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GStream: A Graph Streaming Processing Method for Large-Scale Graphs on GPUs

Title
GStream: A Graph Streaming Processing Method for Large-Scale Graphs on GPUs
Authors
Seo, HyunseokKim, JinwookKim, Min-Soo
DGIST Authors
Seo, Hyunseok; Kim, Jinwook; Kim, Min-Soo
Issue Date
2015-08
Citation
Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 50(8), 253-254
Type
Article
Article Type
Conference Paper
Keywords
AlgorithmsComputational PowerGPUGraph ProcessingGraphic MethodsLarge-ScaleParallel ProcessingParallel ProgrammingProcessingProgram ProcessorsState-of-the-Art MethodsStreamStreaming Processing
ISSN
0362-1340
Abstract
Fast processing graph algorithms for large-scale graphs becomes increasingly important. Besides, there have been many attempts to process graph applications by exploiting the massive amount of parallelism of GPUs. However, most of the existing methods fail to process large-scale graphs that do not fit in GPU device memory. We propose a fast and scalable parallel processing method GStream that fully exploits the computational power of GPUs for processing large-scale graphs (e.g., billions vertices) very efficiently. It exploits the concept of nested-loop theta-join and multiple asynchronous GPU streams. Extensive experimental results show that GStream consistently and significantly outperforms the state-of-the art method.
URI
http://hdl.handle.net/20.500.11750/3677
DOI
10.1145/2688500.2688526
Publisher
Association for Computing Machinery
Related Researcher
Files:
There are no files associated with this item.
Collection:
Information and Communication EngineeringETC1. Journal Articles


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