I was responding to your general statement that python is slow and so there is no point in making it faster, I agree that removing the GIL wont do much to improve the execution speed for programs making heavy use of numpy or things calling outside it.
That’s a bit suss too tbh. Did the C++ version use an existing library like Eigen too or did they implement everything from scratch?
It was written entirely from scratch which is kind of my point, a well writen python program can outperform a naive c implementation and is vastly simpler to create.
If you have the expertise and are willing to put in the effort you likely can squeze that extra bit of performance out by dropping to a lower level language, but for certain workloads you can get good performance out of python if you know what you are doing so calling it extremely slow and saying you have to move to another language if you care about performance is missleading.
The point is that eliminating the GIL mainly benefits pure Python code. Numpy is already multithreaded.
I think you may have forgotten what we’re talking about.
That’s a bit suss too tbh. Did the C++ version use an existing library like Eigen too or did they implement everything from scratch?
I was responding to your general statement that python is slow and so there is no point in making it faster, I agree that removing the GIL wont do much to improve the execution speed for programs making heavy use of numpy or things calling outside it.
It was written entirely from scratch which is kind of my point, a well writen python program can outperform a naive c implementation and is vastly simpler to create.
If you have the expertise and are willing to put in the effort you likely can squeze that extra bit of performance out by dropping to a lower level language, but for certain workloads you can get good performance out of python if you know what you are doing so calling it extremely slow and saying you have to move to another language if you care about performance is missleading.