CUDA for AMD on Windows
github.com
github.com
It's unbearable that most LLM inference happens on closed H/W, closed drivers, and closed SDKs.
I would really appreciate your input!
1. Top end model on high/xhigh thinking (last time I did it it was Sol xhigh I think)
2. Make sure it creates some representative fixtures of different sizes and sets up a good testing, profiling and benchmarking loop that doesn't require my input.
3. Make sure it has access to reference implementation code
Edit: Oh and one obvious pitfall that for some reason I still have to remind even smart models of from time to time: make sure it knows not to try to parallelize its benchmark runs. I've occasionally had an agent struggle to figure out absolutely nonsensical data because it tried to run multiple tests on the same compute hardware simultaneously.
Can you tell us what kind of gain you got ie. what was the speed bump
Keep in mind though that this is also sometimes use case dependent. Off the shelf implementations are generally pretty good overall, but can have pathological behavior on specific workload shapes you care about. So do this as a somewhat later optimization, and particularly when you see performance characteristics that don’t seem to make any sense.
The moment LunarG no longer gets sponsored it will be back to "here is a standard, all the best, the community will provide the tools".
On the SYSCL front, that is mostly driven by Intel, and their own OpenAI tooling, there are hardly any offerings for SYCL from anyone else. Thus it is really only pseudo-open.
In that regard, the Vulkan SDK is definitely an improvement.
However using Vulkan on Android is actually hardly any different, given that there is no SDK as such, only the libs and headers get dumped into the NDK, there is a github with samples from Google and that's it.
So bad that they failed to convince anyone to drop OpenGL ES from Java/Kotlin, and use NDK alongside C or C++ for Vulkan, thus now there is WebGPU implementation for Java and Kotlin consumption.
I do agree with the Vulkan code itself, related extension spaghetti, and overall state of drivers, which has become visible enough to have been part of Vulkanised talks for two years in a row now.
My software is labeled "CPU only unless using an nVidia GPU". I would prefer to strikethrough "nVidia". Incidentally, this means no more Mac support.
Even Carmack later admitted that DirectX had become much better than OpenGL, but they weren't going to change given the studio culture.
See https://www.bit-tech.net/news/gaming/pc/carmack-directx-bett...
Few problems I noticed:
* coopmat2 from nvidia is the classic embrace, extend, extinguish. No point to ask the models to translate from CUDA to coopmat2. Instead, the models can understand the existing CUDA and CPU kernels, and adapt them accordingly to non-nvidia devices.
* However, the standard API is also lacking. The models struggled to make prompt processing compute-bound on strix halo when the graph is complex. Upfront standard API might just be an evolution dead end.
coopmat2 features will eventually be rolled elsewhere. coopmat also started as an NVIDIA extension.
The client use cases that coopmat was intended for are customer machines, not multi-GPU, which is broadly seen as a datacenter feature instead. That said coopmat orthogonal to this.
VK_KHR_cooperative_matrix - embrace?
VK_NV_cooperative_matrix2 - extend?
I am pretty sure VkImportSemaphoreFdInfoKHR, mentioned in https://github.com/ggml-org/llama.cpp/issues/22648, works across multiple AMD devices, but somehow doesn't work across multiple nvidia devices.
p2p is disabled on nvidia customer cards, vulkan device groups are shipped for the RTX 6000s
> Added support for creating Vulkan logical devices from multiple physical devices on select cards via VK_KHR_device_group_creation. This feature can be enabled by setting the environment variable __VK_ENABLE_DEVICE_GROUPS=1.
* manual pipelining
* shared memory staging
* tiling
* bounds checking
It's likely that CUDA will continue dominating until they put aside their differences. The current MLX/MPS/ROCm ecosystems are too fractured to threaten Nvidia.
Intel uses SPIRV iirc
They're migrating away from SPIR-V to their own, Intel PISA: https://discourse.llvm.org/t/rfc-upstreaming-the-pisa-backen...
Vulkan's SPIR-V dialect is substantially different from the OpenCL one, notably with the former having structured control flow. They're incompatible between each other.
The basic underlying tech can be agreed on, but Apple/AMD/Intel all have different GPU priorities that limit their ability to agree on a CUDA-adjacent hardware platform.
Intel missed for a different reason - they didn't invest in the product space at all for decades. In terms of units they had the most market share (millions of Intel Integrated Graphics chips), but it wasn't seen as important. Bare minimum to render Windows and Office UI, nothing more.
Intel (via Codeplay) was handing it out on a silver platter - Nvidia on SYCL, full top chain, and people still wouldn’t want it.
By holistic, I mean hardware architecture cooperation. Nvidia can hold onto their lead forever if GPU designers fight over what a GPGPU hardware baseline looks like. The current ecosystem fragmentation is not competitive, and future fragmentation probably wouldn't work either. I think the fastest way to kill Nvidia would be a hardware consortium.
What would you expect the hardware consortium to coordinate on? Unified ISA?
Yes they have implementations on top of CUDA but they're maintained by... Intel. They didn't get buy-in for cross-vendor collaboration
Source: worked at a bunch of accelerator companies in the kernels or equivalent team. They're nothing alike.
Early graphics APIs ran on GPUs that were just about as different form one another as that.
We could easily figure it out, were it not for the trillion dollars incentives to not figure it out.
My thinking was rather that most of our current programming languages put memory layout fully into the programmer’s responsibility - I can think off hand of a language where the compiler makes performance decisions like whether your structure are SoA, AoS or SoAoS, what alignment, padding, strides and float types to use.
Automatic decisions about when to use cooperative loads through shared local mem versus gathers from global mem and hardware caches are also something that such a hypothetical compiler would have to make.
As for ai and matrix vector performance... I mean matrices are extremely useful across many domains. The hands off language that exists today is called blas. That's fine but won't lead to the best perf on any GPU today.
SoA and AoS data layouts are not even a worthwhile point of contention. Same with shared v global mem. Today's hardware has specialized memory depending on the operation. The hardware on these processors is so specialized as to make anything but first class support for the feature be completely pointless. If you look at Nvidia code that's open source even they will special case a lot of their chips. Literally if you want the best perf you write the kernel exactly for the exact chip. That's intra vendor .. you can only imagine inter vendor
Sucks like important libraries like Alphafold are locked into CUDA. Its ridiculous for researchers.
the source language has never been the moat. Nvidia sells to hyperscalers. Hyperscalers have armies of kernel authors who have no issue translating shaders by hand (or now with claude). Nvidia's moat is (and will remain for the foreseeable future) the entire stack. you cannot fathom the pain and misery of working on literally any other stack. if you've never debugged a GPU synchronization error or kernel panic due to some GPU firmware bug or fought absolute shit profilers hunting for perf you really have no idea what you're talking about.
EDIT: i can't believe this really requires saying but graphics and compute are not the same domain at all. if you work in graphics for GPU but not compute then you are still way out of your depth commenting. to wit: graphics people do not (and cannot) write CUDA kernels/shaders.
You can simply disagree with me, regardless of my experience (or lack thereof).
that's exactly what i did and made an argument for why i think you're wrong. in response you provided exactly zero substantive remarks other than "i've written shaders" and then accused me of pissing.
also FYI it's clear from your profile that you've only worked on graphics (embree, blender, etc) and not compute. so i'll repeat: you're an outsider and you have absolutely no idea what you're talking about.
“if you've never debugged a GPU synchronization error or kernel panic due to some GPU firmware bug or fought absolute shit profilers hunting for perf”
I have done all of those things. As part of my full time job, for years.
Now that we’ve put all of that aside, can we stop talking about me and go back to discussing moats? What do you think are top three things that are holding customers back from buying AMD GPUs instead of Nvidia GPUs?
I could be misremembering, but I think Jensen Huang himself once called CUDA or the CUDA ecosystem their moat, and it certainly seems to be accepted narrative in the tech press. They may be wrong there, and you sharing your first hand experience here would be helpful to many of us readers here.
My 7900XTX is supported, I run local models via rocm all the time recently, mostly to play with/experiment on, Vulcan works as well and for some models works better (or the trade offs are better for that use case).
Their mistake was simply not picking and going all in earlier, they let nvidia become the defacto standard without even contesting it on both the hardware side and software side and that’s a hard though not impossible comeback to make.
Long term I think they’ll catch up in capability if not market share because simply too much money on the table not to.
https://rocm.docs.amd.com/_/downloads/radeon-ryzen/en/docs-6...
Not useful. A crap NVIDIA card can get you started with CUDA. You have to reach students. Without students, there's no future
“graphics people do not (and cannot) write CUDA kernels/shaders” is just not true at all. All it would take to verify that would be things like reading the introduction of the OptiX documentation, a small sample of SIGGRAPH GPU papers or the Blender/Cycles source code.
ZLUDA is already doing that, no?
I personally had to put all my toy projects into hiatus to be able to keep up with life. Maybe if things get a little lighter, we can thaw them or start working on a couple of interesting ones which I stumble on.
It's niche, performance and code-quality sensitive, needs deep and wide knowledge, and hardware dependent.
ZLUDA is JIT (it's working with the binary, not the source, which usually means you've lost some information) and it doesn't support CDNA, which is a showstopper for enterprises, tho it's a great project and it's awesome seeing it work on Windows (I wonder whether MS Defender complains about it due to library injection? IIRC, it used to. That'd be another enterprise showstopper)
Someone here mentioned scale-lang.com already as a portability tool - it also beats HIP in the performance game, on average. scale-lang.com/s/discord is where its community lives.
disclaimer: I am part of SCALE's team, Spectral Compute
This gives me strong "Bash on Ubuntu on Windows" kind of vibes
AMD GPUs build for AI specs for reference: https://flopper.io/gpus?vendor=AMD&page=1
The FP16 throughput on RDNA1 is both shader reliant and requires everything to be packed first. Even with 2 or 4 or 1000 cards, you would be consuming all of the available memory and memory bandwidth just packing and unpacking values, and if you really want to dump a hundred billion tokens into making it work anyways, you're only going to find out that even if you bother to sit there ferrying packed values to ram or disk before then issuing the instructions, paying that already severe penalty again when the values then have to be unpacked is so steep of a cost that the 256 BF16 flops/cu/clock's effective throughput is outright lower than simply doing it on a Zen 2 processor. You also don't have INT8 (or really INT4) on RDNA1 so the other RNS/CRT tricks aren't viable.
Sadly RDNA1's VCN2 also lacks actually good x264 bframe encoding support, or even P010 for 10 bit color, so what I'm saying is you should sell them. Used Radeon VII's are like $260, you'll go a lot further with those especially if you throw in a 7900XTX, and then augment that further with a 9070 CRE (you only want it for its int8 cores), and of course 128GB of ram.
E: And sure, that's 3, or ideally 4 GPUs, and a good bit of extra work. But that gets you up to more than halfway to the naive performance of a $15,000 MI300x in a surprising amount of cases, with additional strengths that it lacks. For far less than half of the cost
But to reason in other direction, unless you absolutely need cards right now, you could throw that ~$1440 in a 6 month CD and let the 4.5% pay for the tax or shipping on a 10090 XT or whatever RDNA 5 flagship when those drop in about as much time. If it lands anywhere close to what the rumors are indicating it should be a fucking monster.
- what exactly is CUDA?
- is it patented hardware tech that AMD cannot replicate?
- is it a library that ll also work on AMD while it currently works on nvidia
- can AMD cook an equivalent of CUDA, if so why havent they done it?
At the botton you have the PTX bytecode.
Followed by (from NVidia) compilers for C, C++, Fortran, Python JIT and more recently Julia JIT.
Additionally there are third party support for .NET, Java, Haskell tooling to also target CUDA.
Then you have the libraries ready to plug into the official NVidia compilers to reduce the need to explicitly write CUDA code manually.
Finally you have the graphical debugging and profiling tools that make targeting GPUs as easy as regular CPU programming, ignoring the hardware differences for a moment.
All wannabe replacements fail, because they focus only on the CUDA C++ compiler and nothing else.
I’ve been working on a Windows setup that lets CUDA-targeted applications run on AMD GPUs using ZLUDA + ROCm/HIP.
Repo: https://github.com/Speedstu/CUDA-for-AMD-Windows
So far, it has only been tested on my RX 9060 XT (gfx1200), where I’ve used it with CUDA-enabled LibTorch workloads, including long ai training and use.
I also added a GPU scanner / auto-detection system that detects:
AMD GPU model
gfxXXXX architecture
ROCm/HIP installation
driver info
whether the GPU has already been validated by the project
Example:
RX 9060 XT → gfx1200 → RDNA4 → HIP detected → validated
The goal now is to test it on more hardware, especially RX 6000 / 7000 / 9000 cards.
If you have an AMD GPU on Windows and want to try it, I’d really appreciate compatibility reports working or broken. There’s a dedicated GPU compatibility issue template in the repo.
If this is useful to you, a star would also help the project get more testers.