Cuda.jl v3.3: union types, debug info, graph APIs(juliagpu.org) |
Cuda.jl v3.3: union types, debug info, graph APIs(juliagpu.org) |
[1] https://rocmdocs.amd.com/en/latest/index.html
As AMDGPU.jl's maintainer, I do certainly appreciate more users using AMDGPU.jl if they have the ability to, but I don't want people to think that it's anywhere close in terms of maturity, overall performance, and feature-richness compared to CUDA.jl. If you already have access to an NVIDIA GPU, it's painless to setup and should work really well for basically anything you want to with it. I can't say the same about AMDGPU.jl right now (although we are definitely getting there).
Julia has all the tools required to "magically" transform code into SPIR-V kernels. Couldn't `:()` syntax be used to create kernels?
If the competition to CUDA wants to be taken serioulsy then provide the tooling and polyglot support to do so.
OpenCL was stuck in their "C only with printf debugging" mentality for too long, now it is too late.
AMD ROCm still isn't available in Windows.
If I learned anything from wasting my time with Khronos stuff, was that I should have switched to DirectX much earlier.
https://github.com/JuliaGPU/GPUCompiler.jl/ https://github.com/tshort/StaticCompiler.jl/
(Before someone complains, you can also opt out of this and direct the library to a version you installed yourself)
Edit: the site has one project per GPU type, shame there isn't one interface that works with every GPU type instead.
https://github.com/JuliaGPU/oneAPI.jl
These are both less mature than CUDA.jl, but are in active development.
> Edit: the site has one project per GPU type, shame there isn't one interface that works with every GPU type instead.
That would be https://juliagpu.github.io/KernelAbstractions.jl
However, the low-level library for AMD GPUs is more of an alpha quality in Julia.
1) You're using a container-ish environment where the host kernel has the CUDA drivers installed anyway (but your base container image probably doesn't have the userspace libraries)
2) The kernel driver comes with your OS distribution, but the userspace libraries are outdated (userspace libraries here includes things like JIT compilers, which have lots of bugs and need frequent updates) or don't have some of the optional components that have restrictive redistribution clauses
3) Your sysadmin installed everything, but then helpfully moved the CUDA libraries into some obscure system specific directory where no software can find it.
4) You need to install the kernel driver yourself, so you find it on the NVIDIA website, but don't realize there's another 5 separate installers you need for all the optional libraries.
5) Maybe you have the NVIDIA-provided libraries, but then you need to figure out how to get the third-party libraries that depend on them installed. Given the variety of ways to install CUDA, this is a pretty hard problem to solve for other ecosystems.
In Julia, as long as you have the kernel driver, everything else will get automatically set up and installed for you. As a result, people are usually up and running with GPUs in a few minutes in Julia.
[0] https://www.tensorflow.org/install/gpu [1] https://cuda.juliagpu.org/stable/installation/overview/