Performance Measurements within Asynchronous Task-based Runtime Systems: A Double White Dwarf Merger as an Application
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Patrick Diehl and Dominic Marcello and Parsa Armini and Hartmut Kaiser and Sagiv Shiber and Geoffrey C. Clayton and Juhan Frank and Gregor Daiß and Dirk Pflüger and David Eder and Alice Koniges and Kevin Huck
2021
Abstract
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.
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