numba VS gtc2017-numba

that project might be abandoned but this strategy is used in nvidia and nvidia adjacent projects (through llvm):

https://github.com/rapidsai/cudf/blob/branch-0.20/python/cud...

https://github.com/gmarkall/numba/blob/master/numba/cuda/com...

>but we also need high level expressibility that doesn't require writing kernels in C

the above are possible because C is actually just a frontend to PTX

https://docs.nvidia.com/cuda/parallel-thread-execution/index...

fundamentally you are not going to ever be able to have a way to write cuda kernels without thinking about cuda architecture anymore so than you'll ever be able to write async code without thinking about concurrency.

here is writing a similar kernel in python with numba: https://github.com/ContinuumIO/gtc2017-numba/blob/master/4%2...

I think the contrast is less about the language, and more about the scope and objective of the project. the blog is describing low-level interfaces in python - probably more comparable is the old CUDAdrv.jl package (now merged into CUDA.jl): https://github.com/JuliaGPU/CUDAdrv.jl/blob/master/examples/...