I think the contrast is probably less about the language, and more about the scope and objective of the projects. 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/...
I gave numba CUDA a spin in late 2018 and was severely disappointed. It didn't work out of the box, I had to tweak the source to remove a reference to an API that had been removed from CUDA more than a year prior (and deprecated long ago). Then I ran into a bug when converting a float array to a double array -- I had to declare the types three different times and it still did a naive byte-copy rather than a conversion. Thanks to a background in numerics, the symptoms were obvious, but yikes. The problem that finally did us in was an inability to get buffers to correctly pass between kernels without a CPU copy, which was absolutely critical for our perf. I think this was supported in theory but just didn't work.
In any case, we did a complete rewrite in CUDA proper in less time than we spent banging our heads against that last numba-CUDA issue.
Under every language bridge there are trolls and numba-CUDA had some mean ones. Hopefully things have gotten better but I'm definitely still inside the "once bitten twice shy" period.
In any case, we did a complete rewrite in CUDA proper in less time than we spent banging our heads against that last numba-CUDA issue.
Under every language bridge there are trolls and numba-CUDA had some mean ones. Hopefully things have gotten better but I'm definitely still inside the "once bitten twice shy" period.