MATRIX FACTORIZATION ON A HYPERCUBE MULTIPROCESSOR.

This paper is concerned with parallel algorithms for matrix factorization on distributed-memory, message-passing multiprocessors, with special emphasis on the hypercube. We consider both Cholesky factorization of symmetric positive definite matrices and LU factorization of nonsymmetric matrices using partial pivoting. We also consider the use of the resulting triangular factors to solve systems of linear equations by forward and back substitutions. Efficiencies of various parallel computational approaches are compared in terms of empirical results obtained on an Intel iPSC hypercube.