NONLINEAR NETWORK PROGRAMMING ON VECTOR SUPERCOMPUTERS: A STUDY ON THE CRAY X-MP.

We survey recent trends in parallel computer systems and study the impact of vector computing on nonlinear network programming. We propose a general framework for migrating FORTRAN optimization software to a vector computer, and apply it in the context of two nonlinear network codes: NLPNETG, based on the primal truncated Newton algorithm, and GNSD, based on the simplicial decomposition method. We include computational experiments on a CRAY X-MP/24 system that tested the nonlinear network codes and compared the results with those of MINOS, a general purpose optimizer. Our experience indicates that vectorized codes can achieve significant improvements in performance (as much as 80% for primal truncated Newton), but achieve only modest improvements (15% for simplicial decomposition) for other algorithms.