DeepBench Inference: RNN and Sparse GEMM

Rounding out the last of our DeepBench inference tests are RNN and Sparse GEMM, both available in single precision only. That being said, the FP16 parameter could be selected anyway. Given the low results all around, this is more of an artifact than anything else.

DL Inference: DeepBench - RNN (LSTM)

DL Inference: DeepBench - RNN (GRU)

DL Inference: DeepBench - Sparse GEMM

While RNNs might also be accelerated, DeepBench and NVIDIA only support single precision RNN inference at this time.

DeepBench Inference: Convolutions NVIDIA Caffe2 Docker: ResNet50 and ImageNet
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  • SirCanealot - Tuesday, July 03, 2018 - link

    No overclocking benchmarks. WAT. ¬_¬ (/s)

    Thanks for the awesome, interesting write up as usual!
    Reply
  • Chaitanya - Tuesday, July 03, 2018 - link

    This is more of an enterprise product for consumers so even if overclocking it enabled its something that targeted demographic is not going to use. Reply
  • Samus - Tuesday, July 03, 2018 - link

    wooooooosh Reply
  • MrSpadge - Tuesday, July 03, 2018 - link

    He even put the "end sarcasm" tag (/s) to point out this was a joke. Reply
  • Ticotoo - Tuesday, July 03, 2018 - link

    Where oh where are the MacOS drivers? It took 6 months to get the pascal Titan drivers.
    Hopefully soon
    Reply
  • cwolf78 - Tuesday, July 03, 2018 - link

    Nobody cares? I wouldn't be surprised if support gets dropped at some point. MacOS isn't exactly going anywhere. Reply
  • eek2121 - Tuesday, July 03, 2018 - link

    Quite a few developers and professionals use Macs. Also college students. By manufacturer market share Apple probably has the biggest share, if not then definitely in the top 5. Reply
  • mode_13h - Tuesday, July 03, 2018 - link

    I doubt it. Linux rules the cloud, and that's where all the real horsepower is at. Lately, anyone serious about deep learning is using Nvidia on Linux. It's only 2nd-teir players, like AMD and Intel, who really stand anything to gain by supporting niche platforms like Macs and maybe even Windows/Azure.

    Once upon a time, Apple actually made a rackmount OS X server. I think that line has long since died off.
    Reply
  • Freakie - Wednesday, July 04, 2018 - link

    Lol, those developers and professionals use their Macs to remote in to their compute servers, not to do any of the number crunching with.

    The idea of using a personal computer for anything except writing and debugging code is next to unheard of in an environment that requires the kind of power that these GPUs are meant to output. The machine they use for the actual computations are 99.5% of the time, a dedicated server used for nothing but to complete heavy compute tasks, usually with no graphical interface, just straight command-line.
    Reply
  • philehidiot - Wednesday, July 04, 2018 - link

    If it's just a command line why bother with a GPU like this? Surely integrated graphics would do?

    (Even though this is a joke, I'm not sure I can bear the humiliation of pressing "submit")
    Reply

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