Designing Denver

Diving into the depths of Denver, Denver is in a lot of ways exactly the kind of CPU you’d expect a GPU company to build. NVIDIA’s traditional engineering specialty is in building wide arrays of simple in-order processors, a scheme that maps well to the ridiculously parallel nature of graphics. Whether intentional to tap their existing expertise or just a result of their plan to go in such a divergent route from “traditional” CPUs, Denver makes you stop and ponder GPUs for a moment when looking at its execution workflow.

The results of NVIDIA’s labors in designing Denver has been a wide but in-order processor. With the potential to retire up to 7 operations per cycle, Denver measured front-to-back is wider than A15/A57 and wider than Cyclone. Officially NVIDIA calls this a “7+” IPC architecture, alluding to Denver’s binary translation and code optimization step, and the potential to merge operations as part of the process.

Meanwhile the existence of this code optimizer is the first sign we see that Denver is not a traditional CPU by the standards of ARM/Apple or Intel/AMD. To understand why that is we must first discuss Out of Order Execution (OoOE), why it exists, and why Denver doesn’t have it.

In traditional CPU designs, we make a distinction between in-order designs and out-of-order designs. As appropriately named, in-order designs will execute instructions in the order they receive them, and meanwhile out-of-order designs have the ability to rearrange instructions within a limited window, so long as the altered order doesn’t change the results. For the kinds of tasks that CPUs work with, OoOE improves throughput, but it does come at a cost.

Overall OoOE is considered the next logical step after in-order execution has reached its natural limits. Superscalar in-order execution can potentially scale up to a few instructions at once, but actually achieving that is rare, even with the help of good compilers. At some point other constraints such as memory accesses prevent an instruction from executing, holding up the entire program. In practice once you need performance exceeding a traditional in-order design, then you switch to out-of-order. With OoOE then it becomes possible to scale performance out further, with the ability to use the reodering process to fill wider processors and to keep from losing performance due to stalls.

K1-64 Die Shot Mock-up (NVIDIA)

The cost of OoOE is complexity, die size, and power consumption. The engines to enable OoOE can be quite large, being tasked with queuing instructions, identifying which instructions can be reordered, and ensuring instructions are safe to execute out-of-order. Similarly, there is a power cost to these engines, and that means adding OoOE to a processor can make it much larger and more power hungry, even without actually adding further units for the OoOE engines to fill. Make no mistake, the benefits of OoOE are quite large, but then so is the cost of implementing it.

As such, while OoOE has been treated as the next step after in-order processors it is not the only solution to the problem being pursued. The fundamental problems in-order processors face are a combination of hardware and software; hardware issues such as memory stalls, and software issues such as poor instruction ordering. It stands to reason then that if the performance scaling problem can be solved in hardware with OoOE, then can it be solved in software as well? It’s this school of thought that NVIDIA is pursuing in Denver.

Perhaps the critical point in understanding Denver then is that it is non-traditional for a high-performance CPU due to its lack of OoOE hardware, and for that reason it’s a CPU unlike any of its contemporaries. We’ll get back to the software aspects of Denver in a bit, but for now it’s enough to understand why NVIDIA has not pursued an OoOE design and what they have pursued instead.

Denver’s Deep Details

Due to NVIDIA’s choice not to pursue OoOE on Denver and simultaneously pursue a large, high performance core, Denver is by consumer standards a very wide CPU. With no OoOE hardware NVIDIA has been able to fill out Denver with execution units, with 7 slots’ worth of execution units backed by a native decoder wide enough to feed all of those units at once. The native decoder in particular is quite notable here, as most other CPU designs have narrower decoders that put a lower limit on their theoretical IPC. The Cortex-A15 cores in Tegra K1-32 for example only feature 3-wide decoders, despite having many more slots’ worth of execution units. Consequently a large decoder not only opens up the ability to increase IPC, but it is a sign that the CPU developer believes that their design is capable of keeping that many execution units busy enough to justify the cost of the wider decoder.

NVIDIA CPU Core Comparison
  K1-32 K1-64
CPU Cortex-A15 NVIDIA Denver
ARM ISA ARMv7 (32-bit) ARMv8 (32/64-bit)
Issue Width 3 micro-ops 2 (ARM) or 7 (Native) micro-ops
Pipeline Length 18 stages 15 stages
Branch Mispredict Penalty 15 cycles 13 cycles
Integer ALUs 2 4
Load/Store Units 1 + 1 (Dedicated L/S) 2 (Shared L/S)
Branch Units 1 1
FP/NEON ALUs 2x64-bit 2x128-bit
L1 Cache 32KB I$ + 32KB D$ 128KB I$ + 64KB D$
L2 Cache 2MB 2MB

These execution units themselves are fairly unremarkable, but none the less are very much at the heart of Denver. Compared again to Terga 4, there are twice as many load/store units, and the NEON units have been extended from 64-bits wide to 128-bits wide, allowing them to retire up to twice as much work per cycle if they can be completely filled.

Internally Denver executes instructions using the Very Long Instruction Word (VLIW) format, which is an instruction format that these days is more common with GPUs than it is CPUs, making it another vaguely GPU-like aspect of Denver. In VLIW all instructions are packed into a single word and sent through the pipeline at once, rather than handing each slot its own instruction. Each VLIW instruction is variable in length, and in turn the length of the operation is similarly variable, depending in part on factors such as the number of registers any given instruction operates upon. With a maximum VLIW instruction size of 32 bytes, this means that the number of operations a single instruction can contain is dependent on the operations, and it’s possible for large operations to fill out the VLIW early.

Another one of Denver’s unusual aspects is its internal instruction format, which is very different from ARMv7 or ARMv8. Though the specific format is beyond the scope of this article, it has long been rumored that Denver was originally meant to be an x86 design, with Denver’s underlying design and binary translation pairing intended to allow for an x86 implementation without infringing on any x86 hardware patents. Whether that is true or not, the end result of Denver is that owing to NVIDIA’s decision to solve their needs in software, NVIDIA was able to create an architecture whose design is decoupled from the actual instruction set it is executing.

Yet in spite of this architectural choice, Denver still needs to be able to execute ARM code as well as native code from binary translation, which leads to one more interesting wrinkle to Denver’s design. Denver has not one but two decoders, the native decoder and a proper ARM decoder. Designed to work in situations where Denver’s software optimizer is not worth running or can’t translate in time – such as with brand new code segments – the ARM decoder allows for Denver to directly decode ARM instructions.

The ARM decoder is not quite a backup, but it is not intended to be the main source of operations for Denver over the long run. Rather the bulk of the work for Denver should come from its binary translator, and only a small fraction of infrequently used code should hit the ARM decoder. At only 2 instructions wide this decoder is narrower than even A15’s decoder, not to mention it forms an entirely in-order pipeline that misses out on the instruction rescheduling and other optimizing benefits of the software code optimizer. Never the less it serves an important role in situations where Denver can’t use native code by giving it a means to immediately begin executing ARM code. This as a result makes Denver a kind of hybrid design, capable of executing either ARM instructions or NVIDIA’s own internal microcode.

Meanwhile Denver’s overall pipeline stands at 15 stages deep. Despite the overall width of Denver this actually makes the pipeline shorter than the 18 stage A15 by a few stages. And similarly, the penalty for branch mispredictions is down from 15 cycles in A15 to 13 cycles in Denver.

Last but not least, on the logical level NVIDIA has also been working to further reduce their power consumption through a new mode called CC4. CC4 is essentially a deeper state of sleep that’s not quite power-gating the entire CPU, but none the less results in most of the CPU being shut off. What ends up being retained in CC4 is the cache and what NVIDIA dubs the “architectural state” of the processor, a minimal set of hardware that allows the core voltage to drop below traditional Vmin and instead hold at just enough voltage to retain the contents of the cache and state, as no work needs to be done in this state. It's worth noting that we've seen similar power collapse states as far back as the A15 though, so the idea isn't necessarily new.

CC4 as a result is intended to be a relatively fast sleep state given its depth, with Denver able to enter and exit it faster than power-gating, and consequently it can be used more frequently. That said since it is deeper than other sleep states it is also slower than them, meaning the CPUIdle governor needs to take this into account and only select CC4 when there’s enough time to take advantage of it. Otherwise if Denver enters CC4 and has to come out of it too soon, the processor can end up wasting more power setting up CC4 than a very short CC4 duration would save.

Of course CC4 is just one of many factors in Denver’s power consumption. Hardware and software alike plays a role, from the silicon itself and the leakage characteristics of the physical transistors to the binary translation layer necessary for Denver to operate at its peak. And that brings us to the final and more crucial piece of the Denver puzzle: the binary translation layer.


SoC Architecture: NVIDIA's Denver CPU The Secret of Denver: Binary Translation & Code Optimization


View All Comments

  • dtgoodwin - Wednesday, February 4, 2015 - link

    I really appreciate the depth that this article has, however, I wonder if it would have been better to separate the in depth CPU analysis for a separate article. I will probably never remember to come back to the Nexus 9 review if I want to remember a specific detail about that CPU. Reply
  • nevertell - Wednesday, February 4, 2015 - link

    Has nVidia exposed that they would provide a static version of the DCO so that app developers would be able to optimize their binaries at compile time ? Or do these optimizations rely on the program state when they are being executed ? From a pure academic point of view, it would be interesting to see the overhead introduced by the DCO when comparing previously optimized code without the DCO running and running the SoC as was intended. Reply
  • Impulses - Wednesday, February 4, 2015 - link

    Nice in depth review as always, came a little late for me (I purchased one to gift it, which I ironically haven't done since the birthday is this month) but didn't really change much as far as my decision so it's all good...

    I think the last remark nails it, had the price point being just a little lower most of the minor QC issues wouldn't have been blown up...

    I don't know if $300 for 16GB was feasible (pretty much the price point of the smaller Shield), but $350 certainly was and Amazon was selling it for that much all thru Nov-Dec which is bizarre since Google never discounted it themselves.

    I think they should've just done a single $350-400 32GB SKU, saved themselves a lot of trouble and people would've applauded the move (and probably whined for a 64GB but you can't please everyone). Or a combo deal with the keyboard, which HTC was selling at 50% at one point anyway.
  • Impulses - Wednesday, February 4, 2015 - link

    No keyboard review btw? Reply
  • JoshHo - Thursday, February 5, 2015 - link

    We did not receive the keyboard folio for review. Reply
  • treecats - Wednesday, February 4, 2015 - link

    Where is the comparison to NEXUS 10????

    Maybe because Nexus 10's battery life is crap after 1 year of use!!!

    Please come back review it again when you used it for a year.
  • treecats - Wednesday, February 4, 2015 - link

    My previously holds true for all the Nexus device line I own.

    I had Nexus 4,

    currently have Nexus 5, and Nexus 10. All the Nexus devices I own have bad battery life after 1 year of use.

    Google, fix the battery problem.
  • blzd - Friday, February 6, 2015 - link

    That tells me you are mistreating your batteries. You think it's coincidence that it's happening to all your devices? Do you know how easy it is for batteries to degrade when over heating? Do you know every battery is rated for a certain number of charges only?

    Mostly you want to avoid heat, especially while charging. Gaming while charging? That's killing the battery. GPS navigation while charging? Again, degrading the battery.

    Each time you discharge and charge the battery you are using one of it's charge cycles. So if you use the device a lot and charge it multiple times a day you will notice degradation after a year. This is not unique to Google devices.
  • grave00 - Sunday, February 8, 2015 - link

    I don't think you have the latest info on how battery charging vs battery life works. Reply
  • hstewartanand - Wednesday, February 4, 2015 - link

    Even though I personal have 6 tablets ( 2 iPads, 2 Windows 8.1 and 2 android ) and as developer I find them technically inferior to Actual PC - except for Windows 8.1 Surface Pro.

    I recently purchase an Lenovo y50 with i7 4700 - because I desired AVX 2 video processing. To me ARM based platforms will never replace PC devices for certain applications - like Video processing and 3d graphics work.

    I am big fan of Nvidia GPU's but don't care much for ARM cpus - I do like the completion that it given to Intel to produce low power CPU's for this market

    What I really like to see is a true technical bench mark that compare the true power of cpus from ARM and Intel and rank them. This includes using extended instructions like AVX 2 on Intel cpus.

    Compared this with equivalent configured Nvidia GPU on Intel CPU - and I would say ARM has a very long way to go.

    But a lot depends on what you doing with the device. I am currently typing this on a 4+ year old Macbook Air - because it easy to do it and convenient. My other Windows 8.1 ( Lenovo 2 Mix 8 - Intel Adam Baytrail ) has roughly the same speed - but Macbook AIR is more convenient. My primary tablet is the Apple Mini with Retina screen, it is also convent for email and amazon and small stuff.

    The problem with some of bench marks - is that they maybe optimized for one platform more than another and dependent on OS components which may very between OS environments. So ideal the tests need to native compile for cpu / gpu combination and take advantage of hardware. I don't believe such a benchmark exists. Probably the best way to do this get developers interested in platforms to come up with contest for best score and have code open source - so no cheating. It would be interesting to see ranking of machines from tablets, phones, laptop and even high performance xeon machines. I also have an 8+ Year old dual Xeon 5160 Nvidia GTX 640 (best I can get on this old machine ) and I would bet it will blow away any of this ARM based tablets. Performance wise it a little less but close to my Lenovo y50 - if not doing VIDEO processing because of AVX 2 is such significant improvement.

    In summary it really hard to compare performance of ARM vs Intel machines. But this review had some technical information that brought me back to my older days when writing assembly code on OS - PC-MOS/386

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