1. A method for automating parallelization kernel selection in distributed simulations, comprising: receiving, by one or more processors, data representing a model simulation job; converting, by the one or more processors, the received data into a format suitable for processing; first performing, by the one or more processors, simulation runs of the model simulation job with an external parallelization kernel; determining, by the one or more processors, whether all simulation runs in the model simulation job successfully completed on the external kernel; second performing, by the one or more processors, simulation runs of the model simulation job with an internal kernel responsive to a determination that not all simulation runs in the model simulation job successfully completed on the external kernel; generating, by the one or more processors, model output data representing results of all simulation runs in the model simulation job; and selecting the external kernel or the internal kernel based on the model output data.