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Sparse Matrix Operations In Vector And ParallelProcessors

Vector computers have been extensively used for years in matrix algebra to treat with large dense matrix problems. However, if matrices are sparse and we use special storage schemes for them, vectorization provides a poor performance due to the great amount of indirections in the code. An alternative option is the utilization of a multiprocessor (or a cluster of workstations); in this case, a data parallel programming model also fails because of the reason pointed out for vector computers. Therefore, the best choice is to parallelize the corresponding algorithms using message passing routines. In order to discuss these features, we will focus on solving sparse linear least squares problems, which appear in several scientific areas such as structural analysis, geodetic survey, molecular structure and many others. Experimental results are obtained for vector and parallel computer architectures.