This sort of parallelism is common for big matrix number-crunching on machines with large numbers of CPUs. There's a big computational cycle, where lots of CPUs are turned loose on an isolated chunk of the problem. No shared state changes during this phase. When they're all done, there's a state update to shared state for the next round. For that kind of work, each thread does about the same amount of work, so the threads finish at roughly the same time. This is useful for finite element analysis, hydrodynamics, weather prediction, and similar large problems that decompose into spatial cells. If everything follows the updating isolation rules, results are repeatable.

But for real-time control?