The success of homotopy methods in solving large-scale optimization problems and nonlinear systems of equations depends heavily on the solution of large sparse nonsymmetric linear systems on parallel architectures. Iterative solution techniques, such as GMRES(k), favor parallel implementations. However, their straightforward parallelization usually leads to a poor parallel performance because of global communication incurred by processors. One variation of GMRES(k) considered here is to adapt the restart value k for any given problem and use Householder reflections in the orthogonalization phase to achieve high accuracy and reduce the communication overhead.