Systems of polynomial equations arise frequently in com- puter vision, especially in multiview geometry problems. Tra- ditional methods for solving these systems typically aim to eliminate variables to reach a univariate polynomial, e.g., a tenth-order polynomial for 5-point pose estimation, using clever manipulations, or more generally using Grobner ba- sis, resultants, and elimination templates, leading to success- ful algorithms for multiview geometry and other problems. However, these methods do not work when the problem is complex and when they do, they face efficiency and stability issues. Homotopy Continuation (HC) can solve more com- plex problems without the stability issues, and with guaran- tees of a global solution, but they are known to be slow. In this paper we show that HC can be parallelized on a GPU, showing significant speedups up to 26 times on polynomial benchmarks. We also show that GPU-HC can be generically applied to a range of computer vision problems, including 4- view triangulation and trifocal pose estimation with unknown focal length, which cannot be solved with elimination tem- plate but they can be efficiently solved with HC. GPU-HC opens the door to easy formulation and solution of a range of computer vision problems