Zhu, Huanzhou, He, Ligang, Leeke, Matthew and Mao, Rui (2020) WolfGraph : the edge-centric graph processing on GPU. Future Generation Computer Systems, 111 . pp. 552-569. doi:10.1016/j.future.2019.09.052

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Abstract

There is the significant interest nowadays in developing the frameworks for parallelizing the processing of large graphs such as social networks, web graphs, etc. The work has been proposed to parallelize the graph processing on clusters (distributed memory), multicore machines (shared memory) and GPU devices. Most existing research on GPU-based graph processing employs the vertex-centric processing model and the Compressed Sparse Row (CSR) form to store and process a graph. However, they suffer from irregular memory access and load imbalance in GPU, which hampers the full exploitation of GPU performance. In this paper, we present WolfGraph, a GPU-based graph processing framework that addresses the above problems. WolfGraph adopts the edge-centric processing, which iterates over the edges rather than vertices. The data structure and graph partition in WolfGraph are carefully crafted so as to minimize the graph pre-processing and allow the coalesced memory access. WolfGraph fully utilizes the GPU power by processing all edges in parallel. We also develop a new method, called Concatenated Edge List (CEL), to process a graph that is bigger than the global memory of GPU. WolfGraph allows the users to define their own graph-processing methods and plug them into the WolfGraph framework. Our experiments show that WolfGraph achieves 7-8x speedup over GraphChi and X-Stream when processing large graphs, and it also offers 65% performance improvement over the existing GPU-based, vertex-centric graph processing frameworks, such as Gunrock.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science > Computer Science
SWORD Depositor:	Library Publications Router
Library of Congress Subject Headings (LCSH):	Graph theory, Graphics processing units, CUDA (Computer architecture), Parallel processing (Electronic computers)
Journal or Publication Title:	Future Generation Computer Systems
Publisher:	Elsevier
ISSN:	0167-739X
Official Date:	October 2020
Dates:	Date Event October 2020 Published 3 October 2019 Available 27 September 2019 Accepted
Date of first compliant deposit:	31 January 2020
Date of first compliant Open Access:	3 October 2020
Volume:	111
Page Range:	pp. 552-569
DOI:	10.1016/j.future.2019.09.052
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID 2018YFB1003201 National Key Research and Development Program of China Stem Cell and Translational Research http://dx.doi.org/10.13039/501100013290 UNSPECIFIED Shandong Worldwide Byte Security Information Technology, Co., Ltd UNSPECIFIED 2017B030314073 Guangdong Key Laboratory UNSPECIFIED

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