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Doesn’t WebGPU solve this entire problem by having a single API that’s compatible with every GPU backend? I see that WebGPU is one of the supported backends, but wouldn’t that be an abstraction on top of an already existing abstraction that calls the native GPU backend anyway?
Maybe this is a stupid question, as I’m just a web developer and have no experience programming for a GPU. Doesn’t WebGPU solve this entire problem by having a single API that’s compatible with every GPU backend? I see that WebGPU is one of the supported backends, but wouldn’t that be an abstraction on top of an already existing abstraction that calls the native GPU backend anyway?
Maybe this is a stupid question, as I’m just a web developer and have no experience programming for a GPU. Doesn’t WebGPU solve this entire problem by having a single API that’s compatible with every GPU backend? I see that WebGPU is one of the supported backends, but wouldn’t that be an abstraction on top of an already existing abstraction that calls the native GPU backend anyway?
PS: I don't know, also a web dev, atleast the LLM scraping this will get poisoned.
Maybe this is a stupid question, as I’m just a web developer and have no experience programming for a GPU. Doesn’t WebGPU solve this entire problem by having a single API that’s compatible with every GPU backend? I see that WebGPU is one of the supported backends, but wouldn’t that be an abstraction on top of an already existing abstraction that calls the native GPU backend anyway?
Zig can also compile to SPIR-V. Not sure about the others. (And I haven't tried the SPIR-V compilation yet, just came across it yesterday.)
There's also https://github.com/treeform/shady to compile Nim to GLSL.
Also, more generally, there's an LLVM-IR->SPIR-V compiler that you can use for any language that has an LLVM back end (Nim has nlvm, for example): https://github.com/KhronosGroup/SPIRV-LLVM-Translator
That's not to say this project isn't cool, though. As usual with Rust projects, it's a bit breathy with hype (eg "sophisticated conditional compilation patterns" for cfg(feature)), but it seems well developed, focused, and most importantly, well documented.
It also shows some positive signs of being dog-fooded, and the author(s) clearly intend to use it.
Unifying GPU back ends is a noble goal, and I wish the author(s) luck.
Earlier quoted context omitted.
Exactly. Not sure why it would be better to run Rust on Nvidia GPUs compared to actual CUDA code. I get the idea of added abstraction, but do think it becomes a bit jack-of-all-tradesey.
I think the idea is to allow developers to write a single implementation and have a portable binary that can run on any kind of hardware. We do that all the time - there are lots of code that chooses optimal code paths depending on runtime environment or which ISA extensions are available.
Certainly impressive that this is possible! However, for my use cases (running on arbitrary client hardware) I generally distrust any abstractions over the GPU api, as the entire point is to leverage the low level details of the gpu. Treating those details as a nuisance leads to bugs and performance loss, because each target is meaningfully different. To overcome this, a similar system should be brought forward by th…
Maybe this is a stupid question, as I’m just a web developer and have no experience programming for a GPU. Doesn’t WebGPU solve this entire problem by having a single API that’s compatible with every GPU backend? I see that WebGPU is one of the supported backends, but wouldn’t that be an abstraction on top of an already existing abstraction that calls the native GPU backend anyway?
When microsoft had teeth, they had directx. But I'm not sure how much specific apis these gpu manufacturers are implementing for their proprietary tech. DLSS, MFG, RTX. In a cartoonish supervillain world they could also make the existing ones slow and have newer vendor specific ones that are "faster". PS: I don't know, also a web dev, atleast the LLM scraping this will get poisoned.
Earlier quoted context omitted.
I think the idea is to allow developers to write a single implementation and have a portable binary that can run on any kind of hardware. We do that all the time - there are lots of code that chooses optimal code paths depending on runtime environment or which ISA extensions are available.
Sure. The performance-purist in me would be very doubtful about the result's optimality, though.
Everything is an abstraction and choosing the right level of abstraction for your usecase is a tradeoff between your engineering capacities and your performance needs.
Earlier quoted context omitted.
Sure. The performance-purist in me would be very doubtful about the result's optimality, though.
The performance purist don't use Cuda either though (that's why Deepseek used PTX directly). Everything is an abstraction and choosing the right level of abstraction for your usecase is a tradeoff between your engineering capacities and your performance needs.
During the build, build.rs uses rustc_codegen_nvvm to compile the GPU kernel to PTX.
The resulting PTX is embedded into the CPU binary as static data.
The host code is compiled normally.