125934
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: High Performance Computing: Proceedings of the 32nd International Conference, (ISC 2017), June 18–22, 2017, Frankfurt, Germany. Lecture Notes in Computer Science, 10266:97-119
Country of Publication:
United States
Language:
English
Subject:
dynamic graphs

Citation Formats

Sengupta, Dipanjan, and Song, Shuaiwen. EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU. United States: N. p., 2017. Web. doi:10.1007/978-3-319-58667-0_6.
Sengupta, Dipanjan, & Song, Shuaiwen. EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU. United States. doi:10.1007/978-3-319-58667-0_6.
Sengupta, Dipanjan, and Song, Shuaiwen. Fri . "EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU". United States. doi:10.1007/978-3-319-58667-0_6.
@article{osti_1440679,
title = {EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU},
author = {Sengupta, Dipanjan and Song, Shuaiwen},
abstractNote = {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.},
doi = {10.1007/978-3-319-58667-0_6},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {5}
}

Conference:
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Works referenced in this record:

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