Hacker Newsnew | past | comments | ask | show | jobs | submitlogin

Regarding the section on Python and high-level CUDA, anyone interested should maybe first take a peek at Warp, which I’m guessing is too new to have a book yet. Warp lets you write CUDA kernels directly in Python, and it’s a breeze to get started. https://github.com/nvidia/warp


It's a bit confusing now with Numba Cuda also being officially maintained by Nvidia. Also Cuda Python, which looks older.

Which of these - warp, numba, cp, is the best bet for a beginner?

https://nvidia.github.io/numba-cuda/

https://developer.nvidia.com/cuda/python


I haven’t tried them all, but I suspect Warp is the easiest; it’s ridiculously easy. I’m sure there are some tradeoffs, so once you learn a little CUDA in Python it might make sense to switch from Warp to Numba or CP depending on what you’re doing.


You can also write CUDA kernels directly in Julia using CUDA.jl. I basically learned CUDA programming by experimenting in Julia with the help of LLMs.




Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: