Explicit integration with GPU acceleration for large kinetic networks

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...

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Bibliographic Details
Published in:Journal of computational physics 2015-12, Vol.302
Main Authors: Brock, Benjamin, Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, Belt, Andrew, Billings, Jay Jay, The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996-1200, Guidry, Mike, Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830
Format: Article
Language:English
Subjects:
ACCELERATION
ACCELERATORS
ALGORITHMS
CALCULATION METHODS
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
COMBUSTION
DIFFERENTIAL EQUATIONS
HYDRODYNAMICS
NUCLEOSYNTHESIS
SIMULATION
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Summary: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 computation time for solving systems of realistic kinetic networks implies that important coupled, multiphysics problems in various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.
ISSN:0021-9991
1090-2716