Harnessing billions of tasks for a scalable portable hydrodynamic simulation of the merger of two stars. (July 2019)

• Record Type: Journal Article
• Authors: Heller, Thomas; Lelbach, Bryce Adelstein; Huck, Kevin A; Biddiscombe, John; Grubel, Patricia; Koniges, Alice E; Kretz, Matthias; Marcello, Dominic; Pfander, David; Serio, Adrian; Frank, Juhan; Clayton, Geoffrey C; Pflüger, Dirk; Eder, David; Kaiser, Hartmut
• Is Part Of: International journal of high performance computing applications. Volume 33:Number 4(2019)
• Page Start: 699
• Page End: 715
• Publication Date: 2019-07
• Subjects: Parallel runtime -- binary star merger -- asynchronous tasks -- HPX -- C++; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342018819744
• Languages: English
• ISSNs: 1094-3420

Abstract

We present a highly scalable demonstration of a portable asynchronous many-task programming model and runtime system applied to a grid-based adaptive mesh refinement hydrodynamic simulation of a double white dwarf merger with 14 levels of refinement that spans 17 orders of magnitude in astrophysical densities. The code uses the portable C++ parallel programming model that is embodied in the HPX library and being incorporated into the ISO C++ standard. The model represents a significant shift from existing bulk synchronous parallel programming models under consideration for exascale systems. Through the use of the Futurization technique, seemingly sequential code is transformed into wait-free asynchronous tasks. We demonstrate the potential of our model by showing results from strong scaling runs on National Energy Research Scientific Computing Center's Cori system (658, 784 Intel Knight's Landing cores) that achieve a parallel efficiency of 96.8% using billions of asynchronous tasks.