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- W3130509711 abstract "Uncertain or probabilistic graphs have been ubiquitously used in many emerging applications. Previously CPU based techniques were proposed to use sampling but suffer from (1) low computation efficiency and large memory overhead, (2) low degree of parallelism, and (3) nonexistent general framework to effectively support programming uncertain graph applications. To tackle these challenges, we propose a general uncertain graph processing framework for multi-GPU systems, named BPGraph. Integrated with our highly-efficient path sampling method, BPGraph can support a wide range of uncertain graph algorithms' development and optimization. Extensive evaluation demonstrates a significant performance improvement from BPGraph over the state-of-the-art uncertain graph sampling techniques." @default.
- W3130509711 created "2021-03-01" @default.
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- W3130509711 date "2021-02-17" @default.
- W3130509711 modified "2023-10-02" @default.
- W3130509711 title "An efficient uncertain graph processing framework for heterogeneous architectures" @default.
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- W3130509711 doi "https://doi.org/10.1145/3437801.3441584" @default.
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