Intel Core i9-9900KS Ships in Oct., Cascade Lake-X Nearly Doubles Performance Per Dollar


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Intel made some product announcements at a pre-IFA event in Berlin this week, including news on the Core i9-9900KS that it announced earlier this summer and an upcoming product refresh for its Core X family. Intel has been pushed onto its proverbial heels by AMD’s 7nm onslaught, and it has yet to respond to those products in a significant way. These new parts should help do that, albeit at the high end of the market.

First, there’s the Core i9-9900KS. This CPU is a specially-binned Core i9-9900K, with the ability to hit 5GHz on all eight CPU cores, and a 4GHz base clock. That’s a 1.1x improvement over base clock on the 9900K, but the impact of the all-core 5GHz boost is harder to estimate. A sustained all-core 5GHz clock speed would be substantially higher than the Core i9-9900K we have here at ET — but Intel CPUsSEEAMAZON_ET_135 See Amazon ET commerce no longer hold their full clocks under sustained load. Our Core i9-9900K will turbo up to high clocks for 20-30 seconds, depending on the workload, before falling back to speeds in the lower 4GHz range when run on our Asus Z390 motherboard.

A faster Core i9 will undoubtedly improve Intel’s positioning against the Ryzen 7 and Ryzen 9 family,SEEAMAZON_ET_135 See Amazon ET commerce but even a chip that could hold an all-core 5GHz boost won’t catch the 12-core/24-thread Ryzen 9 3900X in most multi-threaded applications that can scale up to 12 cores. The gap between the two parts is too large to be closed in such a manner.

What the 9900KS will do for Intel, however, is give it a little more room to maneuver in gaming performance, which is where the company is making its stand. On the desktop side of things, Intel is facing a genuinely tough competitive situation, and even the advent of 10-core desktop CPUs may not solve the problem.

Cascade Lake May Meaningfully Respond to Threadripper

For the past two years, AMD has hammered Intel with high-performing, (relatively) low-cost workstation processors. Even though Intel’s Skylake X CPUs have often punched above their weight class compared with the Core family, AMD’s willingness to shove tons of cores into its chips has secured it the lead as far as performance/dollar, as well as the absolute performance lead in many well-threaded applications.

Intel may intend to challenge this in a far more serious way this year. The company showed the following slide at IFA:

The implication of this slide is that Intel will launch new Cascade X CPUs at substantially lower per-core prices than it has previously offered. We say “implication,” however, because technically this is a slide of performance per dollar, not price. Imagine two hypothetical CPUs, one with a price of $1,000 and performance of 1x, while the other chip costs $1,500 and has 2x the performance of the first chip. The second chip is 1.5x more expensive than the first but offers 1.33x more performance/dollar.

With AMD potentially eyeing Threadripper CPUs with up to 64 cores, however, Intel may not feel it has a choice. We haven’t heard from AMD on this point yet, so much is up in the air. There seems to be a battle brewing in these segments — hopefully, Intel will bring a much more price-competitive series of parts to market.

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Intel’s Cascade Lake With DL Boost Goes Head to Head with Nvidia’s Titan RTX in AI Tests


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For the past few years, Intel has talked up its Cascade Lake servers with DL Boost (also known as VNNI, Vector Neural Net Instructions). These new capabilities are a subset of AVX-512 and are intended to specifically accelerate CPU performance in AI applications. Historically, many AI applications have favored GPUs over CPUs. The architecture of GPUs — massively parallel processors with low single-thread performance — has been a much better fit for graphics processors rather than CPUs. CPUs offer far more execution resources per thread, but even today’s multi-core CPUs are dwarfed by the parallelism available in a high-end GPU core.

Anandtech has compared the performance of Cascade Lake, the Epyc 7601 (soon to be surpassed by AMD’s 7nm Rome CPUs, but still AMD’s leading server core today), and an RTX Titan. The article, by the excellent Johan De Gelas, discusses different types of neural nets beyond the CNNs (Convolutional Neural Networks) that are typically benchmarked, and how a key part of Intel’s strategy is to compete against Nvidia in workloads where GPUs are not as strong or cannot yet serve the emerging needs of the market due to constraints on memory capacity (GPUs still can’t match CPUs here), the use of ‘light’ AI models that don’t require long training times, or AI models that depend on non-neural network statistical models.

Growing data center revenue is a critical component of Intel’s overall push into AI and machine learning. Nvidia, meanwhile, is keen to protect a market that it currently competes in virtually alone. Intel’s AI strategy is broad and encompasses multiple products, from Movidius and Nervana to DL Boost on Xeon, to the upcoming Xe line of GPUs. Nvidia is seeking to show that GPUs can be used to handle AI calculations in a broader range of workloads. Intel is building new AI capabilities into existing products, fielding new hardware that it hopes will impact the market, and trying to build its first serious GPU to challenge the work AMD and Nvidia do across the consumer space.

What Anandtech’s benchmarks show, in aggregate, is that the gulf between Intel and Nvidia remains wide — even with DL Boost. This graph of a Recurrent Neural Network test used a “Long Short-Term Memory (LSTM) network as neural network. A type of RNN, LSTM selectively “remembers” patterns over a certain duration of time.” Anandtech also used three different configurations to test it — out-of-the-box Tensorflow with conda, an Intel-optimized Tensorflow with PyPi, and a version of Tensorflow optimized from-source using Bazel, using the very latest version of Tensorflow.

Image by Anandtech

 

Image by Anandtech

 

This pair of images captures relative scaling between the CPUs as well as the comparison against the RTX Titan. Out of the box performance was quite poor on AMD, though it improved with the optimized code. Intel’s performance shot up like a rocket when the source-optimized version was tested, but even the source-optimized version didn’t match Titan RTX performance very well. De Gelas notes: “Secondly, we were quite amazed that our Titan RTX was less than 3 times faster than our dual Xeon setup,” which tells you something about how these comparisons run within the larger article.

DL Boost isn’t enough to close the gap between Intel and Nvidia, but in fairness, it probably was never supposed to be. Intel’s goal here is to improve AI performance enough on Xeon to make running these workloads plausible on servers that will be mostly used for other things, or when building AI models that don’t fit within the constraints of a modern GPU. The company’s longer-term goal is to compete in the AI market with a range of equipment, not just Xeons. With Xe not quite ready yet, competing in the HPC space right now means competing with Xeon.

For those of you wondering about AMD, AMD isn’t really talking about running AI workloads on Epyc CPUs, but has focused on its RocM initiative for running CUDA code on OpenCL. AMD does not talk about this side of its business very much, but Nvidia dominates the market for AI and HPC GPU applications. Both AMD and Intel want a piece of the space. Right now, both appear to be fighting uphill to claim one.

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