Technical Report: Accelerating Dynamic Graph Analytics on GPUs

Autor:	Sha, Mo, Li, Yuchen, He, Bingsheng, Tan, Kian-Lee
Rok vydání:	2017
Předmět:	Computer Science - Data Structures and Algorithms Computer Science - Distributed Parallel and Cluster Computing H.2.4 E.1
Druh dokumentu:	Working Paper
Popis:	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. Comment: 34 pages, 18 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1709.05061 Zobrazit plný text záznamu View this record from Arxiv