Perhaps any new language targetting GPU acceleration would consider TILE based concept and primitive recently supported by major GPU vendors including Nvidia [1],[2],[3],[4].
For more generic GPU targets there's TRITON [5],[6].
[1] NVIDIA CUDA 13.1 Powers Next-Gen GPU Programming with NVIDIA CUDA Tile and Performance Gains:
It uses the same trick as Go [1]. The grammar has semicolons, but the tokeniser silently inserts them for ease of use. I think quite a few languages do it now
The latency point matters more than it looks imo like the GPU work isn't just async CPU work at a different speed, the cost model is completely different. In LLM inference, the hard scheduling problem is batching non-uniform requests where prompt lengths and generation lengths vary, and treating that like normal thread scheduling leads to terrible utilization. Would be curious if Eyot has anything to say about non-uniform work units.
Not right now, it is far too early days. I'm currently working through bugs, and missing stdlib, to get a simple backpropagation network efficient. Once I'm happy with that I'd like to move onto more complex models.
Not similar in the way of "Decorate any function and now it's a thread on the GPU", but Candle been pretty neat for experimenting with ML on Rust, and easy to move things between CPU and GPU, more of a library than a DSL though: https://github.com/huggingface/candle
I hate doing self-promotion, but this is basically exactly what CubeCL does. CubeCL is a bit more limited because as a proc macro we can't see any real type info, but it's the closest thing I'm aware of. Other solutions need a bunch of boilerplate and custom (nightly-only) compiler backends.
This reminds me that I'd love to see SYCL get more love. Right now, out of the computer hardware manufacturers, it seems that only Intel is putting any effort into it.
CUDA having had such a wide moat for so long has completely warped the GPU software ecosystem. There just isn't any incentive for Nvidia to meaningfully contribute to any external, standards-driven effort like SYCL or OpenCL. Real shame because it leads to a tonne of duplicated effort as AMD and Intel try to reimplement the exact same libraries as Nvidia (and usually worse because neither seem to prioritise good software for whatever reason).
Every time someone does something with threading and makes it a language feature it always seems like it could just be done with stock C++.
Whatever this is doing could be wrapped up in another language.
Either way it's arguable that is even a good idea, since dealing with a regular thread in the same memory space, getting data to and from the GPU and doing computations on the GPU are all completely separate and have different latency characteristics.
For more generic GPU targets there's TRITON [5],[6].
[1] NVIDIA CUDA 13.1 Powers Next-Gen GPU Programming with NVIDIA CUDA Tile and Performance Gains:
https://developer.nvidia.com/blog/nvidia-cuda-13-1-powers-ne...
[2] Nvidia Tilus: A Tile-Level GPU Kernel Programming Language:
https://github.com/NVIDIA/tilus
[3] Simplify GPU Programming with NVIDIA CUDA Tile in Python:
https://developer.nvidia.com/blog/simplify-gpu-programming-w...
[4] Tile Language:
https://github.com/tile-ai/tilelang
[5] Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations:
https://dl.acm.org/doi/10.1145/3315508.3329973
[6] Triton:
https://github.com/triton-lang/triton
[1] https://go.dev/doc/effective_go#semicolons
https://wgpu.rs
Whatever this is doing could be wrapped up in another language.
Either way it's arguable that is even a good idea, since dealing with a regular thread in the same memory space, getting data to and from the GPU and doing computations on the GPU are all completely separate and have different latency characteristics.