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It would help if they were not so much macOS and Linux focused. Julia, Python GPU JITs work great on Windows, and many people only get Windows systems as default at work.
Approximately nobody writing high performance code for AI training is using Windows. Why should they target it?
When is the Year of NPUs on Linux?
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It would help if they were not so much macOS and Linux focused. Julia, Python GPU JITs work great on Windows, and many people only get Windows systems as default at work.
I've commissioned a board of MENSA members to devise a workaround for this issue; they've identified two potential solutions. 1) Install Linux 2) Summon Chris Lattner to play you a sad song on the world's smallest violin in honor of the Windows devs that refuse to install WSL.
What about that outcome?
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For NVidia it suffices this is a Python JIT allowing programming CUDA compute kernels directly in Python instead of C++, yet another way how Intel and AMD, alongside Khronos APIs, lag behind in great developer experiences for GPU compute programming. Ah, and Nsight debugging also supports Python CUDA Tiles debugging. https://developer.nvidia.com/blog/simplify-gpu-programming-w...
Nsight does not have a debugger.
Let's see if developers sleepwalk into another trap to keep us locked into nvidia's hardware for the next decade.
They have had about 15 years to move beyond C99, stone age workflows to compile GLSL and C99 with their drivers, no libraries ecosystem, and printf debugging.
Eventually some of the issues have been fixed, after they started seeing only hardliners would put with such development experience, and then it was too late.
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Isn't the compiler still closed source? I and many other ML devs have no interest in a closed-source compiler. We have enough proprietary things from NVIDIA.
Yes, but Latner said multiple time it's closed until it matures (he apparently did this with llvm and swift too). So not unusal. His open source target is end of 2026. In all fairness, I have 0 doubts that he would deliver.
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Use-cases like this are why Mojo isn't used in production, ever. What does Nvidia gain from switching to a proprietary frontend for a compiler backend they're already using? It's a legal headache. Second-rate libraries like OpenCL had industry buy-in because they were open . They went through standards committees and cooperated with the rest of the industry (even Nvidia) to hear-out everyone's needs. Lattner gave up…
Kernels now written in Mojo were all in hand written in MLIR like in this repo. They made a full language because that's not scalable, a sane language is totally worth it. Nvidia will probably end up buying them in a few years.
CUDA Tile was exactly designed to give parity to Python in writing CUDA kernels, acknowledging the relevance of Python, while offering a path researchers don't need to mess with C++.
It was announced at this years GTC.
NVidia has no reason to use Mojo.
Let's see if developers sleepwalk into another trap to keep us locked into nvidia's hardware for the next decade.
Let's see if developers sleepwalk into another trap to keep us locked into nvidia's hardware for the next decade.
CUDA Tile is an open source MLIR Dialect so it wouldn't take much to write MLIR transforms to map it from the Tile IR to TOSA or gpu + vector + some amdgpu or other specialty dialects. The Tile dialect is pretty much independent of the nvidia ecosystem so all it takes is one good set of MLIR transform passes to run anything on the CUDA stack that compiles to tile out of the nvidia ecosystem prison. So if anything thi…
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That's the whole problem. I had to "R" multiple "FMs" before one of them bothered to define the acronym. Stop carrying water for poor documentation practice.
Use the AI prompt to pinprick learn. Just say to the AI, "Explain THIS".
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Nsight does not have a debugger.
What do you mean? Are you unaware of Nsight VSE? https://developer.nvidia.com/nsight-visual-studio-edition