The CPU-accelerated code to compute the 𝐸𝐸 "⃗ (𝑟𝑟) streamline distributions scales linearly with the number of cores provided, as the problem is grossly parallelizable (Fig. ). We have implemented C-shared libraries to boost performance on computation of 𝐸𝐸 "⃗ (𝑟𝑟). We compare the performance of both CPU (with 32 cores) and GPU-accelerated codes in Fig. and Fig. , for various number of streamlines and step sizes, at smaller (Fig. ) and larger (Fig. ) volumes. Since a larger volume means longer computed streamlines, the GPU's ability to store and propagate a matrix of streamlines simultaneously provides faster performance times (see Supplementary Notes). The same effect is seen at smaller step sizes, as more steps are required for a streamline of the same size. Therefore, we prescribe the GPU-accelerated code for systems where larger volumes and/or finer stepsizes are required for a well-converged 𝐸𝐸 "⃗ (𝑟𝑟) streamline distribution. Lastly, we compared GPU to CPU (with 32 cores) performance based on system size, proxied by number of charges included (Figure ), and implies further utility of the GPU code for systems with >100,000 point charges. Furthermore, for individual workstations with a single GPU, but limited number of dedicated CPUs, the GPU code would be preferred for performance, as based on the scaling relationships shown in Figure , the GPU code would outperform 16-core CPU acceleration on several topology parameters and system sizes.