On the compression of translation operator tensors in FMM-FFT-accelerated SIE simulators via tensor decompositions

Tensor decomposition methodologies are proposed to reduce the memory requirement of translation operator tensors arising in the fast multipole method-fast Fourier transform (FMM-FFT)-accelerated surface integral equation (SIE) simulators. These methodologies leverage Tucker, hierarchical Tucker...

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Main Authors: Qian, Cheng, Yucel, Abdulkadir C.
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/159775
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1597752022-07-01T08:08:23Z On the compression of translation operator tensors in FMM-FFT-accelerated SIE simulators via tensor decompositions Qian, Cheng Yucel, Abdulkadir C. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Tensors Memory Management Tensor decomposition methodologies are proposed to reduce the memory requirement of translation operator tensors arising in the fast multipole method-fast Fourier transform (FMM-FFT)-accelerated surface integral equation (SIE) simulators. These methodologies leverage Tucker, hierarchical Tucker (H-Tucker), and tensor train (TT) decompositions to compress the FFT'ed translation operator tensors stored in three-dimensional (3D) and four-dimensional (4D) array formats. Extensive numerical tests are performed to demonstrate the memory saving achieved by and computational overhead introduced by these methodologies for different simulation parameters. Numerical results show that the H-Tucker-based methodology for 4D array format yields the maximum memory saving while Tucker-based methodology for 3D array format introduces the minimum computational overhead. For many practical scenarios, all methodologies yield a significant reduction in the memory requirement of translation operator tensors while imposing negligible/acceptable computational overhead. Ministry of Education (MOE) Nanyang Technological University This work was supported in part by the Ministry of Education, Singapore, under Grant AcRF TIER 1-2018-T1-002-077 (RG 176/18) and in part by Nanyang Technological University under a Start-Up Grant. 2022-07-01T08:08:23Z 2022-07-01T08:08:23Z 2020 Journal Article Qian, C. & Yucel, A. C. (2020). On the compression of translation operator tensors in FMM-FFT-accelerated SIE simulators via tensor decompositions. IEEE Transactions On Antennas and Propagation, 69(6), 3359-3370. https://dx.doi.org/10.1109/TAP.2020.3030981 0018-926X https://hdl.handle.net/10356/159775 10.1109/TAP.2020.3030981 2-s2.0-85107350364 6 69 3359 3370 en 2018-T1-002-077 (RG 176/18) IEEE Transactions on Antennas and Propagation © 2020 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Tensors
Memory Management
spellingShingle Engineering::Electrical and electronic engineering
Tensors
Memory Management
Qian, Cheng
Yucel, Abdulkadir C.
On the compression of translation operator tensors in FMM-FFT-accelerated SIE simulators via tensor decompositions
description Tensor decomposition methodologies are proposed to reduce the memory requirement of translation operator tensors arising in the fast multipole method-fast Fourier transform (FMM-FFT)-accelerated surface integral equation (SIE) simulators. These methodologies leverage Tucker, hierarchical Tucker (H-Tucker), and tensor train (TT) decompositions to compress the FFT'ed translation operator tensors stored in three-dimensional (3D) and four-dimensional (4D) array formats. Extensive numerical tests are performed to demonstrate the memory saving achieved by and computational overhead introduced by these methodologies for different simulation parameters. Numerical results show that the H-Tucker-based methodology for 4D array format yields the maximum memory saving while Tucker-based methodology for 3D array format introduces the minimum computational overhead. For many practical scenarios, all methodologies yield a significant reduction in the memory requirement of translation operator tensors while imposing negligible/acceptable computational overhead.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Qian, Cheng
Yucel, Abdulkadir C.
format Article
author Qian, Cheng
Yucel, Abdulkadir C.
author_sort Qian, Cheng
title On the compression of translation operator tensors in FMM-FFT-accelerated SIE simulators via tensor decompositions
title_short On the compression of translation operator tensors in FMM-FFT-accelerated SIE simulators via tensor decompositions
title_full On the compression of translation operator tensors in FMM-FFT-accelerated SIE simulators via tensor decompositions
title_fullStr On the compression of translation operator tensors in FMM-FFT-accelerated SIE simulators via tensor decompositions
title_full_unstemmed On the compression of translation operator tensors in FMM-FFT-accelerated SIE simulators via tensor decompositions
title_sort on the compression of translation operator tensors in fmm-fft-accelerated sie simulators via tensor decompositions
publishDate 2022
url https://hdl.handle.net/10356/159775
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