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Large-Scale Modal Analysis on Multi-Core Architectures

[+] Author Affiliations
Krishnan Suresh, Praveen Yadav

University of Wisconsin-Madison, Madison, WI

Paper No. DETC2012-70281, pp. 785-791; 7 pages
  • ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 32nd Computers and Information in Engineering Conference, Parts A and B
  • Chicago, Illinois, USA, August 12–15, 2012
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4501-1
  • Copyright © 2012 by ASME


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.

Copyright © 2012 by ASME
Topics: Architecture



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