GPU-accelerated preconditioned GMRES method for two-dimensional Maxwell's equations

DOI10.1080/00207160.2017.1280156zbMath1404.65088OpenAlexW2572982442MaRDI QIDQ3174889

Guixia He, Kesong Wu, Jiaquan Gao, Panpan Qi, Yu Shun Wang

Publication date: 18 July 2018

Published in: International Journal of Computer Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207160.2017.1280156

zbMATH Keywords

Maxwell's equationsGMRES methodmultisymplecticCUDAGPU

Mathematics Subject Classification ID

Finite difference methods for initial value and initial-boundary value problems involving PDEs (65M06) Finite difference methods applied to problems in optics and electromagnetic theory (78M20) Iterative numerical methods for linear systems (65F10) Numerical algorithms for specific classes of architectures (65Y10) Preconditioners for iterative methods (65F08) Maxwell equations (35Q61)

Related Items (2)

Mixed Precision Iterative Refinement with Sparse Approximate Inverse Preconditioning ⋮ Development of a parallel algorithm based on an implicit scheme for the discontinuous Galerkin method for solving diffusion type equations

Uses Software

• ITPACK
• CUDA
• SparseMatrix
• CUBLAS
• CUSPARSE
• clSpMV
• AmgX
• yaSpMV

Cites Work

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• A brief review of the ITPACK project
• A novel CSR-based sparse matrix-vector multiplication on GPUs
• A Unified Sparse Matrix Data Format for Efficient General Sparse Matrix-Vector Multiplication on Modern Processors with Wide SIMD Units
• The university of Florida sparse matrix collection
• AmgX: A Library for GPU Accelerated Algebraic Multigrid and Preconditioned Iterative Methods
• Multisymplectic Preissman scheme for the time-domain Maxwell’s equations
• A Priori Sparsity Patterns for Parallel Sparse Approximate Inverse Preconditioners

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