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I mostly agree regarding GL's programming model, but tbf some problems have been fixed by WebGL2 (much better 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.

WebGPU will however always suffer from being an API designed for the Web, and targeting 2015 thereabouts hardware.

Better than OpenGL, but don't expect that much improvements coming into their way, other than what the Chrome team might feel like pushing, as I doubt Firefox or Safari teams have much left to say about where it goes beyond version 1.0.

Oh my I totally forgot about NeHe! I probably still have some of the lessons printed out at my parents house somewhere.

Good times.

Yep, can confirm.

The tutorials and hands on coding examples made me understand how to program with GPUs. I loved them because at the time, khronos was still in its infancy and just started out with GLES extensions and the whole WebGL ecosystem.

Meanwhile I kind of believe that learning where it came from (fixed function pipeline, buffer/texture and uniform problems etc) makes a lot of sense to learn first so you start thinking in shader computation pipelines much easier, as you understand the whys and whens much better.

> carry on to Vulkan/WGPU with background story that there is also DX12 and Metal that follow similar principles

Except that's a minefield all of its own. DirectX 12 is its own special beast that is unlike the others. Vulkan is already mired in 3 revisions of architecture changes. WGPU is based on the (outdated) first cut of Vulkan, not the current model. Of all of these, only Metal has a reasonable on-ramp for newcomers to 3D graphics.

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, often with a separate pool of RAM, so you need implicit or explicit synchronization and data transfer which adds a lot of complexity especially in error cases.

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 Aaltonen's "No Graphics API" blog post (which he actually implemented too): https://www.sebastianaaltonen.com/blog/no-graphics-api/

I think most of it comes down to 'historical accidents' tbh.

E.g. if CPUs would have closed ISAs and heavily guarded IPs, you'd also need a 'driver' and an 'OpenCPU API' to make any use of them (of course the public ISA of a CPU is essentially the driver interface baked into the hardware).

The other big difference is focus on decent single-thread performance for CPUs versus performing thousands of small tasks in parallel on GPUs, but each one slowly. This difference causes a different programming model (e.g. running an entire application in a single 'main' function from start to finish, versus spawning small specialized tasks which are running across hundreds of threads).

And the specific problem with Vulkan is that it tried to solve two conflicting requirements: on one hand being an explicit low-level API without 'magic abstractions', on the other hand supporting very different GPU architectures (especially the mobile GPU zoo). The resulting mess was quite predictable to be honest...

If you care about every last bit of performance, CPUs are difficult to program for as well, possibly more difficult than a GPU.

Generally GPUs punish you more by bad performance if you don't optimise your code.

One thing that helped me understand it was to reframe it as a network application, where you have a client running on the CPU and a server running on the GPU (the shaders).

A bunch of the complexity comes from having to write both client and server, and the communication protocol between them, then some more comes from the client being responsible for spinning up the server. As with every network service, client/server traffic is the slowest part of the architecture, so a bunch of complexity then comes from having to minimise the amount of network calls (or draw calls, in this case).

Then you hit the part where all of this is performance critical (soft-realtime requirements), so the whole thing is geared towards “writing your own userspace IP stack in C++” rather than “writing a flask application in Python”.

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 ?)

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 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 GLES3.0/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