https://doi.org/10.1051/epjconf/201610906003
Efficient GPU Accelerationfor Integrating Large Thermonuclear Networks in Astrophysics
Department of Physics and Astronomy, University of Tennessee, Knoxville TN 37996, USA
a e-mail: guidry@utk.edu
Published online: 12 February 2016
We demonstrate the systematic implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. We take as representative test cases Type Ia supernova explosions with extremely stiff thermonuclear reaction networks having 150–365 isotopic species and 1600–4400 reactions, assumed coupled to hydrodynamics using operator splitting. In such examples we demonstrate the capability to integrate independent thermonuclear networks from ~250–500 hydro zones (assumed to be deployed on CPU cores) in parallel on a single GPU in the same wall clock time that standard implicit methods can integrate the network for a single zone. This two or more orders of magnitude increase in efficiency for solving systems of realistic thermonuclear networks coupled to fluid dynamics implies that important coupled, multiphysics problems in various scientific and technical disciplines that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible. As examples of such applications I will discuss our ongoing deployment of these new methods for Type Ia supernova explosions in astrophysics and for simulation of the complex atmospheric chemistry entering into weather and climate problems.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
