Revisiting the 2008 Exascale Computing Study at SC18

By Scott Gibson

November 29, 2018

Jeffrey Vetter, Distinguished R&D Staff Member at Oak Ridge National Laboratory, led the SC18 Birds of a Feather session “Revisiting the 2008 ExaScale Computing Study and Venturing Predictions for 2028.”

A report published a decade ago conveyed the results of a study aimed at determining if it were possible to achieve 1000X the computational power of the then-emerging petascale systems at a system power of no more than 20 MW. On November 14 at the SC18 supercomputing conference in Dallas, some of the original contributors to the report participated in a Birds of a Feather session in which they reflected on the document, sharing what they deemed to be its hits and misses and making predictions for 2028.

Session leader, Jeffrey Vetter of Oak Ridge National Laboratory, said the 2008 report, titled “Exascale Computing Study: Technology Challenges in Achieving Exascale Systems,” has been cited more than 1,000 times and that many people look to it to understand what research agendas they should undertake and to consider what are the most salient challenges to be faced in high-performance computing.

The study was sponsored by the Defense Advanced Research Projects Agency (DARPA) Information Processing Techniques Office (IPTO) with Bill Harrod as program manager. The report represents the ideas of people from universities, industry, and research labs collected during periodic meetings conducted during the course of more than a year.

Harrod, who is now program manager for the Intelligence Advanced Research Projects Activity (IARPA), told the BoF audience that consideration of petascale system specifications as they existed at the time informed the study group members’ assumptions about exascale. Petascale systems operated at about 13 MW with several hundred cabinets. Thus, the anticipated parameters for exascale were 1018 operations/second at 20 MW and with fewer than 500 cabinets. The pivotal big-picture questions, Harrod said, were whether an exascale system was needed and could it be used for scientific discovery and other practical purposes.

Two other studies, on software and resiliency, respectively, followed the study upon which the 2008 report was based. The resounding, overarching comment concerning the findings of the three studies, Harrod said, was that co-design would be essential. He added that although the co-design concept was not revolutionary, it was determined to be critical for ensuring hardware design would correspond properly with the intended uses for the system, and it became an integral aspect of the US Department of Energy’s Exascale Computing Initiative (ECI) and Exascale Computing Project (ECP).

Peter Kogge of the University of Notre Dame led the Exascale Computing study and served as editor of the 2008 report. In his presentation for the BoF, he outlined four key challenges that surfaced from the study: energy and power, memory, concurrency, and resiliency. He also summarized the 2008 computing environment and what it was anticipated to look like by 2015, noting that the study team did not focus on application needs and the Roofline model. For matrix multiply like the High-Performance Linpack (HPL) benchmark, he said, having a large enough cache would supersede concerns about memory speed; and to reach a peak of 1 exaflops, the goal was to hit 20 pJ/flop.

The team assembled what Kogge referred to as an aggressive strawman with an architecture that was largely influenced by study contributor Bill Dally (then with Stanford University, now with Nvidia), who participated in the BoF. The architecture was characterized by multicore, no coherency, and shared global address space. Reaching the 1 exaflops peak meant 68 MW power usage from 583 racks. Relative to programming, about 1 billion threads needed to be maintained. A wire interconnect was assumed.

Kogge provided details from the report on the aggressive strawman system, which he said he considered to be “remarkably prescient” with respect to what ultimately materialized in the evolution toward exascale.

A 2015 paper for the International Supercomputing Conference (ISC) by Kogge titled “Updating Energy Model for Future Exascale Systems” examined an update of the models that the Exascale Computing study team had built to project performance for only the heavyweight (Xeon chips) sockets. The paper received a Gauss Award.

The study group’s final analysis showed that an exaflops could be reached by 2020, but with a peak of 180 MW to 430 MW.

The Study Contributors’ Assessments of Hits and Misses

Bill Harrod

At the inception of the DARPA studies, the target year for reaching exascale was 2015, but based on the results of the software study it was adjusted to 2018. Today, projections are focused on the 2021–2023 time frame. Harrod said that although the projections have evolved, the studies paved the way for DARPA’s Ubiquitous High-Performance Computing (UHPC) Exascale Projects and laid the foundation for DOE’s ECI and ECP. They have, he added, greatly enhanced the environment for exascale development.

In terms of hits and misses, the importance of co-design has played out at DOE and many other places, including the FastForward and PathForward programs, Harrod said. As a key miss of the study, he highlighted the fact that it did not foresee the impact of artificial intelligence (AI).

Peter Kogge

The study group’s approach in focusing on the heavyweight systems was dead-on through 2015, and the aggressive strawman they developed greatly resembles today’s GPU, Kogge said. In addition, he said the study group was right to point out that some form of memory stacking would be necessary, and that interconnects, at least locally within racks, would still largely be copper. Among the misses, he highlighted the heterogeneous systems and the SIMT threading model, which constitutes what is done with GPUs today.

Keren Bergman (Columbia University)

Bergman said that as someone whose background is in optical networks, she considered the close examination of the energy consumption of the interconnects in this study to be enlightening. With respect to the study’s hits, she opined that the deep discussions captured the growing challenge of data movement. However, in her view, one of the study’s sizable misses was the cost associated with manufacturability. She said substantial innovations would be required to integrate photonics into chips and remedy one of the last real bottlenecks.

Dean Klein (Micron/now retired)

Klein, who was vice president of memory system development at Micron at the time of the study and today in retirement mentors and motivates engineering students, highlighted as a hit the study group’s awareness that the energy of memory subsystems would drive compromises in the memory in systems, and as a miss the idea of NAND flash playing a role in supercomputing.

Bill Dally

The prescience of the study’s aggressive silicon strawman made it a hit, Dally said. Conversely, he viewed as shortcomings the paucity of capable networks due to funding, failure to anticipate AI, and an overly conservative approach in addressing software.

Exascale Study Contributors’ Predictions for 2028

The belief that complementary metal-oxide-semiconductor (CMOS) technology for constructing integrated circuits would remain predominant was a recurring notion, as the BoF contributors offered diverse predictions for 2028 based on the perspectives of their areas of expertise.

The contributors also responded to comments and questions from the audience.

Scott Gibson is a science writer and communications specialist with Oak Ridge National Laboratory.

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