Performance Measurements within Asynchronous Task-based Runtime Systems: A Double White Dwarf Merger as an Application

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 to collect performance data and energy consumption. We added HPX application-specific performance counters to the Octo-Tiger full 3D adaptive multigrid astrophysics code. Enabling the combined visualization of physical and performance data to highlight bottlenecks with respect to different solvers. We examine an performance counter overhead, which is around 1%, with respect to the overall application runtime. We perform a resolution 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 runs were obtained on NERSC's Cori, LONI's QueenBee2, and Indiana University's Big Red 3.