Breadth-First Search is an important kernel used by many graph-processing applications. In many of these emerging applications of BFS, such as analyzing social networks, the input graphs are low-diameter and scale-free. We propose a hybrid approach that is advantageous for low-diameter graphs, which combines a conventional top-down algorithm along with a novel bottom-up algorithm. The bottom-up algorithm can dramatically reduce the number of edges examined, which in turn accelerates the search as a whole. On a multi-socket server, our hybrid approach demonstrates speedups of 3.3–7.8 on a range of standard synthetic graphs and speedups of 2.4–4.6 on graphs from real social networks when compared to a strong baseline. We also typically double the performance of prior leading shared memory (multicore and GPU) implementations.

中文翻译：

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方向优化广度优先搜索

广度优先搜索是许多图形处理应用程序使用的重要内核。在BFS的许多新兴应用程序中，例如分析社交网络，输入图是小直径且无标度的。我们提出了一种对小直径图形有利的混合方法，该方法将常规的自上而下算法与新颖的自下而上算法结合在一起。自下而上的算法可以极大地减少检查的边的数量，从而从整体上加快搜索速度。在多路服务器上，我们的混合方法展示了一系列标准合成图的加速比为3.3–7.8，而来自真实社交网络的图的加速比为2.4–4.6。通常，我们还可以将先前领先的共享内存（多核和GPU）实现的性能提高一倍。