调用图集合的可扩展比较可视化
Scalable Comparative Visualization of Ensembles of Call Graphs
摘要：
优化大规模并行代码的性能对于有效利用计算资源至关重要。代码开发人员经常探索各种执行参数，例如硬件配置、系统软件选择和应用程序参数，并且对检测和理解不同执行中的瓶颈感兴趣。它们通常收集表示为调用图的分层性能配置文件，调用图将性能指标与其执行上下文结合起来。一起探索多个调用图的关键任务是乏味和具有挑战性的，因为执行上下文中的许多结构差异和收集的性能度量中的显著可变性（例如，执行运行时）。在本文中，我们提出了一个增强版本的CallFlow，以支持使用新类型的可视化，分析，图形操作和功能的调用图的集合的探索。我们介绍了集成桑基，一个新的视觉设计，结合了资源流（桑基）和箱形图可视化技术的优势。尽管资源流可视化可以轻松直观地描述调用图的图形性质，但覆盖在Sankey节点上的箱形图传达了集成中的性能变化。我们的交互式可视界面提供了链接视图，以帮助探索调用图的集合，例如，通过促进结构差异的分析以及识别相似或不同的调用图。我们证明了我们的设计的有效性和实用性，通过大规模并行代码的案例研究。
英文摘要：
Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present an enhanced version of CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey, a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. We demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.
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