How the United States Invests in Supercomputing

By Maciej Chojnowski

November 14, 2018

At the end of October, the U.S. Department of Energy unveiled Sierra – the second leadership-class supercomputer delivered as a result of CORAL collaboration between Lawrence Livermore, Oak Ridge and Argonne national laboratories. Earlier this year, the first CORAL system Summit was launched and became the world’s fastest system in June. In this contributed interview which was conducted ahead of SC18, Maciej Chojnowski, editor with the Interdisciplinary Centre for Mathematical and Computational Modelling at the University of Warsaw, discusses the details of the CORAL project with Dr. Dimitri Kusnezov from the U.S. Department of Energy.

Disclaimer: The views expressed in the responses are personal and do not necessarily represent the view of the U.S. Department of Energy or the United States.

Maciej Chojnowski: At the moment, the CORAL systems Summit and Sierra are the number one and number three [now number two] most powerful supercomputers on the planet. In an interview during the Supercomputing Frontiers Europe 2018 conference in Warsaw, you said that what counts most in HPC is not computers themselves but the purpose we try to achieve using them. What is the purpose of these systems? For what sort of work will it be harnessed?

Dimitri Kusnezov, Supercomputing Frontiers Europe 2018 in Warsaw, Poland

Dimitri Kusnezov: You are asking the right questions. We use public money to support the development and delivery of these remarkable supercomputers. But they are just tools – so we need to be sure that it is the right tool for the problems people care most about, and that the return on the investment (ROI) far exceeds the cost of these systems – increasingly in the hundreds of millions of dollars. The ranking of the systems should not be interpreted as a measure of the success or overall utility of such a tool. It can be a distraction that drives systems to perform against metrics that do not accurately measure the needed workflow you may otherwise optimize your computer design to.

We do look hard at who will use these systems – years in advance, whether the cloud is a more effective option, whether a collection for small systems is more cost effective, and so forth. It is all based on the classes or problems you believe are worth that scale of investment. These large systems take years to plan, co-design and deliver, and we develop our computer codes during this period so we can test performance on early prototypes prior to full delivery. So to your question, what is the ROI and what will these systems do? Of our two big systems, one is for a well-defined set of nuclear security questions and the other one is for the open scientific and technology community. Interestingly, fundamental and applied science issues are common to both systems, they just answer different types of questions.

I think of the ROI through several measures. For Sierra, we have classes of problems that impact billion dollar class decisions. So it is not hard to make that calculation. For decisions based on simulation, the confidence in your predictions is very important. We call this ‘uncertainty quantification’ or UQ and it can be computationally demanding – more than we can accommodate even at this scale. I described some of this in my keynote last winter at SCFE2018 (which I wrote up in https://arxiv.org/pdf/1804.11002.pdf). For applied science and technology, the ROI is on the order of 500 to 1. That is for every dollar invested, you have a return of somewhere around 500 dollars in productivity or market value or similar. For pure science, you can measure the impact of the work produced and assess whether the results have had fundamental changes in the understanding of key problems: are they the top discovery, in the top 5 or top 10 or less impactful, for example. An objective look can help gauge your effectiveness in using these systems. But this type of return depends on planning the use cases in advance in order to maximize the impact of the supercomputer during its 4 to 5 year typical lifetime in service.

Chojnowski: The CORAL system designs in Sierra and Summit create “a new breed of computer,” according to Nvidia CEO Jensen Huang this summer. It is expected to let scientists harness AI hand in hand with simulation. What are the key technological components that make it possible and why is it important?

Kusnezov: We are in a remarkable period of technology change today. Artificial intelligence is of increasing relevance to all our activities, across the spectrum from sensor data and detection, through learning methods such as machine learning (ML), to decisions based on searching, planning and proving, to actions such as autonomy or human/AI interfaces. When we started down the exascale path some years back, it was not with AI in mind. But as technologies have developed, we have found that these hybrid architectures are well suited to helping us better understand the ML piece of AI in more detail. For these particular systems, we did push in a number of directions, from the design of the motherboards and water cooled compute nodes, to pushing limits for the GPU resilience, scheduling, diagnostics, burst buffers, switch based collectives, GPFS performance & scalability and so forth. But these are productivity enablers for the larger purpose of taking initial small steps in starting to drive AI into model based prediction. We will begin to study how to augment computer simulation with learning based methods, recognizing that we have a user base invested in traditional computer simulation. So a gentle turn is needed in our large systems. As tools, these systems can help us understand ML in different ways, but we know they are not specifically optimized for that. Today, there is a remarkable global industry developing in novel AI based hardware, designed and fabbed for AI which can offer remarkable speed-ups. We are certainly pursuing that as well.

Chojnowski: IBM’s architecture for the CORAL systems integrate the data analysis capabilities of IBM Power9 CPUs with the deep learning capabilities of GPUs. What are the expected results of this architecture?

Kusnezov: Aside from the power efficiency, network advantage of the fat nodes and the complex memory hierarchies, what really caught my eye some years back was the coherent memory space on the nodes. That and the number of PCIe slots. It opened the door for exploration of how neuromorphic or machine learning technologies could coexist with our more traditional approaches to computer simulation. I do believe that the future of predictive simulation will require a bold step into data centered AI approaches and these architectures are suited for taking some first steps in understanding how you integrate machine learning methods with the more traditional approaches to model-based prediction. That is what I really find exciting.

Why is this important? Our department should better be called the Department of Hard Problems, or the Department of Modelling. We drive computing and simulation not as a means to an end, but as a tool to help us answer questions that are consequential and important to get right. From nuclear security to the energy sector and cyber, there are decisions we have to make with limited funds to ensure against situations we hope never happen. Simulation provides us with a means to understand problems we face and provide options. But simulations without rigorous bounds on their validity are not predictions nor actionable. For that reason we have been pushing validation, verification, uncertainty quantification (UQ) and the many methods needed to help bracket our confidence in any prediction. These architectures move us closer to those which will ultimately be able to help us with this – more intelligent, better able to deal with the deluge of experimental and numerical data, and more cognitive. These architectures, while ‘smarter’ will help us start this transition to tackling the UQ problem which I believe to be NP-Hard, in the sense of complexity theory, and consequently problematic on any von Neumann architecture anyway.

Chojnowski: In April this year, it was announced that DOE intends to spend $1.8 billion on building two, or possible three, exascale supercomputers under the CORAL-2 program. Both the CORAL and CORAL-2 programs mandate architecturally diverse machines: CORAL systems are the results of the collaboration of IBM, Nvidia and Mellanox, while A21 will be produced by Intel and Cray. Why this diversity?

Kusnezov: The diversity of the industry and the competition that emerges when we challenge the technology sector with new designs helps drive innovation and is an important part of the technology development cycle. We look for approaches where industry can collaborate on solutions together, leveraging their strengths and product development paths, to allow for novel architectures and hardware/software approaches to otherwise hard problems through such cost and risk sharing. We also do quite a bit of non-recoverable engineering (NRE) work with the industry to help develop technologies that would not otherwise be available for us, and which might be aligned with their technology roadmaps. The larger the pool of companies out there, the richer the set of ideas that emerge.

Dr. Dimitri Kusnezov was the keynote speaker at Supercomputing Frontiers Europe 2018 in Warsaw, Poland. You can watch his talk and interview with him in the MEDIA section on the SCFE website.

Registration for SCFE2019 is already open.

This article originally appeared on the ICM website.

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…

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…

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…

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