Vectorization and multitasking of nonlinear network programming algorithms

Vector supercomputers are designed with two levels of parallelism in order to achieve computational efficiency: low level parallelism through vector operations and high level parallelism with multiple independent processors. These innovations have a significant impact on the development of algorithms for network optimization. In this paper a framework for the vectorization and multitasking of optimization software is developed. It is then applied on the primal truncated Newton algorithm for nonlinear generalized network problems. The vectorization and multitasking of the algorithm is discussed and illustrated with computational experiments with the software system NLPNETG on the CRAY series of vector multiprocessors.