%0 Conference Paper
%A Basermann, Achim
%A Steffen, Bernhard
%T New Preconditioned Solvers for Large Sparse Eigenvalue Problems on Massively Parallel Computers
%C Philadelphia, Pa.
%I Society for Industrial and Applied Mathematics
%M FZJ-2014-04412
%@ 0-89871-395-1
%P 8 p.
%D 1997
%< Proceedings of the Eighth SIAM Conference on Parallel Processing for Scientific Computing, PPSC 1997
%X We present preconditioned solvers to find a few eigenvalues and eigenvectors of large dense or sparse symmetric matrices based on the Jacobi-Davidson (JD) method by G. L. G. Sleijpen and H. A. van der Vorst. For preconditioning, we apply a new adaptive approach using the QMR iteration. To parallelize the solvers, we divide the interesting part of the spectrum into a few overlapping intervals and asynchronously exchange eigenvector approximations from neighboring intervals to keep the solutions separated. Per interval, matrix-vector and vector-vector operations of the JD iteration are parallelized by determining a data distribution and a communication scheme from an automatic analysis of the sparsity pattern of the matrix. We demonstrate the efficiency of these parallelization strategies by timings on an Intel Paragon and a Cray T3E system with matrices from real applications.
%B Eighth SIAM Conference on Parallel Processing for Scientific Computing
%C 14 Mar 1997 - 17 Mar 1997, Minneapolis (USA)
Y2 14 Mar 1997 - 17 Mar 1997
M2 Minneapolis, USA
%K Parallelverarbeitung (gbv)
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
%9 Contribution to a conference proceedingsContribution to a book
%U https://juser.fz-juelich.de/record/155238