We demonstrated that converging streamline distributions requires fine-step size and large number of streamline samples, as well as a large radius of protein charges. This leads to an increase in computational cost; therefore, we have developed CPU and GPU-accelerated codes for PyCPET that reduce the cost of computing streamline distributions. In our CPU-accelerated implementation, we have parsed the problem of computing properties of field streamlines by assigning each streamline to a thread (see Methods), and therefore achieving roughly linear accelerations in computation with number of threads provided. Our GPU-accelerated implementation computes all streamlines simultaneously on a grid, and therefore outperforms CPU-accelerated codes on several metrics.