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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机译:我们展示了我们在异构GPU + CPU HPC平台的背景下探索了加速BFS在大型复杂网络上的配置空间的经验。我们通过系统地研究不同的图形分区策略来研究异构计算方法的可行性,同时加工合成和现实世界复杂网络。为实现这一目标,我们利用复杂网络进行负载分区的验诚。
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