Response to ‘The Dangers of COTS Supercomputing’

By Jack Dongarra

April 11, 2008

My comments during a panel discussion at SC07 that things were out of balance in the HPC “ecosystem” has recently drawn criticism to the contrary from Michael Wolfe (http://www.hpcwire.com/hpc/2264586.html). Of course, like many policy issues, the question of whether or not the current investment in hardware R&D for HPC is well matched by a corresponding investment in essential software infrastructure is more than a little slippery. But even if I didn’t think Wolfe’s counter claim missed the mark in some important respects, which I do, I very much agree that the discussion he aims to provoke is one that is vitally important for our community to pursue. So it seems like a good time to try to make the idea I was expressing a little clearer.

It is worth noting that the remark in question from SC07 was not original with me. It represents a view that has been percolating through the community for a decade at least. To take a recent example, in the PITAC report of 2005 (see http://www.nitrd.gov/pitac/reports/20050609_computational/computational.pdf), we wrote that the HPC community’s preoccupation with peak performance and computing hardware, vital though it is, masks a troubling reality, namely that the most serious technical problems in computational science lie in software, usability, and the shortage of trained personnel. The twenty and thirty year life spans of major application codes, such as those studied by Doug Post (DoD, HPCMP) and his colleagues, are possible only because of the heroic efforts that scientific programmers repeatedly make to port them to new generations of hardware, using comparatively primitive software tools and programming models. Meanwhile, the fundamental R&D necessary to create balanced hardware-software systems that are easy to use, facilitate application expression in high-level models, and deliver large fractions of their peak performance on computational science applications is routinely postponed for a more opportune (but always elusive) time. Among its more insidious effects is the failure to overcome the intellectual challenges involved in creating such systems serves to exacerbate the scarcity of the broad education and training our community so desperately needs.

So this perceived imbalance in R&D investment in software infrastructure is long standing. It precedes and is largely independent of the mass migration to COTS platforms for HPC, which Wolfe finds so problematic. Even if this latter trend were reversed, and we were once again indulging our traditional fixation on HPC-tailored hardware, there is no reason to think the imbalance with regard to software tools, methods and infrastructure would be improved. On the contrary, there is every reason to think that it would be made worse.

The remarkable escalation in system complexity that we are currently experiencing is unlikely to recede under any circumstances, whether we stay with a COTS-based approach or not. The size and complexity of hardware systems (close to 500,000 cores in the largest hardware platform) continues to grow and only compounds the problems we face. Given the obstacles that now confront the processor design community — power wall, ILP wall, memory wall — I see no reason to believe that we can avoid the problems associated with exposing and managing order of magnitude increases in parallelism, even if the level of single thread performance were to remain completely stable and the investment in non-COTS designs (as desirable as they might be) were to be dramatically increased.

The point that I, along with many others in the HPC software community, continue to make is that the software base for computational science that we currently have is inadequate to keep pace with and support evolving hardware and application needs. I happily concede that the movement toward all-COTS HPC is problematic in various ways, some of which Wolfe mentions. But focusing on that fact only obscures the deeper problem that I am concerned with. Regardless of which path we take to achieve progress in hardware performance, chronic under investment in enabling software and applications forces researchers to build atop crumbling and inadequate foundations rather than on a modern, high-quality software base. The result is diminishing productivity for researchers and computing systems alike. Moreover, this condition is not susceptible of short-term solutions. One need only look at the development history of any large-scale software system to recognize the importance of an iterated cycle of development, deployment and feedback to develop an effective, widely used product. Consequently, achieving better balance in the HPC ecosystem will require sustained investment, long-term research and the opportunity to incorporate the lessons learned from a relatively long series of well considered iterations.

While many of us may have reservations (and excitement) about the growing onslaught of COTS multicore chip architectures, with all their attendant complexities, and the proliferation of petascale systems based on them, does anyone seriously believe that this movement will abate anytime soon? On the contrary, it gives every indication of being inescapable. If that’s the case, we should expect that the consequences of the longstanding imbalance in the HPC ecosystem will soon be thrown into much sharper relief and the discussion of how to recover from it will take on much greater urgency.

Jack Dongarra
University of Tennessee
Oak Ridge National Laboratory
University of Manchester

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