Solving Electromagnetic Scattering Problems with Tens of Billions of Unknowns Using GPU Accelerated Massively Parallel MLFMA Article Swipe
Wei-Jia He
,
Zeng Yang
,
Xiao‐Wei Huang
,
Wu Wang
,
Ming‐Lin Yang
,
Xin‐Qing Sheng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.17292470.v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.17292470.v1
In this paper, a massively parallel approach of the multilevel fast multipole algorithm (PMLFMA) on graphics processing unit (GPU) heterogeneous platform, noted as GPU-PMLFMA, is presented for solving extremely large electromagnetic scattering problems involving tens of billions of unknowns, In this approach, the flexible and efficient ternary partitioning scheme is employed at first to partition the MLFMA octree among message passing interface (MPI) processes. Then the computationally intensive parts of the PMLFMA on each MPI process, matrix filling, aggregation and disaggregation, etc., are accelerated by using the GPU. Different parallelization strategies in coincidence with the ternary parallel MLFMA approach are designed for GPU to ensures a high computational throughput. Special memory usage strategy is designed to improve the computational efficiency and benefit data re-using. The CPU/GPU asynchronous computing pattern is designed with the OpenMP and CUDA respectively for accelerating the CPU and GPU execution parts and computation time overlapped. GPU architecture-based optimization strategies are implemented to further improve the computational efficiency. Numerical results demonstrate that the proposed GPU-PMLFMA can achieve over 3 times speed-up, compared with the 8-threaded conventional PMLFMA. Solutions of scattering by electrically large and complicated objects with about 24000 wavelengths and over 41.8 billion unknowns, are presented.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.36227/techrxiv.17292470.v1
- https://www.techrxiv.org/articles/preprint/Solving_Electromagnetic_Scattering_Problems_with_Tens_of_Billions_of_Unknowns_Using_GPU_Accelerated_Massively_Parallel_MLFMA/17292470/1/files/34211576.pdf
- OA Status
- gold
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281720080
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4281720080Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.36227/techrxiv.17292470.v1Digital Object Identifier
- Title
-
Solving Electromagnetic Scattering Problems with Tens of Billions of Unknowns Using GPU Accelerated Massively Parallel MLFMAWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-26Full publication date if available
- Authors
-
Wei-Jia He, Zeng Yang, Xiao‐Wei Huang, Wu Wang, Ming‐Lin Yang, Xin‐Qing ShengList of authors in order
- Landing page
-
https://doi.org/10.36227/techrxiv.17292470.v1Publisher landing page
- PDF URL
-
https://www.techrxiv.org/articles/preprint/Solving_Electromagnetic_Scattering_Problems_with_Tens_of_Billions_of_Unknowns_Using_GPU_Accelerated_Massively_Parallel_MLFMA/17292470/1/files/34211576.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.techrxiv.org/articles/preprint/Solving_Electromagnetic_Scattering_Problems_with_Tens_of_Billions_of_Unknowns_Using_GPU_Accelerated_Massively_Parallel_MLFMA/17292470/1/files/34211576.pdfDirect OA link when available
- Concepts
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Computer science, Parallel computing, CUDA, Massively parallel, Computational science, Graphics processing unit, Octree, Speedup, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2128556617, https://openalex.org/W2163068304, https://openalex.org/W2154089789, https://openalex.org/W2019789345, https://openalex.org/W2171479497, https://openalex.org/W2099789761, https://openalex.org/W1542002907, https://openalex.org/W2132755674, https://openalex.org/W2125322382, https://openalex.org/W6662748727, https://openalex.org/W1903718817, https://openalex.org/W2091958452, https://openalex.org/W2109027746, https://openalex.org/W2016067822, https://openalex.org/W2158173533, https://openalex.org/W2896321576, https://openalex.org/W1591776253, https://openalex.org/W2133397108, https://openalex.org/W2136222595, https://openalex.org/W2054357898, https://openalex.org/W2962358977, https://openalex.org/W2795117369, https://openalex.org/W4226054303, https://openalex.org/W6678318891, https://openalex.org/W2167243874, https://openalex.org/W3201079539, https://openalex.org/W2564229740, https://openalex.org/W2122834723, https://openalex.org/W2963117067, https://openalex.org/W2048145697 |
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