Parallelize simulation function of models to process multiple parameter sets in parallel

[kratzert]

The parallelization feature of the numba library yields a great potential for further speed improvements, whenever multiple parameter for a model needs to be evaluated. This can be done in parallel, since each model run with one parameter set is independent of another parameter set. First quick test show huge speed improvements that come to play e.g. for the Monte Carlo implementation or any further optimization scheme.

Here I have published an article explaining some of the basics of numbas parallelization features. The important two things are the parallel=True flag for the decorator, as well as the prange function, used to iterate of the parameter sets.

This might need adaption in the _loss() function of each model, since they are expecting a 1-D array as output of the simulation function, but will now return a 2-D array.