In this paper, we study several unsmoothed aggregation based algebraic multigrid (UA-AMG) methods with regard to different characteristics of CPUs and graphics processing units (GPUs). We propose some UA-AMG methods with lower computational complexity for CPU and CPU-GPU, and study these UA-AMG methods mixing with 4 kinds of red-black colored Gauss-Seidel smoothers for CPU-GPU since the initial mesh is structured. These UA-AMG methods are used as preconditioners for the conjugate gradient (CG) solver to solve a class of two-dimensional single-temperature radiation diffusion equations discretized by preserving symmetry finite volume element scheme. Numerical results demonstrate that, UA-NA-CG-s, which wins the best robustness and efficiency among them, is much more efficient than the default AMG preconditioned CG solvers in HYPRE, AGMG and Cusp for CPU; Under CPU-GPU, UA-W-CG-p is the most robust and efficient one, and rather more efficient than the smoothed aggregation based AMG preconditioned CG solver in Cusp.
- Nuclear Engineering Division
UA-AMG Methods for 2-D 1-T Radiation Diffusion Equations and Their CPU-GPU Implementations
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Yue, X, Shu, S, & Feng, C. "UA-AMG Methods for 2-D 1-T Radiation Diffusion Equations and Their CPU-GPU Implementations." Proceedings of the 2013 21st International Conference on Nuclear Engineering. Volume 5: Fuel Cycle, Radioactive Waste Management and Decommissioning; Reactor Physics and Transport Theory; Nuclear Education, Public Acceptance and Related Issues; Instrumentation and Controls; Fusion Engineering. Chengdu, China. July 29–August 2, 2013. V005T14A015. ASME. https://doi.org/10.1115/ICONE21-16157
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