I will give you personal experience learning CUDA that might be helpful.
Disclaime: I don't claim that this is actually a systematic way to learn it and it is more for academic work.
I got assigned to a project that needs learning CUDA as part of my PhD. There was no one in my research group who have any experience or know CUDA. I started with standard NVIDIA courses (Getting Started with Accelerated Computing with CUDA C/C++ and there is python version too).
This gave me good introduction to the concepts and basic ideas but I think after that I did most of learning by trial and error. I tried a couple of online tutorials for specific things and some books but it was always a deprecated function there or here or a change of API that make things obsolete. Or basically things changed for your GPU and now you have to be careful because yoy might be using GPU version not compatible with what I develop for in production and you need things to work for both.
I think learning CUDA for me is an endeavor of pain and going through "compute-sanitizer" and Nsight because you will find that most of your time will go into debugging why things is running slower than you think.
Take things slowly. Take a simple project that you know how to do without CUDA then port it to CUDA ane benchmark against CPU and try to optimize different aspect of it.
The one advice that can be helpful is not to think about optimization at the beginning. Start with correct, then optimize. A working slow kernel beats a fast kernel that corrupts memory.
Disclaime: I don't claim that this is actually a systematic way to learn it and it is more for academic work.
I got assigned to a project that needs learning CUDA as part of my PhD. There was no one in my research group who have any experience or know CUDA. I started with standard NVIDIA courses (Getting Started with Accelerated Computing with CUDA C/C++ and there is python version too).
This gave me good introduction to the concepts and basic ideas but I think after that I did most of learning by trial and error. I tried a couple of online tutorials for specific things and some books but it was always a deprecated function there or here or a change of API that make things obsolete. Or basically things changed for your GPU and now you have to be careful because yoy might be using GPU version not compatible with what I develop for in production and you need things to work for both.
I think learning CUDA for me is an endeavor of pain and going through "compute-sanitizer" and Nsight because you will find that most of your time will go into debugging why things is running slower than you think.
Take things slowly. Take a simple project that you know how to do without CUDA then port it to CUDA ane benchmark against CPU and try to optimize different aspect of it.
The one advice that can be helpful is not to think about optimization at the beginning. Start with correct, then optimize. A working slow kernel beats a fast kernel that corrupts memory.