CUA OpenMP Nonlinear Optimization Tool

Nonlinear programming (NLP) allows for the solution of complex engineering problems, however, none of the currently available solvers fully capitalize on parallel computing. Many NASA trajectory design packages (OTIS, EMTG, MALTO) have already had their own code streamlined, and it is now the serial execution of existing NLP solvers that represents the largest bottleneck. It is the goal of this Phase II effort to further develop the CUA OpenMP Nonlinear Optimization Tool (COMPNOT), which will utilize shared memory systems to significantly improve the time-to-solution of NLP problems. As large-scale shared memory parallel systems, such as Intel’s Xeon Phi family, become more commercially available, COMPNOT will greatly expand the market for a parallel NLP solver, even enabling most modern desktop computers to effectively run it. Phase II will focus largely on creating a distributed/shared memory hybrid mode, enabling COMPNOT to take advantage of the shared memory nodes that comprise large distributed memory systems. Additionally the development of hardware-specific optimization, focusing on the Intel Math Kernel Library (MKL), will be a priority. At the end of Phase II, COMPNOT can begin integration into NASA trajectory design packages, significantly reducing the time-to-solution.