FlashAttention-2, 2x faster than FlashAttention (twitter.com)

Tri Dao and Tim Dettmers ftw

I'd start with the example of implementing the fastest reduction you possibly can. Pretty much all complexity in every kernel used in ML extends from this concept (reductions with addition).

https://developer.download.nvidia.com/assets/cuda/files/redu...

If you’d like a practical goal, you probably want to learn PyTorch and have a little background knowledge of the memory architecture of the GPUs. If you want to go deep, learn CUDA: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index....

I found it helpful to start with CUDA on numba since it lets you write GPU kernels in python. Assuming you're like most ML engineers and you're more familiar with python than C++, this allows you to separately learn CUDA concepts from also learning C++ at the same time. There's also a set of GPU puzzles for beginners [1] using to get started with numba CUDA.

[1] https://github.com/srush/GPU-Puzzles

Why couldn't it be applied to SD?