The computation of partial eigensolutions on a distributed memory machine using a modified lanczos method
The Lanczos algorithm is one of the most widely used methods for finding a small number of the extremal eigenvalues and associated eigenvectors of large, sparse, symmetric matrices. In this paper the performance of a modified version of the algorithm which incorporates a novel convergence monitoring method is assessed. The investigation has been carried out using a 16-node Intel iPSC/860 hypercube. It is shown that a parallel implementation of the modified algorithm can efficiently exploit the facilities provided by this machine.
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