I've learned more graphics writing software rasterizers and ray tracers than using any APIs. If the goal is to teach GPU rendering specifically, then OpenGL is indeed more amenable to teaching than the others. But if the goal is to teach graphics and rendering concepts, then OpenGL et al are just miserable and counter-productive.
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.
Nice breakdown. I've been wondering why it's so complicated to program on the GPU compared to the CPU. Is it a language paradigm issue or maybe a hardware design limitation? I always think of this when looking at the graphic APIs, espeically Vulkan.
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).
Right, that makes sense. There's a ton of overhead in the setup though, I'm assuming that's because everything runs on the CPU by default. Is this then an OS design choice? Thanks for your input.
There can be overhead, though GPU memory + drivers are some of the most optimized software in the world.
Some OS/hardware have separate GPU memory (Nvidia), some have unified memory (Macos, modern consoles), but moving memory is usually not the problem.
Things don't "run on CPU by default", it's up to the programmer where to run it. But the CPU and GPU paradigm of "running a program" are very different, since on GPU you're running SIMD (i.e. running your code on arrays) or else there is no point to use a GPU.
GLES isn't a bad way to teach graphics but I wish there was a middle ground between it and VK.
The worst part of GL is the mysterious global state you will never understand without studying the spec. The world could use an API that's basically GL but without global hidden singleton (per thread!) BS behind the scenes.
I've learned more graphics writing software rasterizers and ray tracers than using any APIs. If the goal is to teach GPU rendering specifically, then OpenGL is indeed more amenable to teaching than the others. But if the goal is to teach graphics and rendering concepts, then OpenGL et al are just miserable and counter-productive.
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.
Nice breakdown. I've been wondering why it's so complicated to program on the GPU compared to the CPU. Is it a language paradigm issue or maybe a hardware design limitation? I always think of this when looking at the graphic APIs, espeically Vulkan.
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.
Right, that makes sense. There's a ton of overhead in the setup though, I'm assuming that's because everything runs on the CPU by default. Is this then an OS design choice? Thanks for your input.
There can be overhead, though GPU memory + drivers are some of the most optimized software in the world.
Some OS/hardware have separate GPU memory (Nvidia), some have unified memory (Macos, modern consoles), but moving memory is usually not the problem.
Things don't "run on CPU by default", it's up to the programmer where to run it. But the CPU and GPU paradigm of "running a program" are very different, since on GPU you're running SIMD (i.e. running your code on arrays) or else there is no point to use a GPU.
GLES isn't a bad way to teach graphics but I wish there was a middle ground between it and VK.
The worst part of GL is the mysterious global state you will never understand without studying the spec. The world could use an API that's basically GL but without global hidden singleton (per thread!) BS behind the scenes.
Sounds like a great way to make sure students understand fundamental concepts.