One of my teachers used to say: "Thinking is not knowing"

why would the author iceland2k14 then hide the source code by not disclosing it? Especially if he still uses source code from AlbertoBSD and Jean-Luc-Ponds? Apart from the fact that this would be illegal as far as I know, since the tools mentioned use appropriate GNU licenses, why would iceland2k14 hide the code if there was nothing to hide? I think this is an interesting point that ymgve2 has made here. I hadn't given this library any serious thought before, but he's right in a way. To the extent that we as users of this closed-source library don't know if malicious functions are being run in the background or data is being exfiltrated, I see that as a danger as well. I will therefore consider not using this library until I am convinced otherwise. Thanks to both of you for this important advice.


I understand that while Python code executed on the GPU using these libraries can be very fast, it may not be as fast as native C++ code running on the GPU. This is because the Python interpreter introduces some overhead, and the libraries themselves may have some overhead as well. However, for many applications, the speedup provided by running on the GPU can more than make up for any overhead introduced by Python, and can still result in significantly faster calculations than would be possible using only the CPU. That is why I think it is reasonable to get such calculations running on the GPU, it should be possible with Python.

Python can be used for fast calculations with CUDA by using libraries that provide Python bindings for CUDA. These libraries allow you to write code in Python that can be executed on the GPU, which can provide significant speedups for certain types of calculations. Examples of such libraries that provide Python bindings for CUDA are -as already mentioned before- PyCUDA or Numba. PyCUDA allows writing code in Python that is executed on the GPU using CUDA. It provides a Pythonic interface to the CUDA programming model, making it easy to use for Python developers. Numba, which is a just-in-time (JIT) compiler for Python that can automatically optimize Python code for execution on the GPU. Numba can be used to write GPU-accelerated code in Python using familiar Python syntax, and it can provide significant performance improvements for certain types of calculations. Unfortunately I wasn't able to successful use those two libraries until now, hopefully anyone else can give some insight and assistance here. Sample code is provided on my first post.