[_0w8t]

This is my thinking as well. Python is nice to glue things, but doing high-performance math is not its strength. Things like GIL should be addressed long time ago, but it seems it is so fundamental to make things work in Python that I have big doubts that it will ever be addressed.

[Q6T46nT668w6i3m]

I agree that the GIL has become a problem for a variety of high-performance tasks, but, I’m curious, what kind of problems have you encountered with numerical computation? I contribute to both NumPy and TensorFlow, two libraries with different processing models, and I don’t see any obvious area where removing the GIL would provide substantial benefits. However, I’ll readily admit that I don’t think about this too often and it’s entirely possible I’m missing something obvious! Maybe Julia could provide some guidance around this.

I would also bet (but not too much) that we eventually see major progress in removing the GIL. I really don’t think it’ll be around forever!

[_0w8t]

numpy wiki summaries that well [1]. Too many things especially with complex math cannot run in parallel unless one spends a lot of time on workarounds.

One starts with quick and dirty solution, makes it work on a small dataset and then struggle to make it utilize at least 4 cores to cut running time with more realist datasets. Surelly I can code numerical calculations in C++, but then the code cannot be maintained by python-only guy. So I hope that Julia or anything else with better parallel support replaces Python for scientific calculations when scaling quick and dirty solutions is straightforward.

[1] http://scipy-cookbook.readthedocs.io/items/ParallelProgrammi...

[dagw]

The GIL has been 'addressed' regularly since python 1.4, just no one has come up with an acceptable solution.

[crimsonalucard]

By 'addressed' I think he means 'solved'.