First Thoughts

Given what we’ve seen over the past half-decade or so with NVIDIA’s success in the GPU computing space, it’s easy to see why NVIDIA has made the architectural decisions that have shaped the Pascal and now Volta architectures. At the same time however, the increasing specialization of NVIDIA’s compute-centric GPUs and resulting products has meant that the company has needed to rethink and refocus the Titan family of video cards. Though originally created for a mixed prosumer market, Titan X and later have seen increasing success not as graphics cards but as workstation-class compute cards as professional customers have invested heavily in deep learning technologies. As a result the Titan line itself has been drifting more and more towards being a compute product, and with the launch of the Titan V, NVIDIA’s latest card takes its biggest step yet in this regard.

By switching the Titan family over from NVIDIA’s consumer GPUs to their compute-centric GV100 GPU, NVIDIA has centered the Titan V on compute more strongly than any Titan before it. And in compute it flies. Though we’ve still only had a chance to scratch the surface of Titan V, GV100, and the Volta architecture in today’s preview, all of our early benchmark data points to something that datacenter customers have already known for several months now: GV100’s not just a bigger NVIDIA GPU, and Volta’s not a minor iteration on Pascal. Sure, GV100 is also literally huge, but under the hood Volta brings a great deal of new architectural improvements and optimizations that, until the launch of the Titan V (and the opportunity to get our hands on the card) we haven’t really had the chance to properly appreciate.

The biggest factor here for NVIDIA is of course the tensor cores. NVIDIA has bet big on what’s essentially a highly specialized dense matrix multiplier, and because it maps so well to the needs of neural network training and execution, this is a bet that appears to already be paying off in spades for the company. As we saw in our GEMM results, if you can really put the tensor cores to good use, then the sheer mathematical throughput is nothing short of immense. But – and this is key – developers need to be able write their software around those tensor cores to get the most out of the Titan V. And I’m unsure whether any of this will translate into the consumer space and use cases, or if this is a feature that’s ultimately going to be mostly adopted by the HPC crown.

However even disregarding the tensor cores, what we’re seeing with the Titan V is that in terms of compute efficiency, this card appears to be on a very different level from the Pascal-based Titan Xp, and that card was no slouch either. I feel like there’s a bit of an apples-to-oranges comparison here since under the hood the cards are so different – the consumer GP102 GPU versus the compute GV100 GPU – but they are still very much Titans. Titan V is punching above its weight in many compute tasks compared to what it should be able to do on paper, and this is thanks in large part to the efficiency gains we’re seeing. For our full review hopefully we can track down a Quadro GP100 and try to better isolate how much of Volta and GV100’s gains come from improvements versus the Pascal architecture, and how much is from the unique, compute-centric configurations of NVIDIA’s Gx100-class GPUs.

For compute customers then, the flip side to all of this is that at $3000, the Titan V is quite the expensive card. Much more so than the mere $1200 of the Titan Xp. As the Titan V doesn’t experience dramatic performance gains in every application, I suspect that Titan compute customers are going to continue picking up both cards for some time to come. But a crazy as a $3000 price tag is, for customers that need to maximize single-GPU performance and/or get their hands on a product with tensor cores, then the Titan V is certainly going to fill this role well. Titan V can absolutely deliver the kind of performance to justify its price in the compute space.

However for graphics customers, we’re looking at a different story. To preface this, I don’t want to entirely discount the Titan V as a video card here, because at the end of the day it’s the fastest video card released by NVIDIA to date, gaming and all. But the immense performance gains we saw in our compute benchmarks – the kind of gains that justify a $3000 price tag – are not present in our gaming benchmarks. Right now the Titan V is moderately faster than the Titan Xp in gaming workloads – and in a world where multi-GPU scaling is on the rocks, that’s a huge element in favor of the Titan V – but that’s counterbalanced by the fact that in some cases the Titan V can’t even beat the Titan Xp.

Now how much of that is because of GV100’s suitability for gaming or how much of it is due to drivers is something that remains to be seen. The Titan V is a new card, the first graphics card based on the Volta architecture, and it’s not at all clear whether NVIDIA’s launch drivers are fully optimized for the new architecture. What is obvious is that the current drivers do have some notable bugs, and that does lend credit to the idea that the launch drivers aren’t fully baked. But even if that’s the case, that’s likely not something worth betting on. The Titan V may use the GeForce driver stack, but mentally it’s better compartmentalized as a compute card that can do graphics on the side, rather than the jack-of-all-trades nature that has previously defined the Titan family.

Closing out today’s preview, it’s hard not to come away impressed with what we’ve seen. But we’ve also just scratched the surface over the span of a few days. We’re going to continue looking at the Titan V, including getting some deep learning software up and running. Which is not to say that Titan V is merely a deep learning accelerator – it’s clearly much more than that – but this is definitely one of the most practical uses for the hardware right now, especially as software developers just now get their hands on Volta. Though for that matter I think it’ll be interesting to see what academics and researchers do with the hardware; they were one of the driving forces behind the original GTX Titan, and it’s no mistake that NVIDIA has a history of giving away Titans to these groups. Part of the need for a card like the Titan V in NVIDIA’s lineup is to help seed Volta in this fashion, so the launch of the Titan V is part of a much larger and longer-term plan by NVIDIA.

Power, Temperature, & Noise
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  • maroon1 - Wednesday, December 20, 2017 - link

    Correct if I'm wrong, Crysis warhead running 4K with 4xSSAA means it is running 8K (4 times as much as 4K) and then downscale to 4K
  • Ryan Smith - Wednesday, December 20, 2017 - link

    Yes and no. Under the hood it's actually using a rotated grid, so it's a little more complex than just rendering it at a higher resolution.

    The resource requirements are very close to 8K rendering, but it avoids some of the quality drawbacks of scaling down an actual 8K image.
  • Frenetic Pony - Wednesday, December 20, 2017 - link

    A hell of a lot of "It works great but only if you buy and program exclusively for Nvidia!" stuff here. Reminds me of Sony's penchant for exclusive lock in stuff over a decade ago when they were dominant. Didn't work out for Sony then, and this is worse for customers as they'll need to spend money on both dev and hardware.

    I'm sure some will be shortsighted enough to do so. But with Google straight up outbuying Nvidia for AI researchers (reportedly up to, or over, 10 million for just a 3 year contract) it's not a long term bet I'd make.
  • tuxRoller - Thursday, December 21, 2017 - link

    I assumed you've not heard of CUDA before?
    NVIDIA had long been the only game in town when it comes to gpgpu HPC.
    They're really a monopoly at this point, and researchers have no interest in making they're jobs harder by moving to a new ecosystem.
  • mode_13h - Wednesday, December 27, 2017 - link

    OpenCL is out there, and AMD has had some products that were more than competitive with Nvidia, in the past. I think Nvidia won HPC dominance by bribing lots of researchers with free/cheap hardware and funding CUDA support in popular software packages. It's only with Pascal that their hardware really surpassed AMD's.
  • tuxRoller - Sunday, December 31, 2017 - link

    Ocl exists but cuda has MUCH higher mindshare. It's the de facto hpc framework used and taught in schools.
  • mode_13h - Sunday, December 31, 2017 - link

    True that Cuda seems to dominate HPC. I think Nvidia did a good job of cultivating the market for it.

    The trick for them now is that most deep learning users use frameworks which aren't tied to any Nvidia-specific APIs. I know they're pushing TensorRT, but it's certainly not dominant in the way Cuda dominates HPC.
  • tuxRoller - Monday, January 1, 2018 - link

    The problem is that even the gpu accelerated nn frameworks are still largely built first using cuda. torch, caffe and tensorflow offer varying levels of ocl support (generally between some and none).
    Why is this still a problem? Well, where are the ocl 2.1+ drivers? Even 2.0 is super patchy (mainly due to nvidia not officially supporting anything beyond 1.2). Add to this their most recent announcements about merging ocp into vulkan and you have yourself an explanation for why cuda continues to dominate.
    My hope is that khronos announce vulkan 2.0, with ocl being subsumed, very soon. Doing that means vendors only have to maintain a single driver (with everything consuming spirv) and nvidia would, basically, be forced to offer opencl-next. Bottom-line: if they can bring the ocl functionality into vulkan without massively increasing the driver complexity, I'd expect far more interest from the community.
  • mode_13h - Friday, January 5, 2018 - link

    Your mistake is focusing on OpenCL support as a proxy for AMD support. Their solution was actually developing OpenMI as a substitute for Nvidia's cuDNN. They have forks of all the popular frameworks to support it - hopefully they'll get merged in, once ROCm support exists in the mainline Linux kernel.

    Of course, until AMD can answer the V100 on at least power-effeciency grounds, they're going to remain an also-ran, in the market for training. I think they're a bit more competitive for inferencing workloads, however.
  • CiccioB - Thursday, December 21, 2017 - link

    What are you suggesting?
    GPU are a very customized piece of silicon and you have to code for them with optimization for each single architecture if you want to exploit them at the maximum.
    If you think that people buy $10.000 cards to be put in $100.000 racks for a multiple $1.000.000 server just to use open source not optimized not supported not guarantee code in order to make AMD fanboys happy, well, not, it's not like the industry works.
    Grow up.

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