HPGraph: High-Performance Graph Analytics with Productivity on the GPU
Joint Authors
Zhang, Chunyuan
Su, Huayou
Wen, Mei
Lan, Qiang
Yang, Haoduo
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-11
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The growing use of graph in many fields has sparked a broad interest in developing high-level graph analytics programs.
Existing GPU implementations have limited performance with compromising on productivity.
HPGraph, our high-performance bulk-synchronous graph analytics framework based on the GPU, provides an abstraction focused on mapping vertex programs to generalized sparse matrix operations on GPU as the backend.
HPGraph strikes a balance between performance and productivity by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model for users to implement various graph algorithms with relatively little effort.
We evaluate the performance of HPGraph for four graph primitives (BFS, SSSP, PageRank, and TC).
Our experiments show that HPGraph matches or even exceeds the performance of high-performance GPU graph libraries such as MapGraph, nvGraph, and Gunrock.
HPGraph also runs significantly faster than advanced CPU graph libraries.
American Psychological Association (APA)
Yang, Haoduo& Su, Huayou& Lan, Qiang& Wen, Mei& Zhang, Chunyuan. 2018. HPGraph: High-Performance Graph Analytics with Productivity on the GPU. Scientific Programming،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1214780
Modern Language Association (MLA)
Yang, Haoduo…[et al.]. HPGraph: High-Performance Graph Analytics with Productivity on the GPU. Scientific Programming No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1214780
American Medical Association (AMA)
Yang, Haoduo& Su, Huayou& Lan, Qiang& Wen, Mei& Zhang, Chunyuan. HPGraph: High-Performance Graph Analytics with Productivity on the GPU. Scientific Programming. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1214780
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214780