Pascal’s Architecture: What Follows Maxwell

With the launch of a new generation of GPUs we’ll start things off where we always do: the architecture.

Discrete GPUs occupy an interesting space when it comes to the relationship between architecture and manufacturing processes. Whereas CPUs have architecture and manufacturing process decoupled – leading to Intel’s aptly named (former) tick-tock design methodology – GPUs have aligned architectures with manufacturing processes, with a new architecture premiering alongside a new process. Or rather, GPU traditionally did. Maxwell threw a necessary spanner into all of this, and in its own way Pascal follows this break from tradition.

As the follow-up to their Kepler architecture, with Maxwell NVIDIA introduced a significantly altered architecture, one that broke a lot of assumptions Kepler earlier made and in the process vaulted NVIDIA far forward on energy efficiency. What made Maxwell especially important from a development perspective is that it came not on a new manufacturing process, but rather on the same 28nm process used for Kepler two years earlier, and this is something NVIDIA had never done before. With the 20nm planar process proving unsuitable for GPUs and only barely suitable for SoCs – the leakage from planar transistors this small was just too high – NVIDIA had to go forward with 28nm for another two years. It would come down to their architecture team to make the best of the situation and come up with a way to bring a generational increase in performance without the traditional process node shrink.

Now in 2016 we finally have new manufacturing nodes with the 14nm/16nm FinFET processes, giving GPU manufacturers a long-awaited (and much needed) opportunity to bring down power consumption and reduce chip size through improved manufacturing technology. The fact that it has taken an extra two years to get here, and what NVIDIA did in the interim with Maxwell, has opened up a lot of questions about what would follow for NVIDIA. The GPU development process is not so binary or straightforward that NVIDIA designed Maxwell solely because they were going to be stuck on the 28nm process – NVIDIA would have done Maxwell either way – but it certainly was good timing to have such a major architectural update fall when it did.

So how does NVIDIA follow-up on Maxwell then? The answer comes in Pascal, NVIDIA’s first architecture for the FinFET generation. Designed to be built on TSMC’s 16nm process, Pascal is the latest and the greatest, and like every architecture before it is intended to further push the envelope on GPU performance, and ultimately push the envelope on the true bottleneck for GPU performance, energy efficiency.

HPC vs. Consumer: Divergence

Pascal is an architecture that I’m not sure has any real parallel on a historical basis. And a big part of that is because to different groups within NVIDIA, Pascal means different things and brings different things, despite the shared architecture. On the one side is the consumer market, which is looking for a faster still successor to what Maxwell delivered in 2014 and 2015. Meanwhile on the high performance compute side, Pascal is the long-awaited update to the Kepler architecture (Maxwell never had an HPC part), combining the lessons of Maxwell with the specific needs of the HPC market.

The result is that there’s an interesting divergence going on between the HPC side and its GP100 GPU, and the consumer side and the GP104 GPU underlying GTX 1080. Even as far back as Fermi there was a distinct line separating HPC-class GPUs (GF100) from consumer/general compute GPUs (GF104), but with Pascal this divergence is wider than ever before. Ultimately the HPC market and GP100 is beyond the scope of this article and I’ll pick it up in detail another time, but because NVIDIA announced GP100 before GP104, it does require a bit of addressing to help sort out what’s going on and what NVIDA’s design goals were with GP104.

Pascal as an overarching architecture contains a number of new features, however not all of those features are present in all SKUs. If you were to draw a Venn diagram of Pascal, what you would find is that the largest collection of features are found in GP100, whereas GP104, like the previous Maxwell architecture before it, is stripped down for speed and efficiency. As a result while GP100 has some notable feature/design elements for HPC – things such faster FP64 & FP16 performance, ECC, and significantly greater amounts of shared memory and register file capacity per CUDA core – these elements aren’t present in GP104 (and presumably, future Pascal consumer-focused GPUs).

Ultimately what we’re seeing in this divergence is a greater level of customization between NVIDIA’s HPC and consumer markets. The HPC side of NVIDIA is finally growing up, and it’s growing fast. The long term plan at NVIDIA has been to push GPU technology beyond consumer and professional graphics, and while it has taken years longer than NVIDIA originally wanted, thanks in big part to success in the deep learning market, NVIDIA is finally achieving their goals.

This means that although GP100 is a fully graphics capable GPU, it doesn’t necessarily have to be put into video cards to make sense for NVIDIA to manufacture, and as a result NVIDIA can make it even more compute focused than prior-generation parts like GK110 and GF110. And that in turn means that although this divergence is driven by the needs of the HPC market – what features need to be added to make a GPU more suitable for HPC use cases – from the perspective of the consumer market there is a tendency to perceive that consumer parts are falling behind. Especially with how GP100 and GP104’s SMs are differently partitioned.

This is a subject I’ll revisit in much greater detail in the future when we focus on GP100. But for now, especially for the dozen of you who’ve emailed over the past month asking about why the two are so different, the short answer is that the market needs for HPC are different from graphics, and the difference in how GP100 and GP104 are partitioned reflect this. GP100 and GP104 are both unequivocally Pascal, but GP100 gets smaller SM partitions in order to increase the number of registers and the amount of shared memory available per CUDA core. Shared memory and register contention on graphics workloads isn’t nearly as great as with HPC tasks – pixel shader threads are relatively short and independent from each other – which means that while the increased ratios benefit HPC workloads, for graphics the gains would be minimal. And the costs to power and die space would, in turn, far outweigh any benefits.

The GTX 1080 & GTX 1070 Review GP104: The Heart of GTX 1080
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  • Ranger1065 - Thursday, July 21, 2016 - link

    Your unwavering support for Anandtech is impressive.

    I too have a job that keeps me busy, yet oddly enough I find the time to browse (I prefer that word to "trawl") a number of sites.

    I find it helps to form objective opinions.

    I don't believe in early adoption, but I do believe in getting the job done on time, however if you are comfortable with a 2 month delay, so be it :)

    Interesting to note that architectural deep dives concern your art and media departments so closely in their purchasing decisions. Who would have guessed?

    It's true (God knows it's been stated here often enough) that
    Anandtech goes into detail like no other, I don't dispute that.
    But is it worth the wait? A significant number seem to think not.

    Allow me to leave one last issue for you to ponder (assuming you have the time in your extremely busy schedule).

    Is it good for Anandtech?
    Reply
  • catavalon21 - Thursday, July 21, 2016 - link

    Impatient as I was at the first for benchmarks, yes, I'm a numbers junkie, since it's evident precious few of us will have had a chance to buy one of these cards yet (or the 480), I doubt the delay has caused anyone to buy the wrong card. Can't speak for the smart phone review folks are complaining about being absent, but as it turns out, what I'm initially looking for is usually done early on in Bench. The rest of this, yeah, it can wait. Reply
  • mkaibear - Saturday, July 23, 2016 - link

    Job, house, kids, church... more than enough to keep me sufficiently busy that I don't have the time to browse more than a few sites. I pick them quite carefully.

    Given the lifespan of a typical system is >5 years I think that a 2 month delay is perfectly reasonable. It can often take that long to get purchasing signoff once I've decided what they need to purchase anyway (one of the many reasons that architectural deep dives are useful - so I can explain why the purchase is worthwhile). Do you actually spend someone else's money at any point or are you just having to justify it to yourself?

    Whether or not it's worth the wait to you is one thing - but it's clearly worth the wait to both Anandtech and to Purch.
    Reply
  • razvan.uruc@gmail.com - Thursday, July 21, 2016 - link

    Excellent article, well deserved the wait! Reply
  • giggs - Thursday, July 21, 2016 - link

    While this is a very thorough and well written review, it makes me wonder about sponsored content and product placement.
    The PG279Q is the only monitor mentionned, making sure the brand appears, and nothing about competing products. It felt unnecessary.
    I hope it's just a coincidence, but considering there has been quite a lot of coverage about Asus in the last few months, I'm starting to doubt some of the stuff I read here.
    Reply
  • Ryan Smith - Thursday, July 21, 2016 - link

    "The PG279Q is the only monitor mentionned, making sure the brand appears, and nothing about competing products."

    There's no product placement or the like (and if there was, it would be disclosed). I just wanted to name a popular 1440p G-Sync monitor to give some real-world connection to the results. We've had cards for a bit that can drive 1440p monitors at around 60fps, but GTX 1080 is really the first card that is going to make good use of higher refresh rate monitors.
    Reply
  • giggs - Thursday, July 21, 2016 - link

    Fair enough, thank you for responding promptly. Keep up the good work! Reply
  • arh2o - Thursday, July 21, 2016 - link

    This is really the gold standard of reviews. More in-depth than any site on the internet. Great job Ryan, keep up the good work. Reply
  • Ranger1065 - Thursday, July 21, 2016 - link

    This is a quality article. Reply
  • timchen - Thursday, July 21, 2016 - link

    Great article. It is pleasant to read more about technology instead of testing results. Some questions though:

    1. higher frequency: I am kind of skeptical that the overall higher frequency is mostly enabled by FinFET. Maybe it is the case, but for example when Intel moved to FinFET we did not see such improvement. RX480 is not showing that either. It seems pretty evident the situation is different from 8800GTX where we first get frequency doubling/tripling only in the shader domain though. (Wow DX10 is 10 years ago... and computation throughput is improved by 20x)

    2. The fastsync comparison graph looks pretty suspicious. How can Vsync have such high latency? The most latency I can see in a double buffer scenario with vsync is that the screen refresh just happens a tiny bit earlier than the completion of a buffer. That will give a delay of two frame time which is like 33 ms (Remember we are talking about a case where GPU fps>60). This is unless, of course, if they are testing vsync at 20hz or something.
    Reply

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