Graph Processing on GPUs: A Survey
Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
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Graph Processing on GPUs : A Survey. / Shi, Xuanhua; Zheng, Zhigao; Zhou, Yongluan; Jin, Hai; He, Ligang; Liu, Bo; Hua, Qiang-Sheng.
I: A C M Computing Surveys, Bind 50, Nr. 6, 81, 01.2018.Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
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TY - JOUR
T1 - Graph Processing on GPUs
T2 - A Survey
AU - Shi, Xuanhua
AU - Zheng, Zhigao
AU - Zhou, Yongluan
AU - Jin, Hai
AU - He, Ligang
AU - Liu, Bo
AU - Hua, Qiang-Sheng
PY - 2018/1
Y1 - 2018/1
N2 - In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modeled as graph processing. Graph processing, especially the processing of the large-scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large-scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping, and specific GPU programming. In this article, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in detail, and explore the research opportunities for the future.
AB - In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modeled as graph processing. Graph processing, especially the processing of the large-scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large-scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping, and specific GPU programming. In this article, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in detail, and explore the research opportunities for the future.
KW - BSP model
KW - GAS model
KW - GPU
KW - Graph processing
KW - graph datasets
KW - parallelism
UR - http://www.scopus.com/inward/record.url?scp=85040237134&partnerID=8YFLogxK
U2 - 10.1145/3128571
DO - 10.1145/3128571
M3 - Review
VL - 50
JO - ACM Computing Surveys
JF - ACM Computing Surveys
SN - 0360-0300
IS - 6
M1 - 81
ER -
ID: 182749405