Efficient vectorization of forward/backward substitutions in solving sparse linear equations

Vector processors have promised an enormous increase in computing speed for computationally intensive and time-critical power system problems which require the repeated solution of sparse linear equations. Due to short vectors processed in these applications, standard sparsity-based algorithms need to be restructured for efficient vectorization. The paper presents a novel data storage scheme and an efficient vectorization algorithm that exploits the intrinsic architectural features of vector computers such as sectioning mid chaining. As the benchmark, the solution phase of the Fast Decoupled Load Flow algorithm is used in simulations. The relative performances of the proposed and existing vectorization schemes are evaluated both theoretically and experimentally on an IBM 3090/VF