Technical Report: Accelerating Dynamic Graph Analytics on GPUs

TL;DR

This paper introduces a GPU-based dynamic graph storage and update scheme that significantly accelerates high-speed graph analytics by efficiently handling streaming updates, demonstrated through extensive experiments.

Contribution

It presents a novel GPU-oriented dynamic graph storage and parallel update algorithms that improve processing speed for streaming graph analytics.

Findings

Superior performance on large-scale datasets

Efficient handling of high-speed stream updates

Compatibility with existing graph algorithms

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 a rebuild 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.