Optimizing Datalog for the GPU

Curious, why use cuda and hip? These frameworks are rather opinionated about kernel design, they seem suboptimal for implementing a language runtime when SPIR-V is right there, particularly in the case of datalog.

Why is cuda sub-optimal compared to SPIR-V? I don't think I know the internals enough to understand if it's supposed to be obvious why one is better than the other.

I'm currently sitting and learning cuda for ML purposes, so happy to get more educated :)

Just depends on how the manufacturer of the GPU handles code written in different languages. For example, what level of API access, what level of abstraction, and how is the source compiled i.e. how optimized is it. For example, on an apple GPU, you’ll see benchmarks that openCL and metal can vary depending on the tasks.