Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2017

Abstract

As graph analytics often involves compute-intensive operations,GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform are build of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper,we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore,we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing on GPUs. Our extensive experiments with three streaming applications on large-scale real and synthetic datasets demonstrate the superior performance of our proposed approach.

Keywords

Algorithms, processing, breath-first search, Graph structures

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the VLDB Endowment: 44th International Conference on Very Large Data Bases, Rio de Janeiro, Brazil, 2017 August 27-31

Volume

11

First Page

107

Last Page

120

Identifier

10.14778/3136610.3136619

Publisher

VLDB Endowment

City or Country

Stanford, CA

Additional URL

https://www.vldb.org/pvldb/vol11/p107-sha.pdf

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