3 comments

  • doctorpangloss 23 minutes ago
    What is the use case? Okay, ultra low latency streaming. That is good. But. If you are sending the frames via some protocol over the network, like WebRTC, it will be touching the CPU anyway. Software encoding of 4K h264 is real time on a single thread on 65w, decade old CPUs, with low latency. The CPU encoders are much better quality and more flexible. So it's very difficult to justify the level of complexity needed for hardware video encoding. Absolutely no need for it for TV streaming for example. But people keep being obsessed with it who have no need for it.

    IMO vendors should stop reinventing hardware video encoding and instead assign the programmer time to making libwebrtc and libvpx better suit their particular use case.

    • chillfox 8 minutes ago
      The article explains it. This is not for streaming over the web, but for editing professional grade video on consumer hardware.
    • pandaforce 6 minutes ago
      The article explicitly mentions that mainstream codecs like H264 are not the target. This is for very high bitrate high resolution professional codecs.
    • jpc0 14 minutes ago
      I'm not entirely sure that this is true.

      I haven't actually looked into this but it might not be the realm of possibility. But you are generating a frame on GPU, if you can also encode it there, either with nvenc or vulkan doesn't matter. Then DMA the to the nic while just using the CPU to process the packet headers, assuming that cannot also be handled in the GPU/nic

    • eptcyka 20 minutes ago
      It will be more energy efficient. And the CPU is free to jit half a gig of javascript in the mean time.
      • temp0826 8 minutes ago
        It's hugely more efficient, if you're on a battery powered device it could mean hours more of play time. It's pretty insane just how much better it is (I go through a bit of extra effort to make sure it's working for me, hw decoding isn't includes in some distros).
    • xattt 18 minutes ago
      It’s a leftover mindset from the mid-2000s when GPGPU became possible, and additional performance was “unlocked” from an otherwise under-utilized silicon.
  • sylware 1 hour ago
    Well, the problem with hardware decoding is it cannot handle all the variations in data corruption which results in hardware crash, sometimes not recoverable with a soft reset of the hardware block.

    It is usually more reasonable to work with software decoders for really complex formats, or only to accelerate some heavy parts of the decoding where data corruption is really easy to deal with or benign, or aim for the middle ground: _SIMPLE_ and _VERY CONSERVATIVE_ compute shaders.

    Sometimes, the software cannot even tell the hardware is actually 'crashed' and spitting non-sense data. It goes even worse, some hardware block hot reset actually do not work and require a power cycle... Then a 'media players' able to use hardware decoding must always provide a clear and visible 'user button' in order to let this very user switch to full software decoding.

    Then, there is the next step of "corruption": some streams out there are "wrong", but this "wrong" will be decoded ok on only some specific decoders and not other ones even though the format is following the same specs.

    What a mess.

    I hope those compute shaders are not using that abomination of glsl(or the dx one) namely are SPIR-V shaders generated with plain and simple C code.

    • pandaforce 9 minutes ago
      These are all gripes you might have with Vulkan Video. Unlike with Vulkan Video, in Compute, bounds checking is the norm. Overreading a regular buffer will not result in a GPU hang or crash. If you use pointers, it will, but if you use pointers, its up to you to check if overreads can happen.

      The bitstream reader in FFmpeg for Vulkan Compute codecs is copied from the C code, along with bounds checking. The code which validates whether a block is corrupt or decodable is also taken from the C version. To date, I've never got a GPU hang while using the Compute codecs.

  • positron26 36 minutes ago
    > Most popular codecs were designed decades ago, when video resolutions were far smaller. As resolutions have exploded, those fixed-size minimum units now represent a much smaller fraction of a frame — which means far more of them can be processed in parallel. Modern GPUs have also gained features enabling cross-invocation communication, opening up further optimization opportunities.

    One only needs to look at GPU driven rendering and ray tracing in shaders to deduce that shader cores and memory subsystems these days have become flexible enough to do work besides lock-step uniform parallelism where the only difference was the thread ID.

    Nobody strives for random access memory read patterns, but the universal popularity of buffer device address and descriptor arrays can be taken somewhat as proof that these indirections are no longer the friction for GPU architectures that they were ten years ago.

    At the same time, the languages are no longer as restrictive as they once were. People are recording commands on the GPU. This kind of fiddly serial work is an indication that the ergonomics of CPU programming have less of a relative advantage, and that cuts deeply into the tradeoff costs.