GPU accelerated finite-element computation for electromagnetic analysis

Meng, Huan-Ting; Nie, Bao-Lin; Wong, Steven; Macon, Charles; Jin, Jian‐Ming

doi:10.1109/map.2014.6837065

Cited by 19 publications

(2 citation statements)

References 33 publications

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“…In this paper, we consider GPU-acceleration of the finite element Method-one of the most powerful and versatile numerical techniques, which is often applied to the solution of boundary value problems that arise in electromagnetics. To date, most publications on FEM in the context of electromagnets have been related to the solution of a linear system of equations [11][12][13] and FEM matrix generation and assembly [13][14][15]. In this paper, we concentrate on finding the solution of the generalized eigenvalue problems that emerge when FEM is applied to simulate the free oscillation of microwave cavities.…”

Section: Introductionmentioning

confidence: 99%

Single and Dual-GPU Generalized Sparse Eigenvalue Solvers for Finding a Few Low-Order Resonances of a Microwave Cavity Using the Finite-Element Method

Dziekonski¹,

Mrozowski²

2018

RADIOENGINEERING

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This paper presents two fast generalized eigenvalue solvers for sparse symmetric matrices that arise when electromagnetic cavity resonances are investigated using the higher-order finite element method (FEM). To find a few loworder resonances, the locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm with null-space deflation is applied. The computations are expedited by using one or two graphical processing units (GPUs) as accelerators. The performance of the solver is tested for single and dual GPU hardware setups, making use of two types of GPU: NVIDIA Kepler K40s and NVIDIA Pascal P100s. The speed of the GPU-accelerated solvers is compared to a multithreaded implementation of the same algorithm using a multicore central processing unit (CPU, Intel Xeon E5-2680 v3 with twelve cores). It was found that, even for the least efficient setups, the GPU-accelerated code is approximately twice as fast as a parallel CPU-only implementation.

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Section: Introductionmentioning

confidence: 99%

Single and Dual-GPU Generalized Sparse Eigenvalue Solvers for Finding a Few Low-Order Resonances of a Microwave Cavity Using the Finite-Element Method

Dziekonski¹,

Mrozowski²

2018

RADIOENGINEERING

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“…One way to reduce the time needed to deform the mesh is to make use of modern graphics processing units (GPUs) that allow concurrent execution of many computing tasks. Parallelization of the most time-consuming stages by using multicore GPU architectures has been considered for many computational techniques [4][5][6], including the finite-element method in [7][8][9][10][11]. However, it should be noted that, while modern GPUs offer higher performance in terms of theoretical peak FLOPs and bandwidth than current CPUs, each algorithm must be reformulated to make effective use of the capabilities of the GPUs.…”

Section: Introductionmentioning

confidence: 99%

GPU-Accelerated 3D Mesh Deformation for Optimization Based on the Finite Element Method

Lamęcki¹,

Dziekonski²,

Balewski³

et al. 2017

RADIOENGINEERING

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Abstract. This paper discusses a strategy for speeding up the mesh deformation process in the design-byoptimization of high-frequency components involving electromagnetic field simulations using the 3D finite element method (FEM). The mesh deformation is assumed to be described by a linear elasticity model of a rigid body; therefore, each time the shape of the device is changed, an auxiliary elasticity finite-element problem must be solved. In order to accomplish this in a very short time numerical integration and the solution of the resulting system of equations are performed using a graphics processing unit (GPU).The performance of the proposed algorithm is illustrated are verified using a complex example involving 3D FEM analysis of a dielectric-resonator filter.

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Using Modern Multi-/Many-core Architecture for the Engineering Simulations

Michalski

Sczygiol

2014

Transactions on Engineering Technologies

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GPU accelerated finite-element computation for electromagnetic analysis

Cited by 19 publications

References 33 publications

Single and Dual-GPU Generalized Sparse Eigenvalue Solvers for Finding a Few Low-Order Resonances of a Microwave Cavity Using the Finite-Element Method

Single and Dual-GPU Generalized Sparse Eigenvalue Solvers for Finding a Few Low-Order Resonances of a Microwave Cavity Using the Finite-Element Method

GPU-Accelerated 3D Mesh Deformation for Optimization Based on the Finite Element Method

Using Modern Multi-/Many-core Architecture for the Engineering Simulations

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