探索复杂网络异构计算用于大数据分析的可行性

2015 12th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT) Pub Date : 2015-12-03 DOI:10.1109/CEWIT.2015.7338160

A. Garcia-Robledo, A. Díaz-Pérez, G. Morales-Luna

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引用次数: 3

摘要

我们介绍了在异构GPU + CPU HPC平台的背景下，探索在大型复杂网络上加速BFS的配置空间的经验。我们通过系统地研究不同的图划分策略来研究异构计算方法在处理合成网络和现实世界复杂网络时的可行性。为了实现这一点，我们利用复杂网络的核心来进行负载划分。

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Exploring the feasibility of heterogeneous computing of complex networks for big data analysis

We present our experience with exploring the configuration space for accelerating BFS's on large complex networks in the context of a heterogeneous GPU + CPU HPC platform. We study the feasibility of the heterogeneous computing approach by systematically studying different graph partitioning strategies while processing synthetic and real-world complex networks. To achieve this, we exploit the coreness of complex networks for load partitioning.

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2015 12th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT)

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