In the case of RMG, parallelization is challenging to implement since the core algorithm described in detail by Gao et al. is iterative in nature, i.e., tasks must be performed in order because they rely on the results or prior tasks. However, there are certain portions of the algorithm which are more amenable to parallelization. In RMG v3.0, parallelization has been completely revamped using the built-in multiprocessing module in Python, providing parallel processing support for reaction generation and quantum calculations for the QMTP (Quantum Mechanics for Thermochemical Properties) module.