[shepardrtc]

Yes, this is a fundamental part of Python. By default, a single Python process is single-threaded in the traditional sense. So, using "threads" (i.e. the Threading module) in Python is actually more like using fibers in some other language. They're not OS threads. So, if you're not waiting on I/O, then yes, the threads will fight over the GIL and performance will suffer. This is inherent to Python and will not be changed.

But there's a few more things that can be said about this. Python "threads" are really just a mental construct for designing programs. The selling point is that you can share variables and data between "threads" without having to worry about locks or data corruption or anything like that. It just works. But, even with that advantage, you're relying on Python to switch between "threads" on its own, and that could easily slow things down. If you're willing to drop the mental construct and go for better performance but still use a single process and be able to share variables, the asyncio module will let you control when the main Python process will move between points of code flow.

However, if you really want to use traditional multiple processes/threads just use the Multiprocessing module. It actually launches multiple Python processes and links them together. It's called in a similar fashion to Threading, so there isn't much code change for that part. But because it's no longer a single process - and no longer bound by the GIL - you can't share data between the processes as easily. With Multiprocessing, you'll need to create slightly more complex data structures (like a multiprocessing manager namespace) to share that data. It's not that hard, but it requires a bit of planning ahead of time.