A new method (NI-DACG) for the partial eigensolution of large sparse symmetric FE eigen problems is presented. NI-DACG relies on the optimization of Rayleigh quotients in successively deflated subspaces by a preconditioned conjugate gradient technique and uses a multiple grid type approach to assess an improved eigenvector estimate on nested FE grids on which the solution to the continuous eigenproblem is sought. NIDACG is implemented on the CRAY Y-MP supercomputer making use of vectorization and/or parallelization with two and four processors. Results relative to the calculation of the 50 smallest eigenpairs for two representative sample problems show a gain in CPU time that exceeds one order of magnitude with respect to the scalar implementation of NI-DACG and emphasize the promising features of this technique for the partial eigenanalysis on supercomputers.
['Giorgio Pini', 'Giuseppe Gambolati']
Parallel eigenanalysis for nested grids