Benchmarking Testbed Setup

Our hardware has been modified for deep learning workloads with a larger SSD and more RAM.

CPU: Intel Core i7-7820X @ 4.3GHz
Motherboard: Gigabyte X299 AORUS Gaming 7
Power Supply: Corsair AX860i
Hard Disk: Intel 1.1TB
Memory: G.Skill TridentZ RGB DDR4-3200 4 x 16GB (15-15-15-35)
Case: NZXT Phantom 630 Windowed Edition
Monitor: LG 27UD68P-B
Video Cards: NVIDIA Titan V
NVIDIA Titan Xp
NVIDIA GeForce GTX Titan X (Maxwell)
AMD Radeon RX Vega 64
Video Drivers: NVIDIA: Release 390.30 for Linux x64
AMD:
OS: Ubuntu 16.04.4 LTS

With deep learning benchmarking requiring some extra hardware, we must give thanks to the following that made this all happen.

Many Thanks To...

Much thanks to our patient colleagues over at Tom's Hardware, for both splitting custody of the Titan V and lending us their Titan Xp and Quadro P6000. None of this would have been possible without their support.

And thank you to G.Skill for providing us with a 64GB set of DDR4 memory suitable for deep learning workloads, not a small feat in these DDR4 price-inflated times. G.Skill has been a long-time supporter of AnandTech over the years, for testing beyond our CPU and motherboard memory reviews. We've reported on their high capacity and high-frequency kits, and every year at Computex G.Skill holds a world overclocking tournament with liquid nitrogen right on the show floor.

Further Reading: AnandTech's Memory Scaling on Haswell Review, with G.Skill DDR3-3000

Methodology & Testing: Deep Learning Edition DeepBench Training: GEMM & RNN
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  • mode_13h - Wednesday, July 4, 2018 - link

    It's not that hard, really. They're just saying Nvidia made a library (cuDNN), so that different deep learning frameworks don't each have to hand-optimize code for things like its exotic tensor cores.

    For their part, AMD has a similar library they call MIOpen.
  • philehidiot - Wednesday, July 4, 2018 - link

    Why thank you. That now does help it make a little more sense. The maths does make sense but the computer science is generally beyond me.
  • aelizo - Wednesday, July 4, 2018 - link

    At that price point, I would have liked to see some comparison to 2xTinan Xp, or even some comparison to 3x1080Ti's.
    Last year I saw some comparison between this sets on pytorch:
    https://medium.com/@u39kun/titan-v-vs-1080-ti-head...
  • mode_13h - Wednesday, July 4, 2018 - link

    I'm suspicious that he's not actually using the tensor cores. The V100/GV100 also has double-rate fp16, like the P100/GP100 before it. So, a < 2x improvement from going to 16-bit suggests it might only be using the packed half-precision instructions, rather than the tensor cores.

    Either that or he's not using batching and is completely limited by memory bottlenecks.
  • aelizo - Wednesday, July 4, 2018 - link

    I suspect something similar, that is Why Nate could have done a great job with a similar comparison.
  • Nate Oh - Monday, July 9, 2018 - link

    Unfortunately, we only have 1 Titan Xp, which is actually on loan from TH. These class of devices are (usually) not sampled by NVIDIA so we could not have pursued what you suggest. We split custody of Titan V, and that alone was not an insignificant investment.

    Additionally, mGPU DL analysis introduces a whole new can of worms. As some may have noticed, I have not mentioned NCCL/MPI, NVLink, Volta mGPU enhancements, All Reduce, etc. It's definitely a topic for further investigation if the demand and resources match.
  • mode_13h - Tuesday, July 10, 2018 - link

    Multi-GPU scaling is getting somewhat esoteric, but perhaps a good topic for future articles.

    Would be cool to see the effect of NVLink, if you can get access to such a system in the cloud. Maybe Nvidia will give you some sort of "press" access to their cloud?
  • ballsystemlord - Saturday, July 7, 2018 - link

    Here are some spelling/grammar corrections. You write far fewer than most of the other authors at anandtech ( If Ian had written this I would have need 2 pages for all the corrections :) ). Good job!

    "And Volta does has those separate INT32 units."
    You mean "have".
    And Volta does have those separate INT32 units.

    "For our purposes, the tiny image dataset of CIFAR10 works fine as running a single-node on a dataset like ImageNet with non-professional hardware that could be old as Kepler"...
    Missing "as".
    For our purposes, the tiny image dataset of CIFAR10 works fine as running a single-node on a dataset like ImageNet with non-professional hardware that could be as old as Kepler...

    "Moving forward, we're hoping that MLPerf and similar efforts make good headway, so that we can tease out a bit more secrets from GPUs."
    Grammar error.
    Moving forward, we're hoping that MLPerf and similar efforts make good headway, so that we can tease out a bit more of the secrets from GPUs.
  • mode_13h - Saturday, July 7, 2018 - link

    Yeah, if that's the worst you found, no one would even *suspect* him for being a lolcat.
  • Vanguarde - Monday, July 9, 2018 - link

    I purchased this card to get better frames in Witcher 3 at 4K everything maxed out, heavily modded. Never dips below 60fps and usually near 80-100fps

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