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Comment by Calavar | Hacker News Reader

I've seen a few projects along the lines of shader programming in C++, shader programming in Rust, etc., but I'm not sure that I understand the point. There's a huge impedence mismatch between CPU and GPU, and if you port CPU centric code to GPU naively, it's easy to get code that slower than the CPU version thanks to the leaky abstraction. And I'm not sure you can argue pareto principle: Because if you had a scenario where 80% of the code is not performance sensitive, why would you port it to GPU in the first place?

Anyway, there's a good chance that I'm missing something here because there seems to be a lot of interest in writing shaders in CPU centric languages.

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Sometimes, even if you know you're starting with somewhat suboptimal performance, the ability to use CPU code you've already written and tested on the GPU is very valuable.

Many years ago (approx 2011-2012) my own introduction to CUDA came by way of a neat .NET library Cudafy that allowed you to annotate certain methods in your C# code for GPU execution. Obviously the subset of C# that could be supported was quite small, but it was "the same" code you could use elsewhere, so you could test (slowly) the nominal correctness of your code on CPU first. Even now the GPU tooling/debugging is not as good, and back then it was way worse, so being able to debug/test nearly identical code on CPU first was a big help. Of course sometimes the abstraction broke down and you ended up having to look at the generated CUDA source, but that was pretty rare.

This was many years ago, after Unity released mathematics and burst. I was porting (part of) my CPU toy pathtracer to a compute shader. At one point, I literally just copy-pasted chunks of my CPU code straight into an HLSL file, fully expecting it to throw some syntax errors or need tweaks. But nope. It ran perfectly, no changes needed. It felt kinda magical and made me realize I could actually debug stuff on the CPU first, then move it over to the GPU with almost zero hassle.

For folks who don't know: Unity.Mathematics is a package that ships a low-level math library whose types (`float2`, `float3`, `float4`, `int4x4`, etc.) are a 1-to-1 mirror of HLSL's built-in vector and matrix types. Because the syntax, swizzling, and operators are identical, any pure-math function you write in C# compiles under Burst to SIMD-friendly machine code on the CPU and can be dropped into a `.hlsl` file with almost zero edits for the GPU.

It's very common to write c++ in a way that will work well for GPUs. Consider that CUDA, the most used GPU language, is just a set of extensions on top of c++. Likewise for Metal shaders, or high-level dogs synthesis systems like Vitis

high-level.. dogs?

I’m going to guess that they meant Directed Acyclic Graphs or DAGs, which is a useful way to represent data dependencies and transformations, allowing formulation for GPU, CPU, NNA, DSP, FPGA, etc.

If the macrostructure of the operations can be represented appropriately, automatic platform-specific optimization is more approachable.

yes, dogs. very high level, best-of-the-best. the elite. directed ocyclic graphs

The goodest boys.

I'm pretty sure he meant dawgs. Directed acyclic woof graphs.

wops! FPGAs*

People keep repeating this wrongly.

CUDA is a polyglot development stack for compute, with first party support for C, C++, Fortran, Python JIT DSL, and anything PTX. With the hardware semantics, nowadays following the C++ memory model, although it wasn't originally designed that way.

As NVidia blessed extensions for compiler backends targeting PTX, there are Haskell, .NET, Java, Julia tooling.

For whatever reason, all of that keeps being forgotten and only either C or C++ gets a mention, which is the same mistake Intel and AMD keep doing on the CUDA porting kits.

> CPU centric languages.

What does a "GPU centric language" look like?

The most commonly used languages in terms of GPU:

- CUDA: C++ like

- OpenCL: C like

- HLSL/GLSL: C like

To add to this list, Apple has MSL, which uses a subset of C++

Annoyingly, everything is converging to C++-ish via Slang now that DirectX supports SPIR-V.

OpenCL and GLSL might as well be dead given the vast difference in development resources between them and HLSL/Slang. Slang is effectively HLSL++.

Metal is the main odd man out, but is C++-like.

Slang is inspired in C#, , beyond the HLSL common subset, whereas HLSL is moving more towards C++ feature.

The module system, generics and operators definitions.

CUDA is full-on C++20. The trick is learning how to write C++ that works with the hardware instead of against it.

Minus modules though.

C++ is "C like" and uses manual memory management. The major idiom is RAII, which is based on deterministic destructor execution.

Java is "C like" and uses garbage collection for dynamic memory management. It doesn't have determistic destructors. The major idiom is inheritance and overriding virtual methods.

GLSL is "C like" and doesn’t even support dynamic memory allocation, manual or otherwise. The major idiom is an implicit fixed function pipeline that executes around your code - you don't write the whole program.

So what does "C like" actually mean? IMHO it refers to superficial syntax elements like curly braces, return type before the function name, prefix and postfix increment operators, etc. It tells you almost nothing about the semantics, which is the part that determines how code in that language will map to CPU machine code vs. a GPU IR like SPIR-V. For example, CUDA is based on C++ but it has to introduce a new memory model to match the realities of GPU silicon.

What is the main difference in shading languages vs. programming languages such as C++?

Metal Shading Language for example uses a subset of C++, and HLSL and GLSL are C-like languages.

In my view, it is nice to have an equivalent syntax and language for both CPU and GPU code, even though you still want to write simple code for GPU compute kernels and shaders.

I would expect a shading language to provide specialized features for working with GPU resources and intrinsic operations.

The language extensions for GPU semantics and code distribution required in C and C++.

The difference is that shader languages have a specific set of semantics, while the former still have to worry about ISO standard semantics, coupled with the extensions and broken expectations when the code takes another execution semantics from what a regular C or C++ developer would expect.

>> There's a huge impedence mismatch between CPU and GPU

That's already been worked out to some extent with libraries such as Aparapi, although you still need to know what you're doing, and to actually need it.

https://aparapi.github.io/

Aparapi allows Java developers to take advantage of the compute power of GPU and APU devices by executing data parallel code fragments on the GPU rather than being confined to the local CPU. It does this by converting Java bytecode to OpenCL at runtime and executing on the GPU, if for any reason Aparapi can't execute on the GPU it will execute in a Java thread pool.

The value isn't in porting CPU-centric code, but in shared abstractions, tooling, and language familiarity that reduce context switching costs when developing across the CPU/GPU boundary.

From my perspective I just want better DevEx.

C++ DevEx is significantly better than ISF despite them looking very similar and it seems like less of a hurdle to get C++ to spit out an ISF compatible file than it is to build all the tools for ISF (and GLSL, HLSL, WGSL)

I haven't done a lot of shader programming, just modified stuff occasionally.

But one thing I miss in C++ compared to shaders is all the vector sizzling, like v.yxyx. I couldn't really see how they handle vectors but might have missed it.

There are libraries for that, like GLM.

Unfortunately GLM doesn't use SIMD instructions.

I really wanted something that's compatible with shaders and fast so we can quickly swap between CPU and GPU because it was time consuming to port the code.

I've been down this road before. If you aren't doing SIMD it's pretty easy to implement but relies on UB that works on all the compilers I tried (C++ properties would make this better and portable). I got something working with SIMD that unfortunately doesn't compile correctly on Clang!

But GLM does have support for SIMD [1] or do you mean that it doesn't support specific instructions under SIMD?

[1] https://glm.g-truc.net/0.9.1/api/a00285.html

Totally missed that, thanks! I don't know the library very well, but as far as I can tell they don't support .xyxy style sizzling with the SIMD Vec4 type.

They're using the same "proxy object" method I was doing for their sizzling which I'm pretty sure won't work with SIMD types but would love to be proven wrong!

I haven't deep dived into the library as I'm no longer doing this kind of code.

One answer is simply that the tooling is better: test frameworks, linters, LSPs, even just including other files and syntax highlighting are better.

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