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  • Hacker News
  • I don't have that many kidneys
    by lostmsu
  • I guess I submitted the link at the wrong time or something.
    by pixelpoet
  • An apt title. To get to the sun, you need to slow down from orbital speed to zero. And then you fall in. Do they run CUDA yet or is this yet more impressive but unusable hardware?

    AMD never misses a chance to miss a chance.

    by inigyou
  • Duplicate post:

    https://news.ycombinator.com/item?id=49025398 (13 hours earlier)

    by neogodless
  • > The basic GCN microarchitecture underpinned every single one of AMD’s compute accelerators for nearly 15 years [...]. With CDNA5 AMD has moved over to a microarchitecture that is based on the RDNA series putting a bookend to the long-lived line that was the GCN microarchitecture.

    So everything is new again. Will this be another "buy now, have ROCM work in 3 maybe years if you are lucky"?

    by thyristan
  • Not anymore: https://rocm.blogs.amd.com/software-tools-optimization/spir-...
    by nolist_policy
  • Arguably this might reduce it, because now ROCm won't be prioritised for the GCN-based systems?
    by p_l
  • At least going forward, with both AMD's AI accelerators and Consumer GPUs using the same RDNA based IP, there will be less reason for AMD to drop RDNA GPUs from the ROCm/HIP support matrix! And so with both MI series accelerators and consumer GPUs/Graphics all using the same basic RDNA Micro-Architecture the ROCm/HIP code base developed for "CDNA5"/later will work for consumer RDNA as well with minor changes required! Polaris and Vega GPUs/Graphics both have been dropped from the ROCm/HIP support matrix. But with AMD's "The Rock" software stack making use of SPIR-V in the same manner as Nvidia/PTX there made be little issues getting that to work with older Vega/Polaris GPUs. even if AMD's not validated/verified that software stack with Consumer Vega/Earlier Consumer GPUs and Integrated Graphics. AMD's biggest issue has been dropping its older GPU micro-architectures from the ROCm/HIP support matrix too soon, and just look at Blender 3D's HIP Back End that requires RDNA2/Later GPUs and Graphics for any Radeon iGPU/dGPU Accelerated Blender 3D Cycles rendering support!
    by Farfignoggen
  • 432 GB of RAM per chip. Tens of terabytes per rack. Clients queuing up to buy them.

    RAM prices are not coming down any time soon.

    by dist-epoch
  • but you have to rewrite all your software to ROCm. And ROCm, to this day, still sucks.

    ZLUDA basically crash on high memory demand, and only accounted for ~70% of CUDA API coverage (and it is still buggy).

    But hey, at least it does run on Rust-CUDA. I'm one of the few who ported it and fixed a few bugs on ZLUDA. I used it to run a simple SHA256 kernel and it ran sure, but I gave it up because of those fundamental problems on AMD GPUs. You can't believe how messy ROCm is. I wonder if Vulkan compute kernel using SPIR-V would be a better choice.

    by stevefan1999
  • Hey, they are only projected to cost about $5.5million a rack. Pocket change, really
    by swiftcoder
  • Did anyone think RAM prices were coming down soon?
    by nl
  • It's mind blowing seeing these multi exaflop single rack systems.

    The world's first exaflop supercomputer was Frontier. It was launched only 4 years ago in 2022.

    It's not a fair comparison of course. FP4 in Helios barely qualifies as floating point. Frontier was proper fp64, 16 times the bit width and probably 256x as many transistors.

    All the same just wow. Much compute.

    by fancyfredbot
  • Workloads did change over time. Back when we were first approaching practical exascale, the dominant workload for a supercomputer was thought to be physics simulations - and they often benefit from high numerical precision.

    Now, the dominant compute-hungry workload is AI, where precision takes second place to the independent parameter count. To the point that the capacity of BF16, which were originally designed as a radical optimization for AI workloads, is sometimes considered wasteful now.

    AI workloads have some truly peculiar and counterintuitive properties - the kind of things you might expect to see in biology instead of conventional computing. Intrinsic error tolerance, for one. It did necessitate some rethinking and reprioritization, and I'm not quite sure if we converged to the general shape of an "optimal" AI accelerator as of yet.

    by ACCount37