> That's not Amdahl's Law. Amdahl's Law describes the expected total speedup from a speedup to a segment of the program. It is usually used in the context of ad

> That's not Amdahl's Law. Amdahl's Law describes the expected total speedup from a speedup to a segment of the program. It is usually used in the context of adding parallelism but is more general than that. It does not assume parallelism has any overhead. Yeah, it's a fiction. An useful one, but still a sort of spherical cow in a vacuum scenario. It doesn't exactly describe real hardware. > Amdahl's Law predicts performance will approach a limit with added cores, but still predicts that each core will improve performance. Because it's a fiction. You can have slowdowns with bigger tasks on real hardware, like if you run out of L1 cache. > Not necessarily. Modern systems have pools with independent per-core queues and clever ways of distributing work to them with minimal overhead. True, but that's Java and this is Python. I may be wrong, but since the GIL removal is very new still, I don't expect Python to have nearly the same level of efficiency in this regard. But I could be wrong somewhere, of course.