They really don't, no. Vulkan: 50 lines to allocate device memory. Cuda: One single line. What kind of extensive documentation stack do you want for functionality that is trivial in Cuda? And that exact issue continues through every little step of the way to your first usable application. I know there is VMA, it is a very poor solution to a problem that shouldn't even exist, and it only poorly addresses one of 100 parts of the API where Cuda is vastly simpler than Vulkan. Cuda also doesnt force you to use queue families but you can optionally use streams. No ridiculous descriptor management and binding in cuda, just passing pointers and handles via launch arguments. No overengineered explicit syncing mechanis in cuda, everything is nicely implicitly synced until you explicitly opt in to parallel streams. etc.
A couple of years ago I evaluated both Vulkan and Cuda as a choice for future projects. I couldnt get anything done after a week in Vulkan, but had the test prototype project working after just a day in Cuda.
Needless to say, I'd never ever pick Vulkan for any project after that experience. It's just way to needlessly overengineered and bloated.
> Ease of programming and a giant leap in performance is one of the key reasons for the CUDA platform’s widespread adoption
This, so much. Other platforms continue to ignore developer UX, but it's one of the main things that get's new users onboard and keeps old users around.
> unimportant implementation detail that should be assessed on performance alone rather than usability
I disagree. Usability is important, or you end up with garbage like Vulkan. I'd rather have 99% of the performance for 100% usability, than 100% of the performance for 5% of the usability.
Unfortunately when it comes to graphics APIs, the choices are between bad and worse, which is why I switched to doing real time rendering in CUDA. Easier to write a fast software rasterizer in cuda, than to draw a single triangle in Vulkan
Having participated in many studies, I lost much faith in studies. You could study the same thing, and get opposite results depending on how you build the study, which people participate (friends and colleagues will be reluctant to speak against your results), and the bias with which you set everything up. Also, many tasks require learning the tools, and some tools will start to be more productive with expertise than others.
That was an interesting read. Also enjoyed reading about the semaphores in the default stream. It's great that cuda implicitly handles syncing of commands for users and makes parallel commands optional and opt-in via streams, unlike Vulkan which completely unloads the full complexity of syncing to users right from the start.
I like the driver API because it allows treating Cuda kernels like hot-reloadable shaders. It's fun to develop while being able to change the code at runtime.
I haven't tried the latest Codex but I switched from GPT to Claude because I think Claude writes much better Code. GPT's code ends up way more verbose/complex/overengineered than it needs to be.
I've always considered shared_ptr to be semi-garbage collection. Allows me to code C++ almost as if it were Java so long as circular references are avoided. I'm perfectly fine with it being considered a type of garbage collection.
You're skillfully dodging the point: Trilinear filtering is in no way an "ideal" alternative.
It has worse quality than something like a Lanczos filter, and it requires computing image pyramids first, i.e., it is also slower for the very common use case of rescaling images just once. And that article isn't really about projected/distorted textures, where trilinear filtering actually makes sense.
Trilinear requires an image pyramid. Without downsampling to create that image pyramid, you can't even do trilinear sampling, so your argument strikes me as odd and circular. Like telling developers of APIs such as ID3D11DeviceContext.GenerateMips to simply use ID3D11DeviceContext.GenerateMips instead of developing ID3D11DeviceContext.GenerateMips. Also, I never took this article to be about 3D rendering and utilizing mip maps for trilinear interpolation. More about 2D image scaling.
Have you never downscaled and upscaled images in a non-3D-rendering context?
Sure is, but there is nothing stopping AMD or Intel from building a working alternative to CUDA, so how is it anti-competitive? The problem with OpenCL, Sycl, ROC, etc. is that the developer experience is terrible. Cumbersome to set up, difficiult to get working accross platforms, lack of major quality of life features, etc.
The magic is that CUDA actually works well. There is no reason to pick OpenCL, ROCm, Sycl or others if you get a 10x better developer experience with CUDA.
OpenCL is an alternative to CUDA just like Legos are an alternative to bricks. The problem with OpenCL isn't even the performance, it's everything. If OpenCL were any good, people could use it to build similarly powerful applications on cheaper AMD GPUs.
It's currently unparalleled when it comes to realism as in realistic 3D reconstruction from the real world. Photogrammetry only really works for nice surfacic data, whereas gaussian splats work for semi-volumetric data such as fur, vegetation, particles, rough surfaces, and also for glossy/specular surfaces and volumes with strong subdivision surface properties, or generally stuff with materials that are strongly view-dependent.
Immunosuppressants are very commonly used for various diseases that are caused by overreactions or undesired reactions of your immune system. Glucocorticoids, for example, are very widespread for all sorts of stuff (rashes, asthma, allergies, inflammations, ...). Monoclonal antibodies are also getting popular as a way to treat allergic reactions by means such as "killing" your IgE antibodies (They're basically antibodies that, in this instance, are used against your own body's antibodies).
WebGL has been around for more than a decade and didn't turn out to be a security issue, other than occasionally crashing tabs. Neither will WebGPU be.
I very much do want that since the WebGPU API is far easier and nicer to use than Vulkan or OpenGL. Also, it makes apps much more accessible to distribute them over web, and it is much more secure to use web apps than native apps. Unfortunately WebGPU is way too limited compared to desktop APIs.
Not really, that is not the problem of WebGPU. The worst you can do is crash the tab. With an unstable graphics driver, there might even be the option to crash the system but that's hardly a security issue, only an annoyance.
Apart from the examples given by the other user: 64 bit integers and their atomics which are the bread and butter of efficient software rasterizers such as Nanite, or for point clouds which can be rendered multiple times faster with 64 bit atomics compared to using the "point-list" primitive; subgroup operations; bindless; sparse buffers; printf; timestamps inside shaders; async memcpy and copy without the necessity for an intermediate copy buffer and so much more. One of the worst is that they're adding limitations of all languages, but not the workarounds that may exist in them. Like WebGPU actively prohibits buffer aliasing or mixing atomic and non-atomic access to memory because of Apple's Metal Shading Language, but Metal supports it via workarounds! I mean... seriously? That actually makes WebGPU even worse than the lowest common denominator.
One of the turning points for the worse was the introduction of WGSL. Before WGSL, you could do lots of stuff with spirv shaders because they weren't artifically limited. But with WGSL, they went all in on turning WebGPU into a toy-language that only supports whatever last decades mobile phones support. I was really hopeful for WebGPU because UX-wise, it is so much better than anything else. Far better than Vulkan or OpenGL. But feature-wise, WebGPU so limited that I had to go back to desktop OpenGL.
In one way WebGPU really has become a true successor to WebGL though - it is a graphics API that is outdated on arrival.
OP is right. WebGPU is targeted towards the lowest common denominator, which is fairly old mobile phones. It therefore doesn't support modern features and is basically an outdated graphics API by design.
I gave up on Vulkan simply because it's too overengineered and cumbersome. WebGPU, on the other hand, is relatively easy to use, even though it is build on many of the same concepts.
"Using a pointer in a shader - In Vulkan GLSL, there is the GL_EXT_buffer_reference extension "
That extension is utter garbage. I tried it. It was the last thing I tried before giving up on GLSL/Vulkan and switching to CUDA. It was the nail in the coffin that made me go "okay, if that's the best Vulkan can do, then I need to switch to CUDA". It's incredibly cumbersome, confusing and verbose.
What's needed are regular, simple, C-like pointers.
AMD/Intel/Khronos are free to compete with something better. NVIDIA isn't preventing them from doing so. And in that regard, CUDA is a massive benefit to consumers, because the alternatives are really bad.
CUDA is just way to good in terms of UX with nothing platform-independent coming close to it. I wish there was a competitor, but there simply isn't.
I wish AMD&Intel would extend compute shaders with pointers&pointer casting, arbitrary large buffers instead of just 4GB, device-wide sync, and function pointers. Those are kinda my must-have functionality. Even better, just use C++ for compute shaders.