Intel's Xeon Cascade Lake vs. NVIDIA Turing: An Analysis in AI
by Johan De Gelas on July 29, 2019 8:30 AM ESTAnalyzing Intel's Cascade Lake in the New Era of AI
Wrapping things up, let’s take stock of the second-generation Xeon Scalable’s performance, and what it brings to the table in terms of features. With Cascade Lake Intel has improved performance by 3 to 6%, improved security, fixed some incredibly important bugs/exploits, added some SIMD instructions, and improved the overall server platform. This is nothing earth shattering, but you get more for the same price and power envelope, so what’s not to like?
That would be fine 5 years ago, when AMD did not have anything like the Zen(2) architecture, ARM vendors were still struggling with cores that offered painfully slow single-threaded performance, and deep learning was in the early stages. But this is not 2014, when Intel outperformed the nearest competition by a factor 3! Ultimately Cascade Lake delivers in areas where CPUs – and only CPUs – do well. But even with Intel’s DL Boost efforts, it’s not enough if the new chips have to go head-to-head with a GPU in a task the latter doesn’t completely suck at.
The reality is that Intel's datacenter group is under tremendous pressure from all sides, and the numbers are showing it. For the first time in years, the datacenter experienced a revenue drop, despite the fact that the overall server market is growing.
It is been going on for a while, but as we’ve experienced firsthand, machine learning-based AI applications are being rolled out successfully, and they are a game changer for both software and hardware. As a result, future server CPU reviews will never quite be the same: it is not Intel versus AMD or even ARM anymore, but NVIDIA too. NVIDIA is extremely successful in the deep learning market, and they are confident enough to take on Intel in areas where Intel dominated for years: HPC, machine learning, and even data processing. NVIDIA is ready to accelerate a much larger part of the data pipeline and a wider range of AI applications.
Features found in Intel Cascade Lake like DL Boost (VNNI) are the first attempts by Intel to push back – to cut away at the massive advantage that NVIDIA has in inference performance. Meanwhile, the next Xeon – Cooper Lake – will try to get closer to NVIDIA in training performance.
Moving on, when we saw this slide, we were gasping for air.
This slide boasting "leadership performance" also conveniently describes the markets where Intel is a very vulnerable position, despite Intel's current dominant position in the datacenter. Although the slide focuses on the Intel Xeon 9200, this could easily be a slide for the high-end Platinum 8200 Xeons too.
Intel points towards HPC, AI, and high-density infrastructure to sell their massively expensive Xeons. But as the market shifts towards less traditional business intelligence and more machine learning, and more GPU-accelerated HPC, the market for high end Xeons is shrinking. Intel has a very broad AI portfolio from Movidius (edge inference) to Nervana NNP (ASIC for DL training), and they’re going to need it to replace the Xeon in those segments where it falls out.
A midrange Xeon combined with a Nervana NNP coprocessor might work out well – and it would definitely be a better solution for most AI applications than a Xeon 9200. And the same is true for HPC: we are willing to bet that you are much better off with midrange Xeons and a fast NVIDIA GPU. And depending on where AMD's EPYC 2 pricing goes, even that might end up being debatable...
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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.
JohanAnandtech - Monday, July 29, 2019 - link
Thanks! Indeed, Nervana coprocessors are indeed Intel's most promising technology in this area.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.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 ...
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.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.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!
Eris_Floralia - Monday, July 29, 2019 - link
Still living in early 2000s?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.
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.