TY - GEN

T1 - A GPU-accelerated integral-equation solution for large-scale electromagnetic problems

AU - Guan, Jian

AU - Yan, Su

AU - Jin, Jianming

PY - 2014/11/12

Y1 - 2014/11/12

N2 - The method of moments (MoM) has been developed and widely used for solving electromagnetic scattering and radiation problems. The major disadvantage of the MoM is that it has O(N2) computational and storage complexities, which result in a large memory requirement and a tremendous amount of computation time (J.-M. Jin, Theory and Computation of Electromagnetic Fields. Hoboken, New Jersey: Wiley, 2010). To alleviate these problems, a GPU-accelerated multilevel fast multipole algorithm (MLFMA) has been developed with a capability of solving one-million-unknown problems on four GPUs (J. Guan, S. Yan, and J.-M. Jin, IEEE Trans. Antennas Propag., vol. 60, pp. 3607-3616, June 2013). However, this parallelized algorithm requires substantially more GPU resources if the problem size increases further, which would result in a reduction of the computational efficiency because more data communications between CPU and GPU are required in the MLFMA. To overcome this problem, a 'compute on-the-fly' strategy is investigated in this work, with the objective to solve larger problems with limited GPU resources.

AB - The method of moments (MoM) has been developed and widely used for solving electromagnetic scattering and radiation problems. The major disadvantage of the MoM is that it has O(N2) computational and storage complexities, which result in a large memory requirement and a tremendous amount of computation time (J.-M. Jin, Theory and Computation of Electromagnetic Fields. Hoboken, New Jersey: Wiley, 2010). To alleviate these problems, a GPU-accelerated multilevel fast multipole algorithm (MLFMA) has been developed with a capability of solving one-million-unknown problems on four GPUs (J. Guan, S. Yan, and J.-M. Jin, IEEE Trans. Antennas Propag., vol. 60, pp. 3607-3616, June 2013). However, this parallelized algorithm requires substantially more GPU resources if the problem size increases further, which would result in a reduction of the computational efficiency because more data communications between CPU and GPU are required in the MLFMA. To overcome this problem, a 'compute on-the-fly' strategy is investigated in this work, with the objective to solve larger problems with limited GPU resources.

UR - http://www.scopus.com/inward/record.url?scp=84916218722&partnerID=8YFLogxK

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U2 - 10.1109/USNC-URSI.2014.6955563

DO - 10.1109/USNC-URSI.2014.6955563

M3 - Conference contribution

AN - SCOPUS:84916218722

T3 - 2014 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2014 - Proceedings

BT - 2014 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2014 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2014

Y2 - 6 July 2014 through 11 July 2014

ER -