Abstract
We discuss several algorithms for the solution of sparse linear systems on parallel architectures. We will be concerned primarily with shared memory machines and will consider both direct and iterative methods of solution. The direct methods are based on Gaussian elimination and exploit parallelism coming from both the sparsity and from a full matrix kernel. The iterative method is a block projection method using a direct solver within the blocks. We then discuss the development of two codes, one for the solution of general sparse equations, the other for sparse symmetric indefinite systems. Some results on vector multiprocessor supercomputers are presented to illustrate the behaviour of our approaches.
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