4.7 Article

Explicit integration with GPU acceleration for large kinetic networks

期刊

JOURNAL OF COMPUTATIONAL PHYSICS
卷 302, 期 -, 页码 591-602

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2015.09.013

关键词

Ordinary differential equations; Reaction networks; Stiffness; Reactive flows; Nucleosynthesis; Combustion

资金

  1. US Department of Energy, Office of Nuclear Physics
  2. ORNL Undergraduate Research Participation Program
  3. ORNL
  4. US Department of Energy [DE-AC05-00OR22725, DE-AC05-00OR22750]

向作者/读者索取更多资源

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. (C) 2015 Elsevier Inc. All rights reserved.

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