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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  • Bp_968 - Tuesday, July 30, 2019 - link

    Oh no, not 8 million, 8 *billion* (for the 8180 xeon), and 19.2 *billion* for the last gen AMD 32 core epyc! I don't think they have released much info on the new epyc yet buy its safe to assume its going to be 36-40 billion! (I dont know how many transistors are used in the I/O controller).

    And like you said, the connections are crazy! The xeon has a 5903 BGA connection so it doesn't even socket, its soldered to the board.
    Reply
  • ozzuneoj86 - Sunday, August 04, 2019 - link

    Doh! Thanks for correcting the typo!

    Yes, 8 BILLION... it's incredible! It's even more difficult to fathom that these things, with billions of "things" in such a small area are nowhere near as complex or versatile as a similarly sized living organism.
    Reply
  • s.yu - Sunday, August 04, 2019 - link

    Well the current magnetic storage is far from the storage density of DNA, in this sense. Reply
  • FunBunny2 - Monday, July 29, 2019 - link

    "As a single SQL query is nowhere near as parallel as Neural Networks – in many cases they are 100% sequential "

    hogwash. SQL, or rather the RM which it purports to implement, is embarrassingly parallel; these are set operations which care not a fig for order. the folks who write SQL engines, OTOH, are still stuck in C land. with SSD seq processing so much faster than HDD, app developers are reverting to 60s tape processing methods. good for them.
    Reply
  • bobhumplick - Tuesday, July 30, 2019 - link

    so cpus will become more gpu like and gpus will become more cpu like. you got your avx in my cuda core. no, you got your cuda core in my avx......mmmmmm Reply
  • bobhumplick - Tuesday, July 30, 2019 - link

    intel need to get those gpus out quick Reply
  • Amiba Gelos - Tuesday, July 30, 2019 - link

    LSTM in 2019?
    At least try GRU or transformer instead.
    LSTM is notorious for its non-parallelizablity, skewing the result toward cpu.
    Reply
  • Rudde - Tuesday, July 30, 2019 - link

    I believe that's why they benchmarked LSTM. They benchmarked gpu stronghold CNNs to show great gpu performance and benchmarked LSTM to show great cpu performance. Reply
  • Amiba Gelos - Tuesday, July 30, 2019 - link

    Recommendation pipeline already demonstrates the necessity of good cpus for ML.
    Imho benching LSTM to showcase cpu perf is misleading. It is slow, performing equally or worse than alts, and got replaced by transformer and cnn in NMT and NLP.
    Heck why not wavenet? That's real world app.
    I bet cpu would perform even "better" lol.
    Reply
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