Driving AI Forward with Intel® Xeon® Scalable Processors

July 12, 2017

AI compute cycles are expected to grow by a factor of 20 within the next three years as the intelligence revolution goes mainstream.  Intel is working to fuel this growth at every level, delivering a major leap in AI performance with new Intel® Xeon® Scalable processors and targeting a game-changing 100X increase in machine learning performance by 2020 with the Intel® Nervana™ Platform.

Figure 1. The Intel® Scalable System Framework simplifies the design of efficient, high-performing clusters that optimize the value of HPC investments.
Figure 1. The Intel® Scalable System Framework simplifies the design of efficient, high-performing clusters that optimize the value of HPC investments.

These leaps in performance will need to be accompanied by comparable leaps in scalability to support growing data volumes and larger neural networks. To address this need, Intel is driving innovation across the entire HPC solution stack through the Intel® Scalable System Framework (Intel® SSF). Tight integration and synchronized innovation across compute, memory, storage, fabric, and software will help organizations scale cost-effectively as their own AI revolution unfolds.

Superior Performance Today

Choosing the right processors for specific workloads is important. Current options include:The move toward faster, more scalable machine learning is already under way.  Intel is working with vendors and the open source community to optimize popular software frameworks and algorithms for higher performance on Intel architecture. The performance benefits can be transformative[1].

  • Intel® Xeon® Scalable processors for inference engines and for some training workloads. Intel Xeon processors already support 97 percent of all AI workloads[2]. With more cores, more memory bandwidth, an option for integrated fabric controllers, and ultra-wide 512-bit vector support, the new Intel Xeon Scalable processors (formerly code name Skylake) provide major advances in performance, scalability, and flexibility. They are ideal for deploying AI inference engines at scale and, in many cases, for tackling the heavier demands of neural network training. With their broad interoperability, they also provide a powerful and agile foundation for integrating AI solutions into other business and technical applications.
  • Intel® Xeon Phi™ processors for training large and complex neural networks. With up to 72 cores, 288 threads, and 512-bit vector support—plus integrated high bandwidth memory and fabric controllers—these processors offer performance and scalability advantages versus GPUs for neural network training. Since they function as host processors and run standard x86 code, they simplify implementation and eliminate the inherent latencies of PCIe-connected accelerator cards. An AI-optimized upgrade to the Intel Xeon Phi processor family (code name Knights Mill) will be in production in the fourth quarter of 2017, and is expected to provide a significant increase in deep learning training performance to help unleash a new wave of innovation.
  • Optional accelerators for agile infrastructure-optimization. Intel offers a range of workload-specific accelerators, including programmable Intel FPGAs that can evolve along with workloads to meet changing requirements. These optional add-ons for Intel Xeon processor-based servers bring new flexibility and efficiency for supporting AI and many other critical workloads. They open new doors for innovation, and can help organizations reduce data center power and space requirements for their most demanding workloads.

Unprecedented Performance Tomorrow

If today’s Intel processors push the boundaries of machine learning performance, tomorrow’s will shatter them. The Intel Nervana Platform is a complete solution stack that is designed for the sole purpose of delivering unprecedented performance and density for neural networks. High-speed memory and powerful interconnects are built into each chip to deliver extreme performance that can be scaled across multiple chips and multiple chassis without performance loss.

At the same time, Intel SSF is evolving to help enable cutting-edge performance at every scale, from small workgroup clusters to the world’s largest supercomputers. In combination with rapid, ongoing advances in the x86 software ecosystem—including applications, frameworks, and optimized Intel libraries—this ramp up in computing capability will provide the foundation for a tidal wave of AI innovation. In relatively short order, this groundbreaking new technology will become a mainstream resource that can be deployed with confidence by virtually every organization.

Learn more: Read previous and future articles about the benefits Intel SSF brings to AI through synchronized innovation across the complete HPC solution stack:  Overview, memory, compute, fabric, storage, software.

[1] For more information on the value of running optimized AI software on Intel Architecture, read the Intel article: “Intel® Xeon Phi™ Delivers Competitive Performance for Deep Learning—And Getting Better Fast,” September 26, 2016. https://software.intel.com/en-us/articles/intel-xeon-phi-delivers-competitive-performance-for-deep-learning-and-getting-better-fast

[2] Based on Intel internal estimates.

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!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

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

March 18, 2024

Nvidia's latest and fastest GPU, codenamed 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…

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…

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…

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…

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

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…

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…

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…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire