What you are asking for is beyond the scope of PyMC3, in my opinion.
The simplest way to perform multiple jobs in parallel in Python is via the threading built-in module, but that won’t help much for anything computational because all jobs run on the same core. The multiprocessing module works similarly to the threading module but uses more than one core. I assume that it is not recommended to use either of these approaches in conjunction with PyMC3 since parallelization is integrated already when sampling, but I’ve never actually tried it.
I can’t imagine why you would want to perform power analysis—which I understand to be a frequentist concept related to the probability of accepting/rejecting the null hypothesis via p-values in simulated data—in PyMC3 in the first place. Unless “power” refers to something else here?