I really hope for Julia to become mainstream and maybe replace Python as the defacto lang for data science. Julia is an incredible language. Kudos to the team developing it.
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.
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!
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.