A Parallel Implementation of the Eigenproblem for Large, Symmetric and Sparse Matrices

Authors:
E. M. Garzon;I. García
Affiliations:
-;-
Venue:
Proceedings of the 6th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Year:
1999

Citing 4
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Abstract

This work studies the eigenproblem of large, sparse and symmetric matrices through algorithms implemented in distributed memory multiprocessor architectures. The implemented parallel algorithm operates in three stages: structuring input matrix (Lanczos Method), computing eigenvalues (Sturm Sequence) and computing eigenvectors (Inverse Iteration). Parallel implementation has been carried out using a SPMD programming model and the PVM standard library. Algorithms have been tested in a multiprocessor system Cray T3E. Speed-up, load balance, cache faults and profile are discussed. From this study, it follows that for large input matrices our parallel implementations perceptibly improve the management of the memory hierarchy.