Explicit Integration with GPU Acceleration for Large Kinetic Networks
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996-1200, USA
arXiv:1409.5826 [physics.comp-ph], (19 Sep 2014)
@article{2014arXiv1409.5826B,
author={Brock}, B. and {Belt}, A. and {Billings}, J.~J. and {Guidry}, M.},
title={"{Explicit Integration with GPU Acceleration for Large Kinetic Networks}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1409.5826},
primaryClass={"physics.comp-ph"},
keywords={Physics – Computational Physics, Astrophysics – Solar and Stellar Astrophysics, Computer Science – Other Computer Science},
year={2014},
month={sep},
adsurl={http://adsabs.harvard.edu/abs/2014arXiv1409.5826B},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
We demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. This orders-of-magnitude decrease in compute time for solving systems of realistic kinetic networks implies that important coupled, multiphysics problems in various scientific and technical fields that were intractible, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.
September 23, 2014 by hgpu
