I mostly agree regarding GL's programming model, but tbf some problems have been fixed by WebGL2 (much better error validation and error reporting - e.g. very helpful error messages right on the browser console, and it's probably the least buggy and most portable 3D API implementation of them all, including the current state of WebGPU.

For teaching I would generally use the web browser via WebGL2 or WebGPU, just because those APIs require much less boilerplate in the browser environment to get to your first triangle and the edit-test-loop is pretty much instant.

The thing that makes it "hard" is that it's all indirect. You compile a program for the GPU (a shader). Then you have to setup data (textures and buffers) for it. Then, you need to spend a bunch of time telling the GPU how to use that data, offsets where each piece of data is, what formats the data is in,, how to advance to the next piece of data, etc.. etc..

There are a few libraries that try to abstract over all this indirection but all the graphics APIs themselves are lower-level so you're left to do all of that yourself.

https://docs.swmansion.com/TypeGPU/

https://github.com/google/toucan

It's mostly hardware design limitations. GPU hardware is way more diverse than CPU hardware, so you need to distribute shaders as either source code or a portable intermediate representation, in both cases compiled at runtime for the user's actual GPU. There are also many fixed function parts of the graphics pipeline with wildly different behavior on different hardware which you need to abstract over. And it all runs in parallel with the CPU, many times with a separate pool of RAM, so you need explicit synchronization and data transfer which is always complex.

If you really want to see why all the cruft is there, and how much you could remove if you dropped support for older hardware, look no further than Sebastian Altonen's "No Graphics API" blog post and associated implementation: https://www.sebastianaaltonen.com/blog/no-graphics-api/

part of the reason is historical cruft & history path dependency. GPUs didn't start as general computing device for parallelism but a device for the specific purpose of accelerating graphics with very specific fixed functions for that job and only that. with time that fixed pipeline became more complex and more configurable, not fixed/more flexible and hacks were in parallel used to make use of it for tasks outside graphics those hacks were later made into something more formal (CUDA/OpenCL).

CUDA being the winner (closed source, not really usable on non-nvidia GPUs) between those 2 and nvidia not wanting to open their competitive advantage to the competition for the niche of non gamings uses / scientific computing (and later AI, which became a huge win for nvidia).

vulkan came around after as this open thing (based on a previous attempt by AMD called mantle) that's trying to break down the leaky abstractions and the divide between language for graphics & video games and separate language/frameworks for gpu non-graphic computing with the promise of giving more control and more room for squeezing performance from the hardware at the cost of more complexity with the idea that you'll probably build your own abstractions on top of it for your subset of applications.

If GPU were from the get go designed as a parallelism tool first and video game tools second the hardware architecture and the language would probably be different than today and simpler (and if the competition between the duopoly didn't sabotage things)

don't forget that the drivers for GPUs would until recently get updates to patch specific video games for performance issues (not sure if they still do that ?)

It's not more complicated, you have things like CUDA which is basically C/C++ that compiles to GPU.

But GPUs have thousands of cores, which means if you want any benefit, the smallest data type you must use is something like array[n * 1024], or your GPU is wasted doing nothing. So you must shape your programs into that memory model or you're just wasting your time (and GPU).

That's >50% of the problem.

It's partially historical; I'm sure if we redesigned GPGPU today from the ground up, we'd have an entirely different pipeline. And partially due to hardware vendor lock-in; you can't really design an alternative to Vulkan/d3d12 even if you had the skill and drive to do so.

But also partly in the fact that the GPU is specialized for certain operations over a CPU. It wants and need to obtain data in a different way from a CPU to maximize throughput. So language constructs are designed around making sure developers stay on this fast path. The complexity can be seen as some intentional gatekeeping to signal that this kind of programming is only for specific kinds of problems.

> but I wish there was a middle ground between it and VK

There's WebGPU (probably the best option for teaching), D3D11 and Metal.

And (shameless plug): sokol-gfx tries to find a balance between simplicity and modern 3D APIs:

https://github.com/floooh/sokol

Demos with links to C source and shader code:

WebGL2 backend: https://floooh.github.io/sokol-html5/

WebGPU backend (has a couple more demos which use features not available on GLES3/WebGL2, like compute shaders): https://floooh.github.io/sokol-webgpu/

That middle ground is often called DirectX 11 but sadly it's stuck to a specific platform.

Disagree. Trying to express all of graphics and rendering through the lens of OpenGL will of course fail and doing 3D rendering itself via raytracing is simply not possible in rasterization focused OpenGL without dirty workarounds. That is obvious.

A fundamental shift in thinking that got me into high performance graphics programming and parallelized image processing, the "Aha" Moment, is being forced to express how to calculate the final pixel independently, without having access to neighboring pixels of the same output buffer, confined to the straight jacket of the fragment shader. OpenGL with it's combination of hand-holding and relative simplicity as compared to Vulkan and limits of a predefined shader pipeline as compared to a pure software implementation is exactly the thing that I needed to get into graphics programming. It's an excellent jump in point I think.

I think it's because of the webgl demo at top stealing focus