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Comment by eigenspace | Hacker News Reader

Most of the julia GPU stuff is being made vendor agnostic via moving infrastructure from CUDA.jl to CPUCompiler.jl. The package AMDGPU.jl is coming along very well and will be plug-able with all this stuff.

https://github.com/JuliaGPU/GPUCompiler.jl

https://github.com/JuliaGPU/AMDGPU.jl

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Tim has been working on making it easy to use CUDA.jl and AMDGPU.jl pretty interchangeably through GPUArrays.jl, and this approach seems to be pretty extensible to other accelerators like Intel's dGPUs. KernelAbstractions.jl will also be gaining AMDGPU.jl support soon, so it'll be easy to write generic kernels without buying into a single vendor's cards.

Also, oneAPI.jl: https://github.com/JuliaGPU/oneAPI.jl

So we're freed from GPU vendor lock-in by getting a programming language lock-in.

Out of the frying pan, into the fire. Great.

What do you mean? What is programming language lock-in, and how is that worse than (or even comparable to) vendor lock-in? What's your goal?

I do not get what you are trying to say. Someone is developing an open-source solution that lets you use a vast array of different numerical accelerators more efficiently and with simpler code. All the while the work is done in the open and you can copy any subset if you want to make your own tool. Why is any of this bad?

What is programming language lock-in?

When any code is written in some language?

Maybe the issue raised is that libraries/APIs that define an ABI (Application Binary Interface) can be used from multiple programming languages that share calling conventions. So a question is about how Julialang libraries can be called from other programming languages. The "Embedding Julia" chapter of the manual says that from C there is an interface layer to lookup function objects, box parameters, and call.

I don't think there is anything in our programming world that is programming language agnostic, except for theory.

As opposed to being trapped in the C++ CUDA API? Julia isn’t that much harder to learn than Python. Does anyone really think the hard part of say, deep learning, is in learning Python or PyTorch?

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