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Should have (2017) in the title.
Indeed cool to program julia directly on the GPU and Julia on GPU and this has further evolved since then, see https://juliagpu.org/
Unfortunately, I don't see a "just a bit of magic here without learning much of anything new" interface coming because it's all about strategizing the movement of data. This is not unique to GPUs. It's a universal problem across computing hardware. It's just enabled to be explicit in OpenCL/CUDA. As compared to most languages where you try to steer things the right way and the CPU does it's best with whatever mess it gets.
Closest I know of is https://halide-lang.org/ And, that is specialized around images.
I cannot overstate the importance of using a programming language targeting GPUs directly like Futhark (https://github.com/diku-dk/futhark). In this case, it is a functional, declarative language where you can focus on the why, not the how. Just like CPUs are incredibly complex, higher level abstractions are very important.
If you were a pro GPU programmer and had 10 years, Futhark would be maybe 10x slower. But just like we do not program in assembly when making critically fast software, most non-simple things are easier written in this.
>Higher level abstractions
like these?
https://github.com/JuliaGPU/KernelAbstractions.jl
