Parallel versions of the Jacobi and Successive Overrelaxation methods
to solve systems of linear equations on a network of transputers are
discussed.  The Jacobi method is easily parallelizable, by distributing
the data in a rowwise fashion among the processors. The SOR is a less
obviously parallelizable method due to data-dependency issues, although
some computation can still be distributed.  An important feature of our
implementations is the parallel computation of matrix norms, which
allow a significant speed-up over the serial versions of the
algorithms.  We show a significant improvement in the efficiency of our
implementations over other previously published parallel iterative
linear equations solvers.