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Three conjugate gradient accelerated row projection (RP) methods for nonsymmetric linear systems are presented and their properties described. One method is based on Kaczmarz's method and has an iteration matrix that is the product of orthogonal projectors; another is based on Cimmino's method and has an iteration matrix that is the sum of orthogonal projectors. A new RP method which requires fewer matrix-vector operations, explicitly reduces the problem size, is error reducing in the 2-norm, and consistently produces better solutions than other RP algorithms is also introduced. Using comparisons with the method of conjugate gradient applied to the normal equations, the properties of RP methods are explained. A row partitioning approach is described which yields parallel implementations suitable for a wide range of computer architectures, requires only a few vectors of extra storage, and allows computing the necessary projections with small errors. Numerical testing veri es the robustness of this approach and shows that the resulting algorithms are competitive with other nonsymmetric solvers in speed and e ciency.

Scientific and engineering research is becoming increasingly dependent upon the development and implementation of efficient parallel algorithms on modern high-performance computers. Numerical linear algebra is an indispensable tool in such research and this paper attempts to collect and describe a selection of some of its more important parallel algorithms. The purpose is to review the current status and to provide an overall perspective of parallel algorithms for solving dense, banded, or block-structured problems arising in the major areas of direct solution of linear systems, least squares computations, eigenvalue and singular value computations, and rapid elliptic solvers. A major emphasis is given here to certain computational primitives whose efficient execution on parallel and vector computers is essential in order to obtain high performance algorithms.

Abstract. Many existing algorithms for obtaining the eigenvalues and eigenvectors of matrices would make poor use of such a powerful parallel computer as the ILLIAC IV. In this paper, Jacobi's algorithm for real symmetric or complex Hermitian matrices, and a Jacobi-like algorithm for real nonsymmetric matrices developed by P. J. Eberlein, are modified so as to achieve maximum efficiency for the parallel computations.