Probability-one homotopy methods are a class of methods for solving non-linear systems of equations that are globally convergent with probability one from an arbitrary starting point. The essence of these algorithms is the construction of an appropriate homotopy map and subsequent tracking of some smooth curve in the zero set of the homotopy map. Tracking a homotopy zero curve requires calculating the unit tangent vector at different points along the zero curve. Because of the way a homotopy map is constructed, the unit tangent vector at each point in the zero curve of a homotopy map ρ_α(λ,x) is in the one-dimensional kernel of the full rank n x (n + 1) Jacobian matrix Dρ_α(λ,x). Hence, tracking a zero curve of a homotopy map involves evaluating the Jacobian matrix and finding the one-dimensional kernel of the n x (n + 1) Jacobian matrix with rank n. Since accuracy is important, an orthogonal factorization of the Jacobian matrix is computed. The QR and LQ factorizations are considered here. Computational results are presented showing the performance of several different parallel orthogonal factorization/triangular system solving algorithms on a hypercube, in the context of parallel homotopy algorithms for problems with small, dense Jacobian matrices. This study also examines the effect of different component complexity distributions and the size of the Jacobian matrix on the different assignments of components to the processors, and determines in what context one assignment would perform better than others.