yea, i wrote this blogpost rather to show how to use scan in different ways than the canonical example of calculating prefix sum of a vector shown in introductions on gpu programming.
It doesn’t. The batch size is just 8. This is a very good trick and often needed to archive peak performance in memory bound kernels. You can checkout the equivalent code in cuda aswell :)
That was exactly my reason to write this blogpost and optimise transpose. It is a simple educational yet not trivial example to learn the basics.
The kernels I mention in CUDA use all the equivalent logic like the Mojo kernels. You can find them on my GitHub: https://github.com/simveit/effective_transpose You may want to provide a faster kernel on H100 via PR and…
yea, i wrote this blogpost rather to show how to use scan in different ways than the canonical example of calculating prefix sum of a vector shown in introductions on gpu programming.
It doesn’t. The batch size is just 8. This is a very good trick and often needed to archive peak performance in memory bound kernels. You can checkout the equivalent code in cuda aswell :)
That was exactly my reason to write this blogpost and optimise transpose. It is a simple educational yet not trivial example to learn the basics.
The kernels I mention in CUDA use all the equivalent logic like the Mojo kernels. You can find them on my GitHub: https://github.com/simveit/effective_transpose You may want to provide a faster kernel on H100 via PR and…