GPU acceleration of a non-standard finite element mesh truncation technique for electromagnetics
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comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Title
GPU acceleration of a non-standard finite element mesh truncation technique for electromagneticsDate
2020-05-07Publisher
IEEEBibliographic citation
J. M. Badía, A. Amor-Martin, J. A. Belloch and L. E. García-Castillo, "GPU Acceleration of a Non-Standard Finite Element Mesh Truncation Technique for Electromagnetics," in IEEE Access, vol. 8, pp. 94719-94730, 2020, doi: 10.1109/ACCESS.2020.2993103.Type
info:eu-repo/semantics/articlePublisher version
https://ieeexplore.ieee.org/abstract/document/9088972Version
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Abstract
The emergence of General Purpose Graphics Processing Units (GPGPUs) provides new opportunities to accelerate applications involving a large number of regular computations. However, properly leveraging the computational ... [+]
The emergence of General Purpose Graphics Processing Units (GPGPUs) provides new opportunities to accelerate applications involving a large number of regular computations. However, properly leveraging the computational resources of graphical processors is a very challenging task. In this paper, we use this kind of device to parallelize FE-IIEE (Finite Element-Iterative Integral Equation Evaluation), a non-standard finite element mesh truncation technique introduced by two of the authors. This application is computationally very demanding due to the amount, size and complexity of the data involved in the procedure. Besides, an efficient implementation becomes even more difficult if the parallelization has to maintain the complex workflow of the original code. The proposed implementation using CUDA applies different optimization techniques to improve performance. These include leveraging the fastest memories of the GPU and increasing the granularity of the computations to reduce the impact of memory access. We have applied our parallel algorithm to two real radiation and scattering problems demonstrating speedups higher than 140 on a state-of-the-art GPU. [-]
Investigation project
Spanish Government through (TEC2016-80386-P, TIN2017-82972-R, ESP2015-68245-C4-1-P) ; Valencian Regional Government (PROMETEO/2019/109)Rights
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info:eu-repo/semantics/openAccess
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