Hyperion (IDC) Paints a Bullish Picture of HPC Future

By John Russell

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Global exascale plans are solidifying (who, what, when, and how much ($)). The new kid on the block – all things ‘big’ data driven – is becoming an adolescent and behaving accordingly. And HPC ROI, at least as measured by Hyperion, is $551 per $1 invested (revenue growth) and $52 per $1 of profit invested.

This new version of HPC has been taking shape for some time and most of the themes are familiar (see HPCwire 2015 article, IDC: The Changing Face of HPC): industry consolidation, SGI’s acquisition by HPE along with the Dell EMC merger being the most recent; accelerated computing versus Moore’s Law; the growing appetite of HPC technology suppliers for expansion into the enterprise; big data’s transformation into a more nuanced multi-faceted blend of technologies and applications making it a form of HPC. These are just a few of the major trends laid out by Hyperion at its HPC User Forum.

All netted down, HPC is still expected to be a growth market, according to Earl Joseph, now CEO of Hyperion, which is expected to be acquired by year’s end. Joseph cited the following drivers:

  • Growing recognition of HPC’s strategic value.
  • HPDA, including ML/DL, cognitive and AI.
  • HPC in the cloud will lift the sector writ large.

“There’s a lot of growth in the upper half of the market and we are back to slowdown in the lower half of the market,” said Joseph. “Supercomputers are showing a very good recovery but they still haven’t hit the high point (~$5 billion) of three or four years ago.” They likely won’t get back to that level till 2022/2023 suggested Joseph.

Overall the HPC market segments have tended to hold their position. Storage ($4,316 million) remained the largest non-server segment and the fastest growing segment overall with a 7.8 percent annual growth expected over the next five years.

Vendor jockeying will continue he noted. Consolidation has been a major factor. HPE topped the revenue list in 2016 and will likely do so again in 2017 when SGI’s revenue is added. Dell EMC would no doubt question that and it will be interesting to watch this rivalry. IBM has never recovered its position after jettisoning its x86 businesses. The battle between x86 offerings, IBM Power, and ARM continues with both Europe and Japan making substantial bets on ARM for HPC uses. Indeed, the rise of heterogeneous computing generally is creating new opportunities for a variety of accelerators and accelerated systems.

These are the top HPC server suppliers by revenue ($ millions) according to Hyperion: HPE/HP ($3,878), Dell ($2,014), Lenovo ($909), IBM ($492), Cray ($461), Sugon ($315), Fujitsu ($226), SGI ($169), NEC ($166), Bull Atos ($118), and Other ($2,453). Interesting to note that “Other” is the second largest total revenue.

Not surprisingly, Hyperion looked closely at the intensifying race for exascale machines. China, for example, has three efforts on the path to exascale. Joseph expects China to be first to stand up an exascale. “They are saying 2019 but we’re not sure they will hit that date. We’re saying 2020,” said Joseph. The major players – U.S., EU, Japan, and China – are all speeding up their efforts. In the U.S., for example, Path Forward awards are expected soon.

Many questions remain. China is still selecting final vendors, something that was supposed to be done last fall said Joseph. Japan’s design is the closest to being “locked in” with the prime contractor Fujitsu having settled on an ARM-based architecture. But that project has experienced some delay and its financing method is not fixed.

“According to Japan’s latest announcement, their machine will be up in 2023 but we really expect it to be 2024. The cost may be a bit higher too, $800 million to $900-plus million range. Also, the Japanese government has not yet agreed to fund the whole system. They are funding it one year at time,” said Joseph.

Nevertheless, exascale funds are starting to flow and plans are taking firmer shape. As shown here, Hyperion has characterized the major exascale programs and forecast likely costs, technology choices, and timetables. Paul Messina, director of the U.S. Exascale Computing Project, provided an update at the HPC User Forum and HPCwire will have detailed coverage of the U.S. effort shortly.

Predictably, the Hyperion presentation covered a lot of ground drawn from Hyperion/IDC’s ongoing research efforts. Steve Conway, another IDC veteran and now Hyperion SVP research, reviewed the adoption of HPDA as well as zeroing in on two of its drivers, deep learning and machine learning. You may recall that IDC was one of the first to recognize the rise of data analytics as part of HPC. Clearly there are many potential uses cases Conway said. Today, the HPC-HPDA convergence is taken for granted and is depicted in the slide below.

Hyperion has just created four new data-intensive segments, bulleted here, with more to follow:

  • Fraud and anomaly detection. Two example use cases include government (intelligence, cyber security) and industry (credit card fraud, cyber security).
  • Affinity Marketing. Discern potential customers’ demographics, buying preferences and habits.
  • Business intelligence. Identify opportunities to advance market position and competitiveness.
  • Precision Medicine. Personalized approach to improve outcomes, control costs.

“Fraud and anomaly detection are the largest today. Business intelligence is growing quickly. The tortoise that will probably win the race is precision medicine because of the size of the health care over time,” said Conway, noting the HPDA market is growing two to three times faster than traditional overall HPC market.

Not surprisingly, deep learning is the darling of this frontier and also the most technically challenging. Singling out precision medicine as a promising area for DL, Conway said “IBM Watson is the name that’s known here but I promise you x86 clusters are doing the same thing.”

Making the machine learning to deep learning shift is a difficult journey said Conway. Having enough data both to train deep learning systems and also to infer high fidelity decisions when put into practice is the big challenge. “If you are in the realm of Google or Baidu or Facebook, you have plenty of data. If you are outside of that realm you are in trouble. In most of these realms you do not have enough data to do deep learning,” said Conway.

“One case in point, and we have many of them: We talked to the United Health Group which has about 100 million people that it covers; that’s not nearly enough to do the deep learning they need and they know it. They have built a facility in Cambridge, Mass., and invited competitors to come in and to pool anonymized data to try to get to the point where they can actually start playing with deep learning. This is a big issue.”

Aside from having enough data, there’s the computation challenge. Today, GPUs “rule the roost in these ecosystems, with the software built around them, but we expect to see other things like Intel Phis and the remarkable resurgence of FPGAs have a role. Another big issue vendors are having here is there really aren’t good benchmarks and they spend too much time just trying to decide what would be satisfactory results,” Conway said.

In earlier studies HPC user willingness to deploy in the cloud has often seemed tepid. Costs, security, adequate performance (data movement, computation, and storage) were all concerns, especially so in public cloud. Hyperion suggested attitudes seem to be changing and reported a jump in the number of HPC sites using public clouds – 64 percent now up from 13 percent in 2011. Conway cautioned that the size and number of jobs were still limited to a small proportion of any give user’s needs. Conversely, suggested Conway, private and hybrid cloud use was growing fast and held more near-term promise.

Despite the great flux within HPC many areas have changed little according to Hyperion. For example, software problems (management s/w, parallel s/w, license issues, etc.) remain the number one pain point to HPC adoption or use according to Hyperion research. This prompted a member of the audience to say, “Earl, this looks like exactly the same IDC slide I saw ten years ago.” It sort of is.

Storage access time was now the number two complaint, followed by clusters still too hard to use and manage.

Hyperion presented a fair amount of detail concerning its ROI study and is making the full data available to requesters. (Download Results: www.hpcuserforum.com/ROI)

Slides courtesy of Hyperion Research.

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