Nvidia Turing Architecture [2018]

Discussion in 'Architecture and Products' started by pharma, Sep 13, 2018.

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  1. pharma

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    New Tesla T4 GPU and New TensorRT Software Enable Intelligent Voice, Video, Image and Recommendation Services
    September 12, 2018

    NVIDIA today launched an AI data center platform that delivers the industry’s most advanced inference acceleration for voice, video, image and recommendation services.

    Delivering the fastest performance with lower latency for end-to-end applications, the platform enables hyperscale data centers to offer new services, such as enhanced natural language interactions and direct answers to search queries rather than a list of possible results.
    ...
    To optimize the data center for maximum throughput and server utilization, the NVIDIA TensorRT Hyperscale Platform includes both real-time inference software and Tesla T4 GPUs, which process queries up to 40x faster than CPUs alone.
    NVIDIA estimates that the AI inference industry is poised to grow in the next five years into a $20 billion market.

    The NVIDIA TensorRT Hyperscale Inference Platform features NVIDIA Tesla T4 GPUs based on the company’s breakthrough NVIDIA Turing™ architecture and a comprehensive set of new inference software.
    Key elements include:

    • NVIDIA Tesla T4 GPU – Featuring 320 Turing Tensor Cores and 2,560 CUDA® cores, this new GPU provides breakthrough performance with flexible, multi-precision capabilities, from FP32 to FP16 to INT8, as well as INT4. Packaged in an energy-efficient, 75-watt, small PCIe form factor that easily fits into most servers, it offers 65 teraflops of peak performance for FP16, 130 teraflops for INT8 and 260 teraflops for INT4.
    • NVIDIA TensorRT 5 – An inference optimizer and runtime engine, NVIDIA TensorRT 5 supports Turing Tensor Cores and expands the set of neural network optimizations for multi-precision workloads.
    • NVIDIA TensorRT inference server – This containerized microservice software enables applications to use AI models in data center production. Freely available from the NVIDIA GPU Cloud container registry, it maximizes data center throughput and GPU utilization, supports all popular AI models and frameworks, and integrates with Kubernetes and Docker.

    https://nvidianews.nvidia.com/news/...form-to-fuel-next-wave-of-ai-powered-services
     
    #1 pharma, Sep 13, 2018
    Last edited: Sep 13, 2018
  2. Pressure

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    8.1 TFlops (FP32) at 75 Watt, not bad.
     
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  3. jlippo

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  4. Rootax

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    Already a video up at Gamers Nexus

     
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  5. Bludd

    Bludd Experiencing A Significant Gravitas Shortfall
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    Funny, It Worked Last Time...
    I see Ryan Smith is piling the pressure on Nate over at Anandtech

    .
     
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  6. Digidi

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  8. DavidGraham

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    Actually it's a bit more than that. But they are similar in that it needs developer support to work.

    Yup. We have 6 benchmarks now to compare Pascal to Turing.
     
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  9. Pinstripe

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    This is what the whitepaper says about DX12 Tier levels (page 54):

    So I suppose this makes it now equal to Vega?
     
  10. Digidi

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    Where do you see, that it is more than Vega?
     
  11. DavidGraham

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    It offloads some of the load on the CPU to the GPU to increase the number of drawn objects on screen. It also has a LOD management system that works through automatic adaptive Tessellation. It can also modify and manipulate geometry on the fly, as shown in the Spherical Cutaway example in the white paper. Where the mesh shader is culling and modifying geometry based on its position relative to the sphere.

    So while Vega's primitive shaders are focused more on accelerating current geometry processing as a means to improve AMD's shortcomings in that area, Turing's mesh shaders build on NVIDAI's lead in geometry processing to enable more stuff on screen and are aimed more at enhancing some of it's quality and flexibility.
     
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  12. Kaotik

    Kaotik Drunk Member
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    If they also added Stencil Reference Value from Pixel Shader then yes, otherwise no.
     
  13. pharma

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    https://hexus.net/tech/reviews/grap...g-architecture-examined-and-explained/?page=6
     
    #13 pharma, Sep 14, 2018
    Last edited: Sep 14, 2018
  14. Bludd

    Bludd Experiencing A Significant Gravitas Shortfall
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    Funny, It Worked Last Time...
  15. Ryan Smith

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  16. Bludd

    Bludd Experiencing A Significant Gravitas Shortfall
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    Funny, It Worked Last Time...
    The plates look good tho
     
  17. Malo

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    Thanks Ryan. I'm really interested in DLSS and exactly how it all works from start to finish. The whole concept is baffling to me.

    The idea of sampling a particular scene at 64x supersampling to determine the best subsample position for that particular frame makes sense. So for final real-time rendering you know ahead of time what a particular subpixel position is best when sampling pixels for geometry that requires it. And it's a concept that works fine with their infiltrator demo as it's on rails, you're using fixed position cameras for every frame. Where the concept breaks down is for a game where the camera position and scene geometry, shading, post-effects etc are all unknown at a point in time.

    Do they have monkeys playing a game for days on end at 64x supersampling to create those ground truth reference images? Are game developers required to create a hook for their DNN to then "play" the game at all possible scenes and camera positions? Even if that concept is even plausible (the data processing and image requirements must be enormous for a single game), what then becomes of all that information? What is then created for use by tensors in the game real-time and how is that stored in the driver? I just don't know enough by AI training and inferencing to begin to understand how this works for AA in real-time games.
     
  18. silent_guy

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    @Malo
    I think you’re overthinking this a little bit.

    Here’s an example about deep learning that I thought very interesting, and that might apply for DLSS.

    I was looking at a hobby project: license plate recognition. Some other guy has done the same thing. He had gathered a bunch pictures with license plates around town and trained his network to detect them.

    It worked great.

    And then he tried it on pictures on the web, and it didn’t work very well in some cases.

    Turned out that the network had become very good at recognizing license plates with a particular font for his country, but not one where the characters were curved a bit differently. It wasn’t that it didn’t work at all, but the results were as good as they could be.

    With a generic DLSS network, you’d expect similar behavior: it’d do an overall decent job, but it wouldn’t be tuned to the particular visual/artistic mood of the game. Or the camera perspective, etc.

    So when developers submit in-game screenshots, you can improve the network to behave better.

    That doesn’t mean that you need screenshots everywhere in the game, just like Google doesn’t need pictures of all the traffic signs in the world to recognize them. Neural nets are excellent at coming up with good results as long as they are similar enough to what they have been trained for.

    It’s just an incremental improvement to get that last extra bit of quality.
     
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  19. Kaotik

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    Here's tech reports take on it:
    https://techreport.com/review/34095/popping-the-hood-on-nvidia-turing-architecture/2

    The way I read the whitepaper would suggest that they actually render on lower than set resolution, use few of rendered frames and the "ground truths" to estimate AA'd target resolution frames at about TAA level of quality (contrary to their whitepaper, people who saw the infiltrator demo live said DLSS doesn't match TAA, not in that demo anyway)
     
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  20. jlippo

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    #20 jlippo, Sep 14, 2018
    Last edited: Sep 14, 2018
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