论文标题
呼叫图的合奏的可扩展比较可视化
Scalable Comparative Visualization of Ensembles of Call Graphs
论文作者
论文摘要
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,并且有兴趣检测和理解不同执行中的瓶颈。他们经常收集表示为呼叫图的层次性能配置文件,该概况将性能指标与执行上下文相结合。一起探索多个呼叫图的关键任务是乏味和挑战性的,因为在执行环境中存在许多结构差异,并且收集的性能指标(例如执行运行时)的差异很大。在本文中,我们提出了一个增强的呼叫流,以使用新型的可视化,分析,图形操作和功能来支持呼叫图的探索。我们介绍了Ensemble-Sankey,这是一种新的视觉设计,结合了Resource-Flow(Sankey)和Box-Strot可视化技术的优势。尽管资源流的可视化可以轻松,直观地描述呼叫图的图形性质,但盒子图覆盖在Sankey节点上,传达了集合中的性能变异性。我们的交互式视觉界面提供了链接的视图,以帮助探索呼叫图的合奏,例如,通过促进结构差异的分析,并识别相似或不同的呼叫图。我们通过大规模平行代码的案例研究证明了设计的有效性和实用性。
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.