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Photon transport within Earth’s atmosphere is a vital aspect of atmospheric science. The accurate modeling of radiative transfer is crucial for remote sensing data analysis. Yet, simulating the photon transport in multi-dimensional models poses a significant computational challenge. Monte Carlo simulations are a common approach, but they demand a large number of photons for reliable results. Parallelization techniques can be employed to accelerate Monte Carlo computations by using multi-core CPUs and GPUs. This research delves into a comparative analysis of different parallelization techniques for the Python version of the Monte Carlo model. We consider conventional photon transport simulations that rely on iterative loops, the multithreading technique, NumPy’s vectorization, and GPU acceleration via the CuPy library. It is shown that CuPy, harnessing GPU parallelism, significantly accelerates simulations, making them suitable for large-scale scenarios. It is shown that as the number of photons grows, the overhead from reading and retrieving data to the GPU decreases, making the CuPy library an effective and easy-to-use option for Monte Carlo simulations.