Abstract
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.
This work was supported by the Engineering and Physical Sciences Research Council under grants GR/J41857 and GR/J41864 and was carried out using the facilities of the Daresbury Laboratory.
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Szularz M., Weston J.S., Murphy K., Clint M.:Monitoring the convergence of the Lanczos algorithm in parallel computing environments. J. of Parallel Algorithms & Applications 6 (1995) 287–302
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© 1996 Springer-Verlag Berlin Heidelberg
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Murphy, K., Clint, M., Szularz, M., Weston, J. (1996). The computation of partial eigensolutions on a distributed memory machine using a modified lanczos method. In: Bougé, L., Fraigniaud, P., Mignotte, A., Robert, Y. (eds) Euro-Par'96 Parallel Processing. Euro-Par 1996. Lecture Notes in Computer Science, vol 1124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024680
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DOI: https://doi.org/10.1007/BFb0024680
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