Inference: ResNet-50

After training your model on training data, the real test awaits. Your AI model should now be able to apply those learnings in the real world and do the same for new real-world data. That process is called inference. Inference requires no back propagation as the model is already trained – the model has already determined the weights. Inference also can make use of lower numerical precision, and it has been shown that even the accuracy from using 8-bit integers is sometimes acceptable. 

From a high-level workflow perfspective, a working AI model is basically controlled by a service that, in turn, is called from another software service. So the model should respond very quickly, but the total latency of the application will be determined by the different services. To cut a long story short: if inference performance is high enough, the perceived latency might shift to another software component. As a result, Intel's task is to make sure that Xeons can offer high enough inference performance. 

DL Inference: ResNet50

Intel has a special "recipe" for reaching top inference performance on the Cascade Lake, courtesy of the DL Boost technology. DLBoost includes the Vector Neural Network Instructions, which allows the use of INT8 ops instead of FP32. Integer operations are intrinsically faster, and by using only 8 bits, you get a theoretical peak, which is four times higher. 

Complicating matters, we were experimenting with inference when our Cascade Lake server crashed. For what it is worth, we never reached more than 2000 images per second. But since we could not experiment any further, we gave Intel the benefit of the doubt and used their numbers.

Meanwhile the publication of the 9282 caused quite a stir, as Intel claimed that the latest Xeons outperformed NVIDIA's flagship accelerator (Tesla V100) by a small margin: 7844 vs 7636 images per second. NVIDIA reacted immediately by emphasizing performance/watt/dollar and got a lot of coverage in the press. However, the most important point in our humble opinion is that the Tesla V100 results are not comparable, as those 7600 images per second were obtained in mixed mode (FP32/16) and not INT8.

Once we enable INT8, the $2500 Titan RTX is no less than 3 times faster than a pair of $10k Xeons 8280s.

Intel cannot win this fight, not by a long shot. Still, Intel's efforts and NIVIDA’s poking in response show how important it is for Intel to improve both inference and training performance; to convince people to invest in high end Xeons instead of a low end Xeon with a Tesla V100. In some cases, 3 times slower than NVIDIA's offering might be good enough as the inference software component is just one part of the software stack. 

In fact, to really analyze all of the angles of the situation, we should also measure the latency on a full-blown AI application instead of just measuring inference throughput. But that will take us some more time to get that one right....

Recurrent Neural Networks: LSTM Exploring Parallel HPC
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  • Drumsticks - Monday, July 29, 2019 - link

    It's an interesting, valuable take on the challenges of responding to many of the ML workloads of today with a general purpose CPU, thanks! A third party review of Intel's latest against Nvidia, and even throwing AMD in to the mix, is pretty helpful as the two companies have been going at it for a while now.

    Intel has a lot of stuff going that should make the next few years quite interesting. If they manage to follow through on the Nervana Coprocessor/NNP-I that Toms talked about, or on their discrete GPUs, they'll have a potent lineup. The execution definitely isn't guaranteed, especially given the software reliance these products will have, but if Intel really can manage to transform their product stack, and do it in the next few years, they'll be well on their way to competing in a much larger market, and defending their current one.

    OTOH, if they fail with all of them, it'll definitely be bad news for their future. They obviously won't go bankrupt (they'll continue to be larger than AMD for the foreseeable future), but it'll be exponentially harder if not impossible to get back into those markets they missed.
    Reply
  • JohanAnandtech - Monday, July 29, 2019 - link

    Thanks! Indeed, Nervana coprocessors are indeed Intel's most promising technology in this area. Reply
  • p1esk - Monday, July 29, 2019 - link

    No one in their right mind would think "gee, should I get CPU or GPU for my DL app?" More concerning for Intel should be the fact that I bought a Threadripper for my latest DL build. Reply
  • Smell This - Monday, July 29, 2019 - link

    You gotta Radeon VII ?

    I'm thinking Intel, and to a lesser extent, nVidia, is waiting for the next shoe(s) to drop in **Big Compute** --- Cascade Lake has been left at the starting gate.

    An AMD Radeon Instinct 'cluster' on a dense specialized 'chiplet' server with hundreds of CPU cores/threads is where this train is headed ...
    Reply
  • JohanAnandtech - Monday, July 29, 2019 - link

    Spinning up a GPU based instance on Amazon is much more expensive than a CPU one. So for development purposes, this question is asked. Reply
  • p1esk - Tuesday, July 30, 2019 - link

    Then you should be answering precisely that question: which instance should I spin up? Your article does not help with that because the CPU you test is more expensive than the GPU. Reply
  • JohnnyClueless - Monday, July 29, 2019 - link

    Really surprised Intel, and to a lesser extent AMD, are even trying to fight this battle with nVidia on these terms. It’s a lot like going to a gun fight and developing an extra sharp samurai sword rather than bringing the usual switchblade knife. The sword may be awesome, but it’s always going to be the wrong tool for the gun fight.

    IMO, a better approach to capture market share in DL/AI/HPC might be to develop a low core count (by 2019 standards) CPU that excelled at sequential single threaded performance. Something like 6-10 GHz. That would provide a huge and tangible boost to any workload that is at least partially single core frequency limited, and that is most DL/AI/HPC workloads. Leave the parallel computing to chips and devices designed to excel at such workloads!
    Reply
  • Eris_Floralia - Monday, July 29, 2019 - link

    Still living in early 2000s? Reply
  • FunBunny2 - Monday, July 29, 2019 - link

    "Something like 6-10 GHz. "

    IIRC, all the chip tried to get near that, but couldn't. it's not nice to fool Mother Nature.
    Reply
  • Santoval - Monday, July 29, 2019 - link

    "Something like 6-10 GHz."
    Google "Dennard scaling" (which ended in ~2005) to find out why this is impossible, at least with silicon based MOSFET transistors (including the GAA-FET based ones of the next decade). Wikipedia has a very informative page with multiple links to various sources for even more. The gist of the end of Dennard scaling is that single core clocks higher than ~5 GHz (at a reasonable TDP of up to ~100W) are explicitly forbidden at *any* node.

    When Dennard scaling ended -in combination with the slowing down of Moore's Law- there was another, related consequence : Koomey's law started to slow down. Koomey's law is all about power efficiency, i.e. how many computations you can extract from each Wh or kWh.

    Before the early 2000s the number of computations per x unit of energy doubled on average every 1.57 years. In 2011 Koomey himself re-evaluated his law and got an average doubling of computations every 2.6 years for the previous decade, a substantial collapse of power efficiency. Since 2011 Koomey's law has obviously slowed down further.

    To make a long story short Moore's law puts a limit to the number of transistors we can fit in each mm^2, and that limit is not too far away. Dennard scaling once allowed us to raise clocks with each new node at the same TDP, and this is ancient history in computing terms. Koomey's law, finally, puts a limit to the power efficiency of our CPUs/GPUs, and this continues to slow down due to the slowing down of Moore's Law (when Moore's Law ends Koomey's law will also end, thus all three fundamental computing laws will be "dead").

    Unless we ditch silicon (and even CMOS transistors, if required) and adopt a new computing paradigm we will have neither 6 - 10 GHz clocked CPUs in a couple of decades nor will we able to speed up CPUs, GPUs and computers at all.
    Reply

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