I think this patch could certainly do with revisiting:

1) reference counting used atomic operations on Windows but on Linux used a full pthread mutex; if that was necessarily back when the patch was written it's definitely not necessarily today.

2) Garbage collection could eliminate the reference counting problem entirely.

3) There's been a lot of work on immutable data structures that could address the dictionary locking issue.

I do wonder if this is really a deeper issue - programming isn't nearly so simple once you have multiple threads concurrently modifying your data structures and you have to start locking etc. A lot of the appeal of Python is that it's so simple, and I think threads and locks would greatly affect that.

I like the idea of exploring other models. Shared data and threads is not the only approach to parallelism.

And, usually, when you are stuck solving a hard problem, it often pays to take a step back and make sure we are failing to solve a problem you should solve. The problem we want to solve is not how to get rid of the GIL, nor improve Python performance with threads but how to use Python more effectively on multi-core/thread architectures and gain performance from that.

This is not a problem only Python has. The machines I work on most of the time (a Core i5 laptop and an Atom netbook) rarely experience loads larger than 2. There are simply not enough threads to keep them busy.

That's not to say they never get slow - they do - but I'd like to emphasize that the limiting factor here is that we are not extracting parallelism from the software already written. We stand do gain a lot from that.