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.

翻译：在非同步、多任务、多任务运行时间系统中分析性能是困难的,因为同时启动数以百万计的任务。特别是在长期运行的情况下,所收集的数据数量将变得惊人。我们研究了HPX及其性能反射框架和APEX,以收集性能数据和能源消耗情况。我们增加了HPX具体应用性能反向于Octo-Triger全3D AD ATM天体物理学应用。这样,物理和性能数据的综合可视化能够突出不同解答器的瓶颈问题。我们研究了这些测量结果带来的间接费用,在总体应用运行时间方面约为1%。我们对四个不同层次的改进进行了趋同研究,并分析了应用在适应性电网改进方面的绩效。测量的间接费用很小,能够将性能数据和物理特性结合起来使用,从而改进代码的性能。所有这些测量结果都是在NERSC的Cori、路易斯光学网络基础设施的Que Bee2和印第安大学的大红3上取得的。