Analyzing performance within asynchronous many-task-based runtime systems is challenging because millions of tasks are launched concurrently. Especially for long-term runs the amount of data collected becomes overwhelming. We study HPX and its performance-counter framework and APEX to collect performance data and energy consumption. We added HPX application-specific performance counters to the Octo-Tiger full 3D AMR astrophysics application. This enables the combined visualization of physical and performance data to highlight bottlenecks with respect to different solvers. We examine the overhead introduced by these measurements, which is around 1%, with respect to the overall application runtime. We perform a convergence study for four different levels of refinement and analyze the application's performance with respect to adaptive grid refinement. The measurements' overheads are small, enabling the combined use of performance data and physical properties with the goal of improving the code's performance. All of these measurements were obtained on NERSC's Cori, Louisiana Optical Network Infrastructure's QueenBee2, and Indiana University's Big Red 3.

中文翻译：

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基于异步任务的运行时系统中的性能测量：双白矮星合并作为应用程序

在异步的基于多任务的运行时系统中分析性能具有挑战性，因为同时启动了数百万个任务。特别是对于长期运行，收集的数据量变得不堪重负。我们研究HPX及其性能计数器框架和APEX，以收集性能数据和能耗。我们在Octo-Tiger完整3D AMR天体物理学应用程序中添加了HPX应用程序特定的性能计数器。这使得物理和性能数据的组合可视化能够突出显示针对不同求解器的瓶颈。我们检查了这些测量带来的开销，相对于整个应用程序运行时，开销约为1％。我们针对四个不同级别的细化进行收敛研究，并分析应用程序的 自适应网格细化方面的性能。测量的开销很小，可以结合使用性能数据和物理属性，以提高代码的性能。所有这些测量都是在NERSC的Cori，路易斯安那州的光网络基础设施的QueenBee2和印第安纳大学的Big Red 3上获得的。