HPGraph: High-Performance Graph Analytics with Productivity on the GPU Article Swipe
YOU?
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· 2018
· Open Access
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· DOI: https://doi.org/10.1155/2018/9340697
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.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1155/2018/9340697
- http://downloads.hindawi.com/journals/sp/2018/9340697.pdf
- OA Status
- hybrid
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2905320744
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2905320744Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2018/9340697Digital Object Identifier
- Title
-
HPGraph: High-Performance Graph Analytics with Productivity on the GPUWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-12-11Full publication date if available
- Authors
-
Haoduo Yang, Huayou Su, Qiang Lan, Mei Wen, Chunyuan ZhangList of authors in order
- Landing page
-
https://doi.org/10.1155/2018/9340697Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/sp/2018/9340697.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/sp/2018/9340697.pdfDirect OA link when available
- Concepts
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Computer science, PageRank, Graph, Analytics, Graph algorithms, Parallel computing, General-purpose computing on graphics processing units, Supercomputer, Implementation, Theoretical computer science, Power graph analysis, Graphics, Programming language, Operating system, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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