
SuperVoxHenry TuckerEnhanced and FFTAccelerated Inductance Extraction for Voxelized Superconducting Structures
This paper introduces SuperVoxHenry, an inductance extraction simulator ...
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On the Compression of Translation Operator Tensors in FMMFFTAccelerated SIE Simulators via Tensor Decompositions
Tensor decomposition methodologies are proposed to reduce the memory req...
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Solution of WienerHopf and Fredholm integral equations by fast Hilbert and Fourier transforms
We present numerical methods based on the fast Fourier transform (FFT) t...
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Memory footprint reduction for the FFTbased volume integral equation method via tensor decompositions
We present a method of memory footprint reduction for FFTbased, electro...
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A FullyAccelerated Surface Integral Equation Method for the Electromagnetic Modeling of Arbitrary Objects
Surface integral equation (SIE) methods are of great interest for the nu...
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OutofCore and Distributed Algorithms for Dense Subtensor Mining
How can we detect fraudulent lockstep behavior in largescale multiaspe...
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VoxCap: FFTAccelerated and TuckerEnhanced Capacitance Extraction Simulator for Voxelized Structures
VoxCap, a fast Fourier transform (FFT)accelerated and Tuckerenhanced integral equation simulator for capacitance extraction of voxelized structures, is proposed. The VoxCap solves the surface integral equations (SIEs) for conductor and dielectric surfaces with three key attributes that make the VoxCap highly CPU and memory efficient for the capacitance extraction of the voxelized structures: (i) VoxCap exploits the FFTs for accelerating the matrixvector multiplications during the iterative solution of linear system of equations arising due to the discretization of SIEs. (ii) During the iterative solution, VoxCap uses a highly effective and memoryefficient preconditioner that reduces the number of iterations significantly. (iii) VoxCap employs Tucker decompositions to compress the block Toeplitz and circulant tensors, requiring the largest memory in the simulator. By doing so, it reduces the memory requirement of these tensors from hundreds of gigabytes to a few megabytes and the CPU time required to obtain Toeplitz tensors from tens of minutes (even hours) to a few seconds for very large scale problems. VoxCap is capable of accurately computing capacitance of arbitrarily shaped and largescale voxelized structures on a desktop computer.
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