  We introduce an efficient parallelization scheme to implement pixel-by-pixel
nanophotonic optimization using a Green's function based formalism. The crucial
insight in our proposal is the reframing of the optimization algorithm as a
large-scale data processing pipeline, which allows for the efficient
distribution of computational tasks across thousands of workers. We demonstrate
the utility of our implementation by exercising it to optimize a high numerical
aperture focusing metalens at problem sizes that would otherwise be far out of
reach for the Green's function based method. Finally, we highlight the
connection to powerful ideas from reinforcement learning as a natural corollary
of reinterpreting the nanophotonic inverse design problem as a graph traversal
enabled by the pixel-by-pixel optimization paradigm.
