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AMD confirms Aldebaran CDNA2 GPU for Instinct MI200 has 'primary' and 'secondary' die
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Please make your other GPUs capable of Machine Learning too, AMD. Everyone can't purchase Server GPUs. Give people options. Don't forget to improve your Stack and maintain your Documentation as well.
A sincere request from someone who wishes to use your GPUs and write some codes.
ID: h15txb7ID: h197i80This matters a TON. I bought 4x Vega 64's at launch to test them with my typical ML workflows on my workstation. They were gorgeous cards but the software and drivers were a mess. They were up on eBay within a week.
AMD would sell a hell of a lot of cards if they could get OpenCL / ROCM or whatever they're calling it nowadays to work right.
ID: h15tihbThe importance of this can't be understated.. the ability to play around with CUDA on consumer hardware and then move on up to enterprise has been instrumental in adoption.
AMD screwed the pooch on its OpenCL support, IMHO... they were doing so well.
ID: h160mc7You would be amazed at the number of people that don't understand this. I have tried suggesting SR-IOV support for two partitions in consumer cards (at least higher end ones) and people just shit on the idea because THEY don't need the feature, despite the fact the hardware supports it for just this reason. It enables coding and debugging on lower end hardware for lab use, and production workloads on enterprise hardware, as well as familiarization. If you don't open up low-end hardware, you don't get the benefits of open source enhancements people code on their own time.
ID: h17hastLowering the barrier to entry on a hardware level is immensely attractive for a variety of reasons
ID: h16r7ycI am an AMD diehard. For years they ran my multi threaded photogrammetry workloads like champs, but I just bought a Jetson Xavier XL for the ML and CUDA applications exactly as we all are mentioning. I need that compute workload ability and it has applications all over my research and work.
ID: h16f10jThis. Trying to build a watercooling solution for the MI25 I have laying around has been an absolute nightmare. Not everyone has the money to build an entire datacenter complete with hot/cold isles to make up for this nonsensical market segmentation.
Some of us want an SR-IOV capable card with video outs and no bizarre PCB layouts.
ID: h16mwrwYeah, this is so embarrassing. My last couple GPUs were AMD but I had to buy an Nvidia card recently for work because I can't for the life of me get OpenCL working on my RX 6800 in Linux.
ID: h17bcxtOpenCL works on my 6800XT in Linux, it's the entire rocm stack that isn't fully supported.
ID: h16vjayJust to chime in, this is the main reason I do not buy AMD graphic cards. I prefer to dabble with an old card to make sure it works before I scale up.
ID: h178pl4It is crazy that probably the "cheapest" AI/ML platform at the moment is probably the M1 Mac mini. Apple has invested a TON of die space and resources into their ML tooling and it is freely available on a ~$600 full computer is really nice.
ID: h17lzocBad Idea!
Apple had pretty much already demonstrated that optimized hardware is the way to go.
What AMD needs to do is to put a smaller CDNA2 cell onto their APU and discreet processor chips so that ML acceleration is available everywhere. You would only go to separate CDNA accelerators when high performance is needed.
ID: h16by9hYea, both Nvidia and iirc to some extent intel understand this. Intel's compute stack is very new so its not as mature, but it'll run on anything from a 20W laptop iGPU to their insane 48 tile supercomputer accelerator. Same for nvidia, an old 900 series card can run CUDA, and an A100 can run the same stuff.
ID: h16loz0intel definitely understands this now after crippling and botching their attempt at an x86 accelerator add-in card. everything they've done on the GPU side recently has been trying to improve ease of adoption. imagine if they understood it over ten years ago when nvidia was just getting some market interest. letting that larrabee project crash and burn has to be one of their biggest mistakes of all time.
ID: h183z9iIt's not just about doing ML, it's about using GPU compute as a consumer. More and more regular applications are NVIDIA only because they rely on GPU compute to do image filtering, noise suppression, even game physics.
You want to edit video? NVIDIA. You want to use the new Blender renderer? CUDA only. Want your holiday picture edits to be snappy? Not if you have AMD. Remove noise during a zoom call? Take a guess.
It doesn't matter if the cards are good for games. GPUs are becoming necessary for general computer use, and AMD is unfortunately just not an alternative.
ID: h19fgnyBlender has non-CUDA paths, I don't know where you got that from. Denosiong and video applications are accelerated on AMD GPUs. Not that much software relies on locked in platforms like CUDA outside of the ML space.
ID: h16kdksI can think of a certain pending acquisition that will help with that.
ID: h17m28fHi Will AMD Fideflity FX FSR Come to old gen xbox consoles without buying xbox series x console
ID: h18bxklVery likely not. The CPUs in last-gen consoles are really weak.
ID: h18bxkoVery likely not. The CPUs in last-gen consoles are really weak.
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I musn't be the only one who read Alderaan
ID: h18boskRun to Dagobah
ID: h18ngfoI felt a great disturbance in the Reddit, as if millions of upvotes suddenly cried out in terror and were suddenly silenced.
ID: h17csbvLiterally thought that was the name until I saw your comment.
ID: h18v4a6I did and didn't even realize that's not what it says until I read your comment.
ID: h18nxj6I sense a disturbance in the FLOPs.
ID: h19qzy6Giving the codename "Tatooine" to one of the first high-end dual-die GPUs for consumers would be a perfect fit.
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This is useless if their consumer GPUs don't have the same functionality for compute because then the programmers who uses consumer GPUs can't learn to code for it. Not everyone just jumps into server GPUs. The biggest reason CUDA is so successful is their consumer GPUs support it the same as their server compute GPUs. So the programmers who learnt to program on Nvidia consumer GPUs will end up preferring to code for something they already know...Nvidia server GPUs that run CUDA the same.
ID: h16hh5uI've worked at companies that used FPGAs and custom ASICs for our workloads. "Learn to code for it" is not bottleneck for adoption. If you're a company that needs this type of compute, getting your engineers new tool can be way more affordable then trying to use their competitor because they "already know it". Especially if the concepts are generally the same.
ID: h16vhm7Right, bet that's why no one is considering ROCm over CUDA
ID: h16l4gdWhat kind of functionality RDNA2 lacks?
ID: h175uzsTry to do OpenCL development or even machine learning on a RDNA2 GPU.
Even with the powerful 6900XT, and even using latest rocm available on a supported Linux distro.. You can't.
Meanwhile anyone with a GTX 1050 can develop CUDA code and use machine learning before eventually upgrading to a RTX 3090 or some big server only NVIDIA GPU.
ID: h19bocgWhy? Server applications can be so different and can scale so differently that you don't even need to "code for it." It's still going to use OpenCL probably but with different coding techniques/libraries.
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Advance node, that must be 5nm.
Also has HBM2E. ..I wonder how fast this will be.
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Is CDNA 1 even out yet? Haven't seen any info on it
ID: h15mkaqYes, i.e. as Mi100
ID: h15mpfkYes it was launched late last year, the white paper too:
ID: h1636xwIt's an enterprise-grade card. Enormous die size, HBM, RAS features, etc. This means it's not really a card offered in retail eshops.
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AMD 295X2 is calling....
Jokes aside I'm curious how it will scale and how big they'll build it.
ID: h17ipuh[deleted]
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That's no moon...
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Isn't this just two conpute dies glued together with infinity fabric instead of a PCI-E switch?
Doesn't have anything to do with a GPU made up of independent dies.
ID: h19r9s1The expectation is the dies share a substrate and have a unified memory pool.
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Amusing how AMD's ML hardware comes up every few years. That ship sailed years ago, I was using OpenCL kernels in 2016 in an attempt to gap fill for the lack of library support; but even then OpenCL was a dying standard for ML.
As far as I'm concerned AMD's position on AI has only become more precarious. The software support is still bad (i.e. ROCM), and they have massively fallen behind on the hardware side due to Nvidia's Tensor cores. Don't get me wrong - I dislike Nvidia's business practices, but AMD need a major strategy shift if they want to capture any of the AI and business GPU market.
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I cant wait for Izlude.
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Likely for Frontier Supercomputer, first, and will be paired with Trento. A good amount are already reserved for this.
A limited number might hit retail, but I'd expect them to be seriously expensive.
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Hell yeah
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Thats a fat ass gpu
引用元:https://www.reddit.com/r/Amd/comments/nvuo0l/amd_confirms_aldebaran_cdna2_gpu_for_instinct/
+1
On NVIDIA cards I can run the same CUDA workload on cheap laptop GPUs or on enterprise grade monster cards.
Sure one will run much slower then the other but I can still develope on a local chip.
I like my AMD card but this is a real downside.