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- W2025576418 abstract "We propose here a subspace augmented Rayleigh-Ritz conjugate gradient method (SaRCG) for solving large-scale eigen-value problems. The method is highly scalable and well suited for multi-core architectures since it only requires sparse matrix-vector multiplications (SpMV). As a specific application, we consider the modal analysis of geometrically complex structures that are discretized via non-conforming voxels. The voxelization process is robust and relatively insensitive to geometric complexity, but it leads to large eigen-value problems, that are difficult to solve via standard eigen-solvers such as block-Lanczos. Such problems are easily solved via the proposed SaRCG, where one can, in addition, exploit the voxelization structure to render the SpMV assembly-free. As the numerical experiments indicate, the resulting implementation on multi-core CPUs, and graphics-programmable-units is a practical solution to automated eigen-value estimation during early stages of design." @default.
- W2025576418 created "2016-06-24" @default.
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- W2025576418 date "2012-08-12" @default.
- W2025576418 modified "2023-09-26" @default.
- W2025576418 title "Large-Scale Modal Analysis on Multi-Core Architectures" @default.
- W2025576418 doi "https://doi.org/10.1115/detc2012-70281" @default.
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