What Knights Landing Is Not

By James Reinders, Intel

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion.

I have found that everything is clear, when we really understand that Knights Landing is an Intel processor. That makes it NOT Knights Corner. That makes it NOT a GPU. That makes it NOT a PCIe limited accelerator. That makes it NOT force large, new, and unique investments in software programming.

Perhaps everything is most clear when we discuss what it is and what it is not.

Knights Landing is NOT Knights Corner

Knights Corner, the first Intel Xeon Phi product, was a coprocessor. Knights Corner has been extraordinarily successful powering many of the world’s fastest computers. Nevertheless, Knights Corner required a hot processor, shuffling of data over the PCIe bus, and often the use of offload-style programming due to limited memory capacity and strong Amdahl’s Law effects when running less parallel code.

Knights Landing, as a processor, is a very easy upgrade for a Knights Corner user. Applications that used Knights Corner run even better on Knights Landing. Even bigger news is this: applications which were never able to adapt to the limitations of Knights Corner (or offloading to GPUs for that matter) will find Knights Landing an exciting option.


Knights Landing is NOT a GPU (neither was Knights Corner)

Knights Landing is a full-fledged, highly scalable, Intel processor. This processor can reach unprecedented levels of performance and parallelism, without giving up programmability. You can use the same parallel programming models, the same tools, and the same binaries that run today on other Intel processors.

Programming languages that work for processors, just work for Knights Landing too. Programming models, like OpenMP, MPI and TBB, just work for Knights Landing also.

Restrictive models tailored for GPUs, including kernel programming in CUDA and OpenCL, do not apply to processors (and I’m not talking just about Intel processors). We do not need them, because we have the full richness and portability of processor programming models fully available on Knights Landing.

Knights Landing is NOT going to invalidate prior processor coding efforts

It’s Knights Landing that really brings us home. It’s a full processor from Intel, one that happens to have up to 72 cores. It has an unprecedented ability to perform on highly parallel programs while being compatible with the tools and programming models common to Intel processors.

One of the first things I did when I initially logged on to a Knights Landing machine was to type in “yum install emacs.” I’m sure that whoever built that emacs binary had never heard of Knights Landing. It worked and I was happy to have the power of emacs so as to no be slowed by the primitive “vi.” I am so happy that software just runs, without a recompilation needed. No need to do something weird with Knights Landing to use it with your favorite software. It’s just like any other processor from Intel in that respect! It can run anything you would expect a processor to run: C, C++, Fortran, Python, and much more. It really is a full processor!

We think that parallel programming is challenging enough. That’s why we took a different approach compared to other device designs – especially GPUs. Our goal has been to deliver never before attainable processor performance while remaining compatible with existing software and tools. It’s quite an accomplishment.

Reinders-KNL-FullCover
Front jacket for “Intel Xeon Phi Processor High Performance Programming, Knights Landing Edition” by James Reinders, Jim Jeffers and Avinash Sodani

Knights Landing is NOT inflexible

When we are considering the design for a new computer, we ask a variety of basic questions, consider options, make choices, and bake a set of choices into a design. In the past, when the topic of using high bandwidth memory came up, there as always a debate: should we make it a cache or should we make it a scratchpad memory? And that, of course, depends to a certain extent on whether your application is cache-friendly – and most are – or if it’s one of those apps that is not cache-friendly and you think you can do better with scratch pad memory. Previously, we generally had to design the computer choosing one approach or the other and then live with the decision. With Knights Landing, we offer choices which make Knights Landing amazingly versatile.

Knights Landing integrates high bandwidth memory known as MCDRAM which greatly enhances performance.

Unprecedented configurability allows it to be operated in different “memory modes.” MCDRAM can either be treated as a high bandwidth memory-side cache, or it can be identified as high bandwidth memory, or a little of each. Knights Landing also supports different “cluster modes,” allowing it to behave as a cluster with one, two or four NUMA domains.

Reinders-KNL-Chapter17
Source: “Intel Xeon Phi Processor High Performance Programming, Knights Landing Edition,” 2016; used with permission – click to enlarge

As Jim Jeffers and I say in our book on Knights Landing, “Knights Landing offers an unprecedented variety of configurations which have traditionally been available only as hardwired and unchangeable design decisions. Specifically, the choices realized by the cluster modes and the memory modes. This wide ranging support allows Knights Landing to act like very different machines based on the configuration used to initialize the CPU, the operating system, and then the applications.” This means that Knights Landing can be adapted to fit application needs.

Knights Landing is NOT limited by small memory and offloading

Knights Landing processors support up to 384 GB DDR using 6 channels (~90GBs sustained bandwidth) memory and do not require applying offload constructs to hot spots because an entire application will run on the processor itself.

Reinders-KNL-Chapter22
Source: “Intel Xeon Phi Processor High Performance Programming, Knights Landing Edition,” 2016; used with permission – click to enlarge

Consider the weather forecasting program called WRF (Weather Research and Forecasting). It does not have just a few hot spots where it does all its computations – instead it has a huge number of algorithms used to solve different problems. There are many parts of the application that you would like to run very fast, especially the particularly complex algorithms. Since it all runs on Knights Landing, we’ve seen very nice results, which I have documented in chapter 22 of the new Knights Landing book, coupled with the ease of using the same code as we would on any processor. Programs like this are essentially an insurmountable challenge for a GPU or coprocessor.

Machine learning and data analytics will receive a boost from the introduction of Knights Landing2 . Both tend to apply computational models to large datasets – the constraints have always been the amount of data you can handle given the computational power available to you. Knights Landing is a highly scalable, highly parallel device that is well suited to handle large, complex computations. Because it is a processor rather than a coprocessor, the Intel Xeon Phi technology provides you with more access to your data. Best of all you are working with an on-package, very large processor-sized memory without the limits of any offload device (coprocessor or GPUs).

Reinders-KNL-Chapter24
Source: “Intel Xeon Phi Processor High Performance Programming, Knights Landing Edition,” 2016; used with permission – click to enlarge

The same holds true for visualization applications – Knights Landing provides a new level of flexibility for these kinds of highly specialized, data intensive workloads. Many people are surprised that Knights Landing can consistently beat the leading GPUs in visualization benchmarks 1 . But this is really not surprising when you consider that a GPU has a hard coded graphics pipeline, which is quite inflexible. Knights Landing, being a processor, has none of those constraints. Plus, you don’t wind up shipping massive amounts of data across the PCIe bus; the data is stored in on-package memory and is available for immediate processing.

Moving Toward Exascale

I think we can safely predict a long and happy life for the evolving Intel Xeon Phi processor family, which includes Knights Landing and all its descendants. Odds are that these next generation processors will play a major role in meeting one of HPC’s most exciting grand challenges – the realization of exascale.

Los Alamos National Laboratory’s Trinity supercomputer and the Cori supercomputer from NERSC are pre-exascale systems that will be operational in 2016. Both are powered by Knights Landing and are proof that double-digit petascale performance and the development of exascale machines are attainable without the use of attached accelerators or coprocessors.

And that’s why we emphasize that Knights Landing is a processor – a full-featured, extraordinarily powerful, highly parallel CPU – not a coprocessor or accelerator. It’s a major milestone on the road to exascale and an exciting new era in the world of high performance computing.

1 Intel Xeon Phi Processor High Performance Programming Knights Landing Edition, chapter 17, Software-defined Visualization

2 Intel Xeon Phi Processor High Performance Programming Knights Landing Edition, chapter 24, Machine Learning

All figures are reproduced with permission from Intel Xeon Phi Processor High Performance Programming, Knights Landing Edition by James Reinders, Jim Jeffers and Avinash Sodani, copyright 2016, published by Morgan Kaufmann, ISBN 978-0-12-809194-4. Figures are available for download at http://lotsofcores.com/KNLbook.

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!

Weekly Wire Roundup: July 8-July 12, 2024

July 12, 2024

HPC news can get pretty sleepy in June and July, but this week saw a bump in activity midweek as Americans realized they still had work to do after the previous holiday weekend. The world outside the United States also s Read more…

Nvidia, Intel not Welcomed in New Apple AI and HPC Development Tools

July 12, 2024

New Mac developer tools will leverage Apple's homegrown chips, limiting HPC users' ability to use parallel programming frameworks from Intel or Nvidia. Apple's latest programming framework, Xcode 16, was introduced at Read more…

Virga: Australia’s New HPC and AI Powerhouse

July 11, 2024

Australia has officially added another supercomputer to the TOP500 list with the implementation of Virga. Officially coming online in June 2024, Virga is the newest HPC system to come out of the Australian Commonwealth S Read more…

NSF Issues Next Solicitation and More Detail on National Quantum Virtual Laboratory

July 10, 2024

After percolating for roughly a year, NSF has issued the next solicitation for the National Quantum Virtual Lab program — this one focused on design and implementation phases of the Quantum Quantum Science and Technolo Read more…

NCSA’s SEAS Team Keeps APACE of AlphaFold2

July 9, 2024

High-performance computing (HPC) can often be challenging for researchers to use because it requires expertise in working with large datasets, scaling the software, and selecting the best user interface. The National Read more…

Anders Jensen on Europe’s Plan for AI-optimized Supercomputers, Welcoming the UK, and More

July 8, 2024

The recent ISC24 conference in Hamburg showcased LUMI and other leadership-class supercomputers co-funded by the EuroHPC Joint Undertaking (JU), including three of the 10 highest-ranking Top500 systems, but some other ne Read more…

Shutterstock 2203611339

NSF Issues Next Solicitation and More Detail on National Quantum Virtual Laboratory

July 10, 2024

After percolating for roughly a year, NSF has issued the next solicitation for the National Quantum Virtual Lab program — this one focused on design and imple Read more…

NCSA’s SEAS Team Keeps APACE of AlphaFold2

July 9, 2024

High-performance computing (HPC) can often be challenging for researchers to use because it requires expertise in working with large datasets, scaling the softw Read more…

Anders Jensen on Europe’s Plan for AI-optimized Supercomputers, Welcoming the UK, and More

July 8, 2024

The recent ISC24 conference in Hamburg showcased LUMI and other leadership-class supercomputers co-funded by the EuroHPC Joint Undertaking (JU), including three Read more…

Generative AI to Account for 1.5% of World’s Power Consumption by 2029

July 8, 2024

Generative AI will take on a larger chunk of the world's power consumption to keep up with the hefty hardware requirements to run applications. "AI chips repres Read more…

US Senators Propose $32 Billion in Annual AI Spending, but Critics Remain Unconvinced

July 5, 2024

Senate leader, Chuck Schumer, and three colleagues want the US government to spend at least $32 billion annually by 2026 for non-defense related AI systems.  T Read more…

Point and Click HPC: High-Performance Desktops

July 3, 2024

Recently, an interesting paper appeared on Arvix called Use Cases for High-Performance Research Desktops. To be clear, the term desktop in this context does not Read more…

IonQ Plots Path to Commercial (Quantum) Advantage

July 2, 2024

IonQ, the trapped ion quantum computing specialist, delivered a progress report last week firming up 2024/25 product goals and reviewing its technology roadmap. Read more…

Shutterstock_1687123447

Nvidia Economics: Make $5-$7 for Every $1 Spent on GPUs

June 30, 2024

Nvidia is saying that companies could make $5 to $7 for every $1 invested in GPUs over a four-year period. Customers are investing billions in new Nvidia hardwa Read more…

Atos Outlines Plans to Get Acquired, and a Path Forward

May 21, 2024

Atos – via its subsidiary Eviden – is the second major supercomputer maker outside of HPE, while others have largely dropped out. The lack of integrators and Atos' financial turmoil have the HPC market worried. If Atos goes under, HPE will be the only major option for building large-scale systems. Read more…

Everyone Except Nvidia Forms Ultra Accelerator Link (UALink) Consortium

May 30, 2024

Consider the GPU. An island of SIMD greatness that makes light work of matrix math. Originally designed to rapidly paint dots on a computer monitor, it was then 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…

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…

Shutterstock_1687123447

Nvidia Economics: Make $5-$7 for Every $1 Spent on GPUs

June 30, 2024

Nvidia is saying that companies could make $5 to $7 for every $1 invested in GPUs over a four-year period. Customers are investing billions in new Nvidia hardwa Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst fir Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top 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…

Leading Solution Providers

Contributors

AMD Clears Up Messy GPU Roadmap, Upgrades Chips Annually

June 3, 2024

In the world of AI, there's a desperate search for an alternative to Nvidia's GPUs, and AMD is stepping up to the plate. AMD detailed its updated GPU roadmap, w Read more…

Intel’s Next-gen Falcon Shores Coming Out in Late 2025 

April 30, 2024

It's a long wait for customers hanging on for Intel's next-generation GPU, Falcon Shores, which will be released in late 2025.  "Then we have a rich, a very Read more…

Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's l 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…

IonQ Plots Path to Commercial (Quantum) Advantage

July 2, 2024

IonQ, the trapped ion quantum computing specialist, delivered a progress report last week firming up 2024/25 product goals and reviewing its technology roadmap. Read more…

The NASA Black Hole Plunge

May 7, 2024

We have all thought about it. No one has done it, but now, thanks to HPC, we see what it looks like. Hold on to your feet because NASA has released videos of wh 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…

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…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire