The NVIDIA GeForce GTX 1080 & GTX 1070 Founders Editions Review: Kicking Off the FinFET Generation
by Ryan Smith on July 20, 2016 8:45 AM ESTGP104’s Architecture
Looking at an architecture diagram for GP104, Pascal ends up looking a lot like Maxwell, and this is not by chance. After making more radical changes to their architecture with Maxwell, for Pascal NVIDIA is taking a bit of a breather. Which is not to say that Pascal is Maxwell on 16nm – this is very much a major feature update – but when it comes to discussing the core SM architecture itself, there is significant common ground with Maxwell.
We’ll start with the GP104 SM. Simply named the SM for this generation – NVIDIA has ditched the generational suffix due to the potential for confusion with the used-elsewhere SMP – the GP104 SM is very similar to the Maxwell SM. We’re still looking at a single SM partially sub-divided into four pieces, each containing a single warp scheduler that’s responsible for feeding 32 CUDA cores, 8 load/store units, and 8 Special Function Units, backed by a 64KB register file. There are two dispatch ports per warp schedule, so when an instruction stream allows it, a warp scheduler can extract a limited amount of ILP with an instruction stream by issuing a second instruction to an unused resource.
Meanwhile shared between every pair of sub-partitions is 4 texture units and the combined L1/texture cache, again unchanged from Maxwell. Finally, we have the resources shared throughout the whole SM: the 96KB shared memory, the instruction cache, and not pictured on NVIDIA’s diagrams, the 4 FP64 CUDA cores and 1 FP16x2 CUDA core.
Overall then at the diagram level the GP104 SM looks almost identical to the Maxwell SM, but with one exception: the PolyMorph Engine. Although the distinction is largely arbitrary for GP104, the PolyMorph Engine has been moved up a level; it’s no longer part of the SM, but rather part of the newly re-introduced TPC, which itself sits between the GPC and the SM.
The TPC exists because although GP104 still has a 1:1 ratio between PolyMorph Engines and SMs, the Pascal architecture itself allows for different SM configurations, which is in turn used on GP100 to allow it to have multiple smaller SMs of 64 CUDA Cores. For GP100 the TPC allows for multiple SMs to share a PolyMorph Engine, but for GP104 there’s no sharing involved. To that end the TPC as an organizational unit technically exists across all Pascal parts, but it has no real significance for GP104. In fact it doesn’t even have a real name; NVIDIA reused the acronym from earlier DX10 architectures, where the TPC was the name assigned to the Texture Processor Cluster.
Looking at the bigger picture of the complete GP104 GPU, the similarities continue between GP104 and GM204. GP104’s SMs are clustered five-a-piece inside of the GPC, with each cluster sharing a single Raster Engine. Overall there are 4 such GPCs, giving us 20 SMs altogether. Compared to GM204 then, we’re looking at the same number of GPCs, with each GPC having gained 1 SM.
Things get more interesting when we look at the back end of the rendering/execution pipeline, which is comprised of the L2 cache, ROPs, and memory controllers. The ROP/L2 count has not changed relative to GM204 – we still have 64 ROPs paired up with a total of 2MB of L2 cache – however the memory controller count has. And with it the logical configuration of the ROP/L2 blocks have changed as well.
Whereas GM204 had 4 64bit GDDR5 memory controllers, each connected to 2 or 4 memory chips, GP104 breaks that down further to 8 32bit GDDR5X memory controllers, each of which is connected to 1 memory chip on GTX 1080. I’ll go into greater detail on GDDR5X a bit later, but the significance of this backend organizational change has to do with the introduction of GDDR5X. Because GDDR5X reads and writes data in 64B amounts (versus 32B amounts on GDDR5), NVIDIA has reorganized the memory controllers to ensure that each memory controller still operates on the same amount of data. With GDDR5 they teamed up two GDDR5 channels to get 64B operations, whereas with GDDR5X this can be accomplished with a single memory channel.
This in turn is where the ROP reorganization comes from. As there’s a 1:1 relationship between ROP partitions and memory controllers, the 64 ROPs are now broken up into 8 partitions for GP104, as opposed to 4 partitions on GM204. There are some performance tradeoffs that come from having more ROP partitions, but to the best of my knowledge these should not be significant.
Meanwhile the new GDDR5X memory controllers are also backwards compatible with traditional GDDR5, which in turn is used to drive the GTX 1070 with its 8Gbps GDDR5. The difference in operation between GDDR5 and GDDR5X does make the ROP situation a bit trickier overall for NVIDIA’s architects – now they need to be able to handle two different memory access patterns – though for NVIDIA this isn’t a wholly new problem. Previous generation architectures have supported both GDDR5 and DDR3, the two of which have their own differences in memory access patterns.
In a by-the-numbers comparison then, Pascal does not bring any notable changes in throughput relative to Maxwell. CUDA cores, texture units, PolyMorph Engines, Raster Engines, and ROPs all have identical theoretical throughput-per-clock as compared to Maxwell. So on a clock-for-clock, unit-for-unit basis, Pascal is not any faster on paper. And while NVIDIA does not disclose the size/speed of most of their internal datapaths, so far I haven’t seen anything to suggest that these have radically changed. This continuity means that outside of its new features, GP104 behaves a lot like GM204. Though it should be noted that real world efficiency isn’t quite as cut and dry, as various factors such as the increased SM count and changes in memory technology can greatly influence this.
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grrrgrrr - Wednesday, July 20, 2016 - link
Solid review! Some nice architecture introductions.euskalzabe - Wednesday, July 20, 2016 - link
The HDR discussion of this review was super interesting, but as always, there's one key piece of information missing: WHEN are we going to see HDR monitors that take advantage of these new GPU abilities?I myself am stuck at 1080p IPS because more resolution doesn't entice me, and there's nothing better than IPS. I'm waiting for HDR to buy my next monitor, but being 5 years old my Dell ST2220T is getting long in the teeth...
ajlueke - Wednesday, July 20, 2016 - link
Thanks for the review Ryan,I think the results are quite interesting, and the games chosen really help show the advantages and limitations of the different architectures. When you compare the GTX 1080 to its price predecessor, the 980 Ti, you are getting an almost universal ~25%-30% increase in performance.
Against rival AMDs R9 Fury X, there is more of a mixed bag. As the resolutions increase the bandwidth provided by the HBM memory on the Fury X really narrows the gap, sometimes trimming the margin to less that 10%,s specifically in games optimized more for DX12 "Hitman, "AotS". But it other games, specifically "Rise of the Tomb Raider" which boasts extremely high res textures, the 4Gb memory size on the Fury X starts to limit its performance in a big way. On average, there is again a ~25%-30% performance increase with much higher game to game variability.
This data lets a little bit of air out of the argument I hear a lot that AMD makes more "future proof" cards. While many Nvidia 900 series users may have to upgrade as more and more games switch to DX12 based programming. AMD Fury users will be in the same boat as those same games come with higher and higher res textures, due to the smaller amount of memory on board.
While Pascal still doesn't show the jump in DX12 versus DX11 that AMD's GPUs enjoy, it does at least show an increase or at least remain at parity.
So what you have is a card that wins in every single game tested, at every resolution over the price predecessors from both companies, all while consuming less power. That is a win pretty much any way you slice it. But there are elements of Nvidia’s strategy and the card I personally find disappointing.
I understand Nvidia wants to keep features specific to the higher margin professional cards, but avoiding HBM2 altogether in the consumer space seems to be a missed opportunity. I am a huge fan of the mini ITX gaming machines. And the Fury Nano, at the $450 price point is a great card. With an NVMe motherboard and NAS storage the need for drive bays in the case is eliminated, the Fury Nano at only 6” leads to some great forward thinking, and tiny designs. I was hoping to see an explosion of cases that cut out the need for supporting 10-11” cards and tons of drive bays if both Nvidia and AMD put out GPUs in the Nano space, but it seems not to be. HBM2 seems destined to remain on professional cards, as Nvidia won’t take the risk of adding it to a consumer Titan or GTX 1080 Ti card and potentially again cannibalize the higher margin professional card market. Now case makers don’t really have the same incentive to build smaller cases if the Fury Nano will still be the only card at that size. It’s just unfortunate that it had to happen because NVidia decided HBM2 was something they could slap on a pro card and sell for thousands extra.
But also what is also disappointing about Pascal stems from the GTX 1080 vs GTX 1070 data Ryan has shown. The GTX 1070 drops off far more than one would expect based off CUDA core numbers as the resolution increases. The GDDR5 memory versus the GDDR5X is probably at fault here, leading me to believe that Pascal can gain even further if the memory bandwidth is increased more, again with HBM2. So not only does the card limit you to the current mini-ITX monstrosities (I’m looking at you bulldog) by avoiding HBM2, it also very likely is costing us performance.
Now for the rank speculation. The data does present some interesting scenarios for the future. With the Fury X able to approach the GTX 1080 at high resolutions, most specifically in DX12 optimized games. It seems extremely likely that the Vega GPU will be able to surpass the GTX 1080, especially if the greatest limitation (4 Gb HBM) is removed with the supposed 8Gb of HBM2 and games move more and more the DX12. I imagine when it launches it will be the 4K card to get, as the Fury X already acquits itself very well there. For me personally, I will have to wait for the Vega Nano to realize my Mini-ITX dreams, unless of course, AMD doesn’t make another Nano edition card and the dream is dead. A possibility I dare not think about.
eddman - Wednesday, July 20, 2016 - link
The gap getting narrower at higher resolutions probably has more to do with chips' designs rather than bandwidth. After all, Fury is the big GCN chip optimized for high resolutions. Even though GP104 does well, it's still the middle Pascal chip.P.S. Please separate the paragraphs. It's a pain, reading your comment.
Eidigean - Wednesday, July 20, 2016 - link
The GTX 1070 is really just a way for Nvidia to sell GP104's that didn't pass all of their tests. Don't expect them to put expensive memory on a card where they're only looking to make their money back. Keeping the card cost down, hoping it sells, is more important to them.If there's a defect anywhere within one of the GPC's, the entire GPC is disabled and the chip is sold at a discount instead of being thrown out. I would not buy a 1070 which is really just a crippled 1080.
I'll be buying a 1080 for my 2560x1600 desktop, and an EVGA 1060 for my Mini-ITX build; which has a limited power supply.
mikael.skytter - Wednesday, July 20, 2016 - link
Thanks Ryan! Much appreciated.chrisp_6@yahoo.com - Wednesday, July 20, 2016 - link
Very good review. One minor comment to the article writers - do a final check on grammer - granted we are technical folks, but it was noticeable especially on the final words page.madwolfa - Wednesday, July 20, 2016 - link
It's "grammar", though. :)Eden-K121D - Thursday, July 21, 2016 - link
Oh the ironychrisp_6@yahoo.com - Thursday, July 21, 2016 - link
oh snap, that is some funny stuff right there