Are Supercomputing’s Elite Turning Backs on Accelerators?

By Nicole Hemsoth

June 26, 2014

Now that this year’s Top500 has been released and analyzed, we wanted to take a step back to look at a few emerging trends. One of the elements of the last couple of lists that caught our eye is that despite the availability of new accelerator/co-processor choices, there is a noticeable leveling off in overall use following a sharp increase that began in 2010 with the full arrival of GPU computing.

This data is rather out of step with what the analysts are finding in terms of accelerator adoption. For instance, both IDC’s high performance computing group and Intersect360 see strong growth for the accelerator/co-processor market now and in years ahead. IDC found that the number of sites using these technologies jumped from 28.2% in 2011 to an incredible 76.9% in 2013. They also noted that NVIDIA GPUs and the Xeon Phi were “neck in neck” in the race for HPC customers, noting the “use of co-processors and accelerators is still wider than it is, meaning that these newer devices have entered many more sites, but are often still used for exploratory purposes rather than production computing.” IDC also highlighted that industrial users were less likely to buy large number of accelerators, but were more likely to use them in production. Intersect360 Research had a more modest estimate, finding that accelerators were being used on 21% of the installed base, although they agreed with IDC that evaluation was playing a large role in the adoption at this point.

So if this massive uptick in accelerator and co-processor adoption is finding its way into analyst research with such striking figures, how is it that only a tick over 12% of the Top500 list of supercomputers is making use of them? These are the most experimental environments and while, as you can see in the charts coming soon, there was a spike in 2010-2011 signalling the full arrival of GPUs in particular, there’s been no rapid increase. Just an even line.

Of all the machines on this year’s Top500, only 62 are using accelerator/co-processor technology, which is only a slight increase from the November ranking, which showed a total of 53 systems. Of thos, 44 are using NVIDIA GPUs (see the generation in the graphic below), 17 have implemented Xeon Phi as the co-processor/acceleration option, and two machines are leveraging the ATI Radeon cards. While there are not necessarily surprises in these vendor breakdowns, we wanted to highlight the flat line that extends across these accelerated machines. On the one hand, it would seem that given the options available with the addition of Intel’s Xeon Phi into the HPC market and the ever-richer ecosystem around CUDA and OpenCL, why aren’t more supercomputing sites choosing to push their machines with accelerators?

AcceleratorGeneral

There are a few reasons for this tapering off, says Top 500 list curator, Erich Strohmaier, but make no mistake, none of them spell a dire future for GPUs or co-processors like the Xeon Phi.

The culprit for this even keel story for what’s remained one of the most exciting technology areas in HPC has nothing to do with interest, it’s a matter of procurement cycles aligning with product cycles. He said that some years ago, the list could see more dramatic swings with new technologies because procurements were secured with upgrade agreements so users could confidently grab a system instead of delaying procurement to wait for the latest and greatest part.

It is probably coming as no surprise then that we’re going to see a marked uptick in accelerator adoption in 2015 (or that’s the plan, according to what publicly stated roadmap details we have suggest) when Knight’s Landing comes into the market, bringing with it a slew of systems that are literally waiting on the right parts. As we noted during the NERSC-8 “Cori” system announcement, the users there were most interested in the on-package memory because of their workloads. It wouldn’t make sense to buy a system now and retrofit–and chances are, there will be other announcements around pending product launches for big systems (CORAL, etc.–just a guess).

But here’s the interesting thing. The Top500 list isn’t just made up of the academic/national lab supercomputers that are more capable of tying their procurements to the products they’re anticipating. What about industry HPC users who have different processes for securing their machines and, arguably, a more mission-focused (read as monetary) incentive to do what works now and buy what’s coming when it’s ready? It would be one thing to look at the analyst data on the sharp rise in accelerator/co-processor use and say that it doesn’t affect the Top500 because of the influence of commercial systems but guess what? Well over half the Top500 is made up of industrial machines.

This could mean a few things. First, we might be wrong in the assumption that industrial users are less likely to wait around and tie their procurement processes to product cycles. Perhaps everyone does now–feel free to comment on this. But even still, there’s no real increase in actual Linpack-ready implementation, so that might suggest that if they had already evaluated accelerators/coprocessors and didn’t find them of immediate value, they may have moved on from the experiment. So perhaps the “honeymoon” phase for these technologies is over, only for the passion to be reignited again with the arrival of Knight’s Landing and the upcoming technologies NVIDIA has on deck (and let’s not forget what will happen with FPGAs and OpenPower–didn’t meant to be exclusionary there).

Either way, there seem to be some conflict fact points about what’s really happening with the accelerator adoption curve. The other argument to all of this, of course, is that the Top500, even with its industry users, isn’t counting a huge number of systems that could run Linpack and rank highly if only they chose to do so. It might be that we have a range of data about adoption that is incomplete on all sides. Analyst figures vary widely between research groups, the Top500 has a leveled-off showing, and as one might imagine, if you ask the vendors how their accelerator/co-processor business is doing, it’s all sunshine and roses.

On the bright side, there are two closing figures from this ISC’s Top500 that seemed worth pointing out. While they’re not related to adoption, they do make the case for the value for these technologies on both a performance and efficiency front. The first graphic, courtesy of Erich Strohmaier, shows the performance share of accelerated systems matched with the top ten supercomputer rankings that shows quite clearly that these are the key piece for high performance. 9 out of 10 of the systems are outfitted with GPUs or Xeon Phi.

Accelerators1Accelerators2

The second graphic shows the green performance of these GPU and Xeon Phi-enabled systems.

acceleratorsEfficiency

We would love to hear from you on this point. Is it a matter of waiting until 2015 for things to pick up again following the product cycle that so many seem to be holding out for? Or is it that the peak interest and experimentation has yielded results that are expected to be stagnant? And further, how should the upcomign Knight’s Landing processor and anything integrated that NVIDIA, IBM, AMD and others do be classified if the accelerator is inside the chip–won’t that become the norm?

 

 

Here is more text.

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