Synthetics

Though we’ve covered bits and pieces of synthetic performance when discussing aspects of the Pascal architecture, before we move on to power testing I want to take a deeper look at synthetic performance. Based on what we know about the Pascal architecture we should have a good idea of what to expect, but these tests none the less serve as a canary for any architectural changes we may have missed.

Synthetic: TessMark, Image Set 4, 64x Tessellation

Starting off with tessellation performance, we find that the GTX 1080 further builds on NVIDIA’s already impressive tessellation performance. Unrivaled at this point, GTX 1080 delivers a 63% increase in tessellation performance here, and maintains a 24% lead over GTX 1070. Suffice it to say, the Pascal cards will have no trouble keeping up with geometry needs in games for a long time to come.

Breaking down performance by tessellation level to look at the GTX 980 and GTX 1080 more closely on a logarithmic scale, what we find is that there’s a rather consistent advantage for the GTX 1080 at all tessellation levels. Even 8x tessellation is still 56% faster. This indicates that NVIDIA hasn’t made any fundamental changes to their geometry hardware (PolyMorph Engines) between Maxwell 2 and Pascal. Everything has simply been scaled up in clockspeed and scaled out in the total number of engines. Though I will note that the performance gains are less than the theoretical maximum, so we're not seeing perfect scaling by any means.

Up next, we have SteamVR’s Performance Test. While this test is based on the latest version of Valve’s Source engine, the test itself is purely synthetic, designed to test the suitability of systems for VR, making it our sole VR-focused test at this time. It should be noted that the results in this test are not linear, and furthermore the score is capped at 11. Of particular note, cards that fail to reach GTX 970/R9 290 levels fall off of a cliff rather quickly. So test results should be interpreted a little differently.

SteamVR Performance Test

With the minimum recommended GTX 970 and Radeon R9 290 cards get in the mid-to-high 6 range, NVIDIA’s new Pascal cards max out the score at 11. Which for the purposes of this test means that both cards exceed Valve’s recommended specifications, making them capable of running Valve’s VR software at maximum quality with no performance issues.

Finally, for looking at texel and pixel fillrate, for 2016 we have switched from the rather old 3DMark Vantage to the Beyond3D Test Suite. This test offers a slew of additional tests – many of which use behind the scenes or in our earlier architectural analysis – but for now we’ll stick to simple pixel and texel fillrates.

Beyond3D Suite - Pixel Fillrate

Starting with pixel fillrate, the GTX 1080 is well in the lead. While at 64 ROPs GP104 has fewer ROPs than the GM200 based GTX 980 Ti, it more than makes up for the difference with significantly higher clockspeeds. Similarly, when it comes to feeding those ROPs, GP104’s narrower memory bus is more than offset with the use of 10Gbps GDDR5X. But even then the two should be closer than this on paper, so the GTX 1080 is exceeding expectations.

As we discovered in 2014 with Maxwell 2, NVIDIA’s Delta Color Compression technology has a huge impact on pixel fillrate testing. So most likely what we’re seeing here is Pascal’s 4th generation DCC in action, helping GTX 1080 further compress its buffers and squeeze more performance out of the ROPs.

Though with that in mind, it’s interesting to note that even with an additional generation of DCC, this really only helps NVIDIA keep pace. The actual performance gains here versus GTX 980 are 56%, not too far removed from the gains we see in games and well below the theoretical difference in FLOPs. So despite the increase in pixel throughput due to architectural efficiency, it’s really only enough to help keep up with the other areas of the more powerful Pascal GPU.

As for GTX 1070, things are a bit different. The card has all of the ROPs of GTX 1080 and 80% of the memory bandwidth, however what it doesn’t have is GP104’s 4th GPC. Home of the Raster Engine responsible for rasterization, GTX 1070 can only setup 48 pixels/clock to begin with, despite the fact that the ROPs can accept 64 pixels. As a result it takes a significant hit here, delivering 77% of GTX 1080’s pixel throughput. With all of that said, the fact that in-game performance is closer than this is a reminder to the fact that while pixel throughput is an important part of game performance, it’s often not the bottleneck.

Beyond3D Suite - INT8 Texel Fillrate

As for INT8 texel fillrates, the results are much more straightforward. GTX 1080’s improvement over GTX 980 in texel throughput almost perfectly matches the theoretical improvement we’d expect based on the specifications (if not slightly exceeding it), delivering an 85% boost. As a result it’s now the top card in our charts for texel throughput, dethroning the still-potent Fury X. Meanwhile GTX 1070 backs off a bit from these gains, as we’d expect, as a consequence of having only three-quarters the number of texture units.

Compute Power, Temperature, & Noise
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  • Robalov - Tuesday, July 26, 2016 - link

    Feels like it took 2 years longer than normal for this review :D Reply
  • extide - Wednesday, July 27, 2016 - link

    The venn diagram is wrong -- for GP104 it says 1:64 speed for FP16 -- it is actually 1:1 for FP16 (ie same speed as FP32) (NOTE: GP100 has 2:1 FP16 -- meaning FP16 is twice as fast as FP32) Reply
  • extide - Wednesday, July 27, 2016 - link

    EDIT: I might be incorrect about this actually as I have seen information claiming both .. weird. Reply
  • mxthunder - Friday, July 29, 2016 - link

    its really driving me nuts that a 780 was used instead of a 780ti. Reply
  • yhselp - Monday, August 8, 2016 - link

    Have I understood correctly that Pascal offers a 20% increase in memory bandwidth from delta color compression over Maxwell? As in a total average of 45% over Kepler just from color compression? Reply
  • flexy - Sunday, September 4, 2016 - link

    Sorry, late comment. I just read about GPU Boost 3.0 and this is AWESOME. What they did, is expose what previously was only doable with bios modding - eg assigning the CLK bins different voltages. The problem with overclocking Kepler/Maxwell was NOT so much that you got stuck with the "lowest" overclock as the article says, but that simply adding a FIXED amount of clocks across the entire range of clocks, as you would do with Afterburner etc. where you simply add, say +120 to the core. What happened here is that you may be "stable" at the max overclock (CLK bin), but since you added more CLKs to EVERY clock bin, the assigned voltages (in the BIOS) for each bin might not be sufficient. Say you have CLK bin 63 which is set to 1304Mhz in a stock bios. Now you use Afterburner and add 150 Mhz, now all of a sudden this bin amounts to 1454Mhz BUT STILL at the same voltage as before, which is too low for 1454Mhz. You had to manually edit the table in the BIOS to shift clocks around, especially since not all Maxwell cards allowed adding voltage via software. Reply
  • Ether.86 - Tuesday, November 1, 2016 - link

    Astonishing review. That's the way Anandtech should be not like the mobile section which sucks... Reply
  • Warsun - Tuesday, January 17, 2017 - link

    Yeah looking at the bottom here.The GTX 1070 is on the same level as a single 480 4GB card.So that graph is wrong.
    http://www.hwcompare.com/30889/geforce-gtx-1070-vs...
    Remember this is from GPU-Z based on hardware specs.No amount of configurations in the Drivers changes this.They either screwed up i am calling shenanigans.
    Reply
  • marceloamaral - Thursday, April 13, 2017 - link

    Nice Ryan Smith! But, my question is, is it truly possible to share the GPU with different workloads in the P100? I've read in the NVIDIA manual that "The GPU has a time sliced scheduler to schedule work from work queues belonging to different CUDA contexts. Work launched to the compute engine from work queues belonging to different CUDA contexts cannot execute concurrently." Reply
  • marceloamaral - Thursday, April 13, 2017 - link

    Nice Ryan Smith! But, my question is, is it truly possible to share the GPU with different workloads in the P100? I've read in the NVIDIA manual that "The GPU has a time sliced scheduler to schedule work from work queues belonging to different CUDA contexts. Work launched to the compute engine from work queues belonging to different CUDA contexts cannot execute concurrently." Reply

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