Could you explain the performance difference when using CUDA with Python on a low-end GPU but processing large datasets?
Additionally, will CUDA with C++ perform faster in this case? I assume there might be a significant difference when utilizing a lot of RAM but with a weak GPU.
To a first order approximation, I don’t expect differences in CUDA processing whether the work is dispatched via python or via C++. an implementation such as numba or pycuda doesn’t have significant differences for basic CUDA kernel activity, compared to CUDA C++.
Note that even CUDA-accelerated applications often include significant portions of host-side processing, and this can become a performance limiting factor once faster GPUs are deployed. Under this aspect, Python offers the advantage of rapid prototyping, while C++ offers the advantage of maximum processing speed.
It all depends on the use case. I know of cases where people new to CUDA used Python and a library like Numba to build a GPU-accelerated processing pipeline within a month. As this solution gave them a 10x performance advantage over their previous CPU-only solution, they simply left it at that.