Intel Announces Cooper Lake, Advances AI Strategy

By Tiffany Trader

August 9, 2018

Intel’s chief datacenter exec Navin Shenoy kicked off the company’s Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel’s datacenter strategy, encompassing a number of product and technology updates, including another 14nm Xeon kicker, called Cooper Lake.

The headline-stealing announcement was Intel’s chip business hitting $1 billion in artificial intelligence revenue in 2017. The bulk of that comes from inferencing workloads, Intel indicated, noting that FGPAs and IoT are not included in the total figure. The company sees that AI opportunity growing to $10 billion by 2022.

Source: Intel, compiled using “an amalgamation of analyst data and Intel analysis, based upon current expectations and available information.”

“There’s going to be a portion [of that total addressable market] that’s strictly training and a portion that’s strictly inference,” said Intel’s head of AI products Naveen Rao. “However, there are new paradigms emerging and you can see that line blurring. We expect that reinforcement learning is going to start coming on the scene in a strong way, combined with simulation capabilities, transfer learning and hybrid models. There’s a future where learning will be distributed from end point to edge to cloud.”

According to Intel’s math and its assessment of AI TAMs, the company has captured the lion’s share of the inferencing market. Nvidia may disagree.

Reached for comment, industry analyst Patrick Moorhead, president and principal analyst, Moor Insights & Strategy, said, “On the AI front, watching Intel’s event you would think Intel is a major player in the AI industry. But I have to say, I don’t hear much about Intel in AI from anyone other than Intel. Yes, $1B is $1B. But would you also say that Nvidia really is the much more the dominant force? And can we also take from today’s event that Intel didn’t make claims to being a strong player on the training side of the AI equation?”

Based on the growth of AI and analytics and its “comprehensive and consistent” IP portfolio, Intel said it is revising its total datacenter business TAM from $160 billion in 2021 to $200 billion in 2022. This is the biggest opportunity in the history of the company, said Shenoy.

With the 10nm Ice Lake delayed until 2020, Intel is continuing to expand and extend the 14nm generation. “We’re making process improvements, we’re adding architectural advancements and we’ll continue to push on the software front as well,” said Shenoy.

In laying out its Xeon roadmap, the company announced the addition of the 14nm Cooper Lake targeting a late-2019 launch. “We have created a flexible, feature-rich platform that allows our customers to select the right CPU for their workloads that will support both a new 14nm CPU called Cooper Lake and the 10nm Ice Lake product,” said Shenoy. Cooper Lake will “generate and deliver a significantly better generation-on-generation performance improvement,” according to the exec.

Intel’s Navin Shenoy

The next-generation Xeon, codenamed Cascade Lake, is on track to ship in late 2018. Based on 14nm technology, Cascade Lake introduces a new AI extension to Xeon called Intel Deep Learning Boost (DL Boost) that extends the Intel AVX 512, adding a new vector neural network instruction (VNNI) that can handle INT8 convolutions with fewer instructions. A performance demonstration using a simulated version* of the future Cascade Lake with DL Boost achieved an average speedup of about 11x over Skylake, running Caffe ResNet-50, a popular AI workload for image classification.

Cascade Lake also debuts Intel Optane DC persistent memory and provides enhanced security features to address Spectre and Meltdown vulnerabilities.

The recently announced Intel Optane DC persistent memory is a new class of memory and storage that enables a large persistent memory tier between DRAM and SSDs. It’s capable of up to eight times the performance of configurations with DRAM only, according to Intel. The first production units of Optane persistent memory have shipped to Google and broad availability is planned for 2019.

The follow-on to Cascade Lake, Cooper Lake, debuts another new AI extension: bfloat16. Part of the DL Boost family, it leverages AI’s tolerance of lower precision and will principally be used for training kinds of workloads. “We are aggressively standardizing on bfloat16 and infusing it into all of our products in Xeon and our Network Neural Processor (NNP) family,” said Shenoy, “and so you can expect us […] to drive an aggressive push over the course of the second half of this year into 2019 and 2020.”

At its AI developer conference in San Francisco in May, Intel announced plans for the first commercial Nervana product, NNP L-1000 (codenamed Spring Crest), said to offer 3-4x the training performance of the development product, Lake Crest. Spring Crest is anticipated in late 2019, and Intel says it is also building a variant for the inference market, but is not ready to disclose any details.

Speaking on the competition for AI market share, Rao said that he wanted to cut through the myth that GPUs are the only thing out there for AI. “The reality is almost all inference in the world runs on Xeon today and the performance gap between general-purpose computing and specific kinds of computing like GPU is not some enormous gap like 100x, it’s more like 3x, and that’s okay,” he said. “Because general-purpose computing has a scale that a specific solution can’t really achieve. Everything has its place. Once AI starts making its way into general-purpose computing, we’ve achieved a scale with this new technology that we simply couldn’t before, so it’s incumbent on us to continually evolve our platform to make it the best that it can be for AI as well as everything else that Xeon does today.

“Xeon wasn’t well optimized [for AI] from a software perspective two years ago,” Rao added. “But just from the launch of Skylake in July of 2017, we’ve increased performance of inference by 5.4x*, and training by 1.4x*. We’ve added things like vector and matrix multiplication and SIMD instructions at the Skylake launch to continue gen-on-gen improvement. On Cascade Lake, we are adding DL Boost, this family of new capabilities, and we showed you [a projected] 11x improvement.”

The Intel event was broadly focused on datacenter strategy and AI, but it did not provide drill down into HPC-specific technology plans. While the Phi line has come to an end, Intel is counting on the successor to Phi for its exascale plans and it is on the hook to deliver an exascale, or at least an exaflops peak, system to Argonne Lab in 2021 (though a contract has not yet been inked). A leaked Intel roadmap that surfaced a couple weeks ago (via AnandTech) reveals that beyond Xeon Phi lies Cascade Lake-AP (AP=Advanced Processor), positioned to debut in the first half of 2019, followed by a “Next-Gen AP” slated for mid-2020.

The leaked slide also shows the 200 Gbps successor to Omni-Path, OPA 200, appearing in late 2019. Planned products include a 64-port top-of-rack switch, a 2,048-port director switch and a PCIe4x16 Host Fabric Interface (HFI) adapter. Intel, of course, does not comment on company information not disclosed through official channels.

*Configuration details provided by Intel

1.4x training throughput improvement in August 2018:
Configuration Details Tested by Intel as of measured August 2nd 2018. Processor: 2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total Memory 376.46GB (12slots / 32 GB / 2666 MHz). CentOS Linux-7.3.1611-Core kernel 3.10.0-693.11.6.el7.x86_64, SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , Deep Learning Framework Intel® Optimizations for caffe version:a3d5b022fe026e9092fc7abc7654b1162ab9940d Topology::resnet_50 BIOS:SE5C620.86B.00.01.0013.030920180427 MKLDNN: version: 464c268e544bae26f9b85a2acb9122c766a4c396 NoDataLayer. Measured: 123 imgs/sec vs Intel tested July 11th 2017 Platform: Platform: 2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables: KMP_AFFINITY=’granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time — forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.
5.4x inference throughput improvement in August 2018:
Tested by Intel as of measured July 26th 2018 :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total Memory 376.46GB (12slots / 32 GB / 2666 MHz). CentOS Linux7.3.1611-Core, kernel: 3.10.0-862.3.3.el7.x86_64, SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , Deep Learning Framework Intel® Optimized caffe version:a3d5b022fe026e9092fc7abc7654b1162ab9940d Topology::resnet_50_v1 BIOS:SE5C620.86B.00.01.0013.030920180427 MKLDNN: version:464c268e544bae26f9b85a2acb9122c766a4c396 instances: 2 instances socket:2 (Results on Intel® Xeon® Scalable Processor were measured running multiple instances of the framework. Methodology described here: https://software.intel.com/enus/articles/boosting-deep-learning-training-inference-performance-on-xeon-and-xeon-phi) NoDataLayer. Datatype: INT8 Batchsize=64 Measured: 1233.39 imgs/sec vs Tested by Intel as of July 11th 2017:2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables: KMP_AFFINITY=’granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time –forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (ResNet-50). Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.
11X inference thoughput improvement with CascadeLake:
Future Intel Xeon Scalable processor (codename Cascade Lake) results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual performance vs Tested by Intel as of July 11th 2017: 2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0- 514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables: KMP_AFFINITY=’granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time –forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (ResNet-50),. Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.
Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire