A stated goal of this block-diagonal-bordered LU solver is to simplify the task organization of the parallel LU algorithm and have interprocessor communications significantly reduced and regular. The performance of this block-diagonal-bordered LU solver is dependent on the ability to order the real power systems sparse matrices into the appropriate form with both uniformly distributed data in the diagonal blocks and a minimum number of equations on the lower border. In section 8.1, we illustrate the ordering capabilities of the node-tearing nodal analysis by presenting pseudo-images of selected sparse power systems network matrices after we have applied our node-tearing algorithm to partition the matrices into block-diagonal-bordered form and also have applied the pigeon-hole load-balancing algorithm. We provide additional information as to the overall performance of the three-step preprocessing phase, with special note to the amount of fillin in the matrices after ordering and to the total number of floating operations required to factor the matrices. We then report on the performance of the block-diagonal-bordered sparse LU and Choleski solvers in section 8.2. Performance of these parallel block-diagonal-bordered direct linear solvers is dependent on both the ability of the node-tearing algorithm and the performance of the parallel implementations. The real performance test of the node-tearing algorithm occurs when the performance of the block-diagonal-bordered sparse LU solver is examined for real power system network matrices in section 8.2. In section 8.3, we present our conclusions concerning the performance of our parallel implementations --- and add projections of how the parallel algorithms would perform when ported to near-term future scalable parallel processing (SPP) architectures.