Investigation on accelerating FFT-based methods for the EFIE on graphics processors

March 19th, 2013


The use of graphic processor units (GPUs) has been recently proposed in computational electromagnetics to accelerate the solution of the electric field integral equation. In these methods, the linear systems obtained by using boundary elements are considered, and then an accelerated solution for a specific excitation is obtained. The existing studies are mostly focused on speeding up the filling time or the LU decomposition of that matrix. This limits the application to simple simulation scenarios if a fast method is not employed. In this paper, we propose a GPU acceleration for FFT-based integral equation solvers. We will investigate the operations involved in the solver, and we will motivate the use of GPUs. Results of numerical tests will be reported firstly on a perfect electric conductor sphere with different radii; then a realistic aircraft will be considered. We found that using GPUs for FFT-based methods allows achieving a reasonable speed-up.

(Elia A. Attardo1, Matteo A. Francavilla, Francesca Vipiana and Giuseppe Vecchi: “Investigation on Accelerating FFT-Based Methods for the EFIE on Graphics Processors”, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, to appear, Nov. 2012. [DOI])


July 29th, 2010

From the paper’s abstract:

A wide class of finite element electromagnetic applications requires computing very large sparse matrix vector multiplications (SMVM). Due to the sparsity pattern and size of the matrices, solvers can run relatively slowly. The rapid evolution of graphic processing units (GPUs) in performance, architecture and programmability make them very attractive platforms for accelerating computationally intensive kernels such as SMVM. This work presents a new algorithm to accelerate the performance of the SMVM kernel on graphic processing units.

From the paper’s conclusion:

We have introduced several efficient techniques to accelerate the execution of the sparse matrix vector multiplication (SMVM) on NVIDIA graphic processing units. The proposed methods increased the performance of the SMVM kernel on GT 8800 up to 18.8 times compared to the quad core CPU and 3 times compared to previous work by Bell and Garland on accelerating SMVM for GPUs.

(M. Mehri Dehnavi, D. Fernandez and D. Giannacopoulos: “Finite element sparse matrix vector multiplication on GPUs”. IEEE Transactions on Magnetics, vol. 46, no. 8, pp. 2982-2985, August 2010. DOI 10.1109/TMAG.2010.2043511)