Visual Analytics Techniques for Exploring the Design Space of Large-Scale High-Radix Networks
Visual Analytics Techniques for Exploring the Design Space of Large-Scale High-Radix Networks
复制标题
用于探索大规模高基数网络设计空间的可视化分析技术
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
K. Ma
中科院分区：
文献类型：
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作者：
J. Li;Misbah Mubarak;R. Ross;C. Carothers;K. Ma
基于Dragonfly拓扑结构的高基数、低直径、分层网络是构建下一代HPC系统的常见选择。然而，缺乏有效的工具来分析网络的性能，并探索这种新兴的网络在规模上的设计选择。在本文中，我们提出了可视化分析方法，耦合数据聚合技术与交互式可视化分析大规模的蜻蜓网络。我们创建一个基于这些技术的交互式可视化分析系统。为了促进对网络行为的有效分析和探索，我们的系统提供了直观的、可扩展的可视化，可以自定义以显示各种流量特征并在不同的性能指标之间建立关联。使用高保真网络模拟和HPC应用程序的通信痕迹，我们证明了我们的系统的有用性与几个案例研究探索网络行为的规模与不同的工作负载，路由策略和就业安置政策。我们的模拟和可视化为缓解网络拥塞和作业间干扰提供了宝贵的见解。
High-radix, low-diameter, hierarchical networks based on the Dragonfly topology are common picks for building next generation HPC systems. However, effective tools are lacking for analyzing the network performance and exploring the design choices for such emerging networks at scale. In this paper, we present visual analytics methods that couple data aggregation techniques with interactive visualizations for analyzing large-scale Dragonfly networks. We create an interactive visual analytics system based on these techniques. To facilitate effective analysis and exploration of network behaviors, our system provides intuitive, scalable visualizations that can be customized to show various traffic characteristics and correlate between different performance metrics. Using high-fidelity network simulation and HPC applications communication traces, we demonstrate the usefulness of our system with several case studies on exploring network behaviors at scale with different workloads, routing strategies, and job placement policies. Our simulations and visualizations provide valuable insights for mitigating network congestion and inter-job interference.