We propose that clusters interconnected with network topologies having minimal mean path length will increase their processing speeds. We approach our heuristic by constructing clusters of up to 32 nodes having torus, ring, Chvatal, Wagner, Bidiakis and optimal topology for minimal mean path length and by simulating the performance of 256 nodes clusters with the same network topologies. The optimal (or near-optimal) low-latency network topologies are found by minimizing the mean path length of regular graphs. The selected topologies are benchmarked using ping-pong messaging, the MPI collective communications and the standard parallel applications including effective bandwidth, FFTE, Graph 500 and NAS parallel benchmarks. We established strong correlations between the clusters’ performances and the network topologies, especially the mean path lengths, for a wide range of applications. In communication-intensive benchmarks, optimal graphs enabled network topologies with multifold performance enhancement in comparison with mainstream graphs. It is striking that mere adjustment of the network topology suffices to reclaim performance from the same computing hardware.

中文翻译：

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用于集群性能增强的最佳低延迟网络拓扑

我们建议与具有最小平均路径长度的网络拓扑互连的集群将提高它们的处理速度。我们通过构建多达 32 个具有环面、环、Chvatal、Wagner、Bidiakis 和最优拓扑的节点来实现我们的启发式算法，以实现最小平均路径长度，并通过模拟具有相同网络拓扑的 256 个节点集群的性能。通过最小化规则图的平均路径长度来找到最优（或接近最优）的低延迟网络拓扑。所选拓扑使用乒乓消息传递、MPI 集体通信和标准并行应用程序进行基准测试，包括有效带宽、FFTE、Graph 500 和 NAS 并行基准测试。我们在集群的性能和网络拓扑之间建立了很强的相关性，尤其是平均路径长度，适用于广泛的应用。在通信密集型基准测试中，与主流图相比，最佳图使网络拓扑具有多倍的性能增强。令人惊讶的是，仅仅调整网络拓扑就足以从相同的计算硬件中恢复性能。