> Things which takes minutes in optimized C++ will probably take days in Python, even if I use the "accelerated" libraries for matrix operations and other math
I’m gonna need an example because I do not believe this whatsoever.
I'd rather open the code and show what I'm talking about, however I can not.
Let's say I'm making a lot of numerical calculations which are fed from a lockless queue with atomic operations to any number of cores you want, where your performance is limited by the CPU cores' FPU performance and the memory bandwidth (in terms of both transfer speed and queries that bus can handle per second).
As I noted below, that code can complete 1.7 million complete evaluations per core, per second on older (2014 level) hardware, until your memory controller congests with all the requests. I need to run benchmarks on a newer set of hardware to get new numbers, however I seriously lack the time today to do so and provide you new numbers.
There are definitely operations you cannot speed up in Python as much as in other languages, unless you implement it in one of those other languages and interface it in Python.
That much is obvious from Python providing a bunch of C-based primitives in stdlib (otherwise they'd just be written in pure Python).
In many cases, you can make use of the existing primitives to get huge improvements even with pure Python, but you are not beating optimized C++ code (which almost has direct access to CPU vector operations as well).
Python's advantage is in speed of development, not in speed of execution. And I say that as a firm believer that majority of the Python code in existence today could be much faster only if written with the understanding of Python's internal structures.
I’m gonna need an example because I do not believe this whatsoever.