Will be interesting to see if Nvidia and other have any interest & energy getting this used by others, if there actually is an ecosystem forming around it.
Google leading XLA & IREE, with awesome intermediate representations, used by lots of hardware platforms, and backing really excellent Jax & Pytorch implementations, having tools for layout & optinization folks can share: they really build an amazing community.
There's still so much room for planning/scheduling, so much hardware we have yet to target. RISC-V has really interesting vector instructions, for example, and it seems like there's so much exploration / work to do to better leverage that.
Nvidia has partners everywhere now. Nvlink is used by Intel, AWS Tritanium, others. Yesterday the Groq exclusive license that Nvidia paid to give to Groq?! Seeing how and when CUDA Tiles emerges: will be interesting. Moving from fabric partnerships, up up up the stack.
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.
I work at Nvidia, and my team is using Slang for all of our (numerous and non-trivial) kernels because its automatic differentiation type system is so nice.
Comments
Will be interesting to see if Nvidia and other have any interest & energy getting this used by others, if there actually is an ecosystem forming around it.
Google leading XLA & IREE, with awesome intermediate representations, used by lots of hardware platforms, and backing really excellent Jax & Pytorch implementations, having tools for layout & optinization folks can share: they really build an amazing community.
There's still so much room for planning/scheduling, so much hardware we have yet to target. RISC-V has really interesting vector instructions, for example, and it seems like there's so much exploration / work to do to better leverage that.
Nvidia has partners everywhere now. Nvlink is used by Intel, AWS Tritanium, others. Yesterday the Groq exclusive license that Nvidia paid to give to Groq?! Seeing how and when CUDA Tiles emerges: will be interesting. Moving from fabric partnerships, up up up the stack.
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...
Slang is a fantastic developer experience.
I work at Nvidia, and my team is using Slang for all of our (numerous and non-trivial) kernels because its automatic differentiation type system is so nice.
Especially when using the tooling from who created it, before offering it to Khronos as GLSL replacement, NVIDIA.
Nsight does not have a debugger.
What do you mean? Are you unaware of Nsight VSE? https://developer.nvidia.com/nsight-visual-studio-edition
I was aware of their Visual Studio plugins but I did not know that they called their debugger support for Visual Studio “Nsight” as well.
Yes it does, apparently you never used it.
this is nicely illustrated by this recent article:
https://news.ycombinator.com/item?id=46366998
Wrong type of scheduling.
Thanks for correcting me. Can you point me to what I need to search for to understand the differences?
https://en.wikipedia.org/wiki/Instruction_scheduling
On the RISC-V vector instructions, could you elaborate? Are the vector extensions substantially different from those in ARM or x86?
it's fairly similar to Arm's sve2, but very different from the x86 side in that the instructions are variable length rather than fixed
non-exclusive license actually.
IREE hasn't been at G for >2 years.