The Machine Learning Hype Cycle and HPC

By Dairsie Latimer

June 14, 2018

Like many other HPC professionals I’m following the hype cycle[1] around Machine Learning/Deep Learning with interest. I subscribe to the view that we’re probably approaching the ‘peak of inflated expectations’ but not quite yet starting the descent into the ‘trough of disillusionment.’

This still raises the probability that we are seeing the emergence of a truly disruptive presence in the HPC space – but perhaps not for the reasons you might expect. We’ve already seen how the current dominance of GPUs in the training of current ML/DL techniques has powered Nvidia to record revenues in the datacenter.

But is that hegemony set to be challenged? At last count there were 25 or more start-ups emerging from stealth or already within a few quarters of shipping hardware implementations aimed directly at accelerating aspects of training and inference.

They will be looking to capture market share from the current incumbents (Intel and Nvidia) as well as positioning themselves for the expected growth in ML/DL for edge computing applications. These companies are also going up against several of the hyperscalers and behemoths of the consumer market that are also rolling their own inference engines (thought admittedly mostly aimed at the mobile/edge space).

Gartner Hype Cycle shows five key phases of a technology’s life cycle (source: Gartner)

Since we seem to have accepted that HPC and big data are two elements of the same problem, how will the fact that research and development for ML/DL (regardless of domain) is often carried out on HPC systems skew procurements in the next few years? Looking at the latest crop of petascale and exascale pathfinders their performance stems mostly from Nvidia’s V100s. However smaller scale more general purpose systems are still predominantly homogeneous in composition with modest if any GPU deployment.

What’s interesting about this is that accelerators are now mainstream at the upper end of the market. While both CPUs and GPUs work well with the existing ML frameworks it’s clear that the new entrants are likely to bring significant advantages in performance and power efficiency even when measured against Nvidia’s mighty V100. What odds on Nvidia having to split their Tesla line to produce pure ML/DL targeted accelerators? How will this affect the way in which we procure heterogeneous HPC systems?

I personally think ML/DL methodology is and will continue to have a more immediate practical impact at the ‘edge’ than in scientific simulation (and there are lots of reasons for this) but there is no doubt that ML/DL will cohabit with more traditional HPC applications on many research systems.

Can we please stop abusing the term AI?

Like many I have a pet peeve which is the tendency to conflate traditional meaning of Artificial Intelligence (AI) with ML and DL. If we must use the term AI to encompass the various techniques by which machines can build models that approximate and in some cases outperform humans also expert in a problem area, can we at least start using the term Artificial Generalized Intelligence (AGI) more widely. There’s a useful primer on the subject on EnterpriseTech which saved me from having to write it myself.

So what will AI be good for in HPC and Big Data?

There are of course many arrows to the AI quiver and many are already successfully deployed as part of various HPC workflows, but most are essentially used for automation of data analysis and visualization tasks that can be performed by humans (or at least programs written by humans). The models have been conceived, built and trained by humans to replicate or improve upon some data analytics task.

Source: Shutterstock

The pursuit of new knowledge from discrete data is still something that is currently very much beyond us in the field of AGI let alone AI, and it also speaks to the method of scientific enquiry and human nature.

When we run simulations for well understood, or at least well defined scientific domain area, we already know how to extract value from the data that is generated. We’ve set up the numerical simulation after all so we know what to expect within certain bounds and we can interpret the results within that framework and mental model.

For new science we often don’t know the right questions to pose in advance, and as a result we can’t set up a precise or well defined process to extract value from it. The discovery process is more in the form of a dialog with the data, where a series of ‘what if’ questions are posed and the results scrutinized to see what value or insights they deliver. It is by nature an iterative process and it still requires a human to judge the value of the results.

If conceivably we could turn over the automation of this process to an AI it would bump up against a significant issue, which is that an AI model almost certainly won’t’ solve a problem in the same way as a scientist. The scientist would not necessarily have the ability to build a mental model that allows the transfer of knowledge and as a result it becomes an unverifiable black box. In science this acts as a red flag, and if a process is not well understood then someone will inevitably set out to document and postulate a theory that can be confirmed by experimental observation.

Now for those computational scientists I have spoken to about this, we accept that we routinely deploy fudge factors, or approximations, which we know are imperfect but serve a purpose, but we console ourselves that there is usually published science behind their use. As humans we are actually quite limited by the scope of the information we can process in pursuit of a solution and this is what DL models are exceedingly good at.

Now take the case of a DL model that has been trained to approximate some computationally expensive part of a time critical simulation. We know what data went into training it, though we many not understand the significance of some of it. We have observed the outputs and at some point they will meet a set criterion which means they are ‘good enough’ to use. But all models have corner cases; you can call them bugs if you like. In the event that a DL model produces a result that trips some sanity check how do you debug or verify a DL model, especially one that a human hasn’t explicitly guided the creation of?

It’s not so much that these models won’t be able to do the job, but we will naturally start to question how comfortable we are as scientists replying on a model that we don’t understand or can’t verify. Like most scientists and engineers I prefer to have a mental model of a process that is a bit more sophisticated than ’it just works.’

As a result, I do think that the uptake of AI in HPC will be tempered by the natural reluctance of many to see too many black boxes in their workflows. Perhaps there will be moves to ensure that the AI frameworks support some sort of human-verifiable intermediate representation rather than rather than us just making the leap of faith that the AI is right.

As humans we also rely on intuition which often requires an equivalent leap of faith but as scientists we’re on the brink of creating systems whose operation we don’t understand and can’t trace. The power of deep learning models and their ability to ingest prodigious quantities of widely different data and provide insights can’t be ignored but the temptation to waive the explainability factor should also be resisted.

[1] https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/

About the Author

Dairsie Latimer, Technical Advisor at Red Oak Consulting, has a somewhat eclectic background, having worked in a variety of roles on supplier side and client side across the commercial and public sectors as an consultant and software engineer. Following an early career in computer graphics, micro-architecture design and full stack software development, he has over twelve years’ specialist experience in the HPC sector, ranging from developing low-level libraries and software for novel computing architectures to porting complex HPC applications to a range of accelerators. Dairise joined Red Oak Consulting (@redoakHPC) in 2010 bringing his wealth of experience to both the business and customers.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

HPC Career Notes: July 2020 Edition

July 1, 2020

In this monthly feature, we'll keep you up-to-date on the latest career developments for individuals in the high-performance computing community. Whether it's a promotion, new company hire, or even an accolade, we've got Read more…

By Mariana Iriarte

Supercomputers Enable Radical, Promising New COVID-19 Drug Development Approach

July 1, 2020

Around the world, innumerable supercomputers are sifting through billions of molecules in a desperate search for a viable therapeutic to treat COVID-19. Those molecules are pulled from enormous databases of known compoun Read more…

By Oliver Peckham

HPC-Powered Simulations Reveal a Looming Climatic Threat to Vital Monsoon Seasons

June 30, 2020

As June draws to a close, eyes are turning to the latter half of the year – and with it, the monsoon and hurricane seasons that can prove vital or devastating for many of the world’s coastal communities. Now, climate Read more…

By Oliver Peckham

Hyperion Forecast – Headwinds in 2020 Won’t Stifle Cloud HPC Adoption or Arm’s Rise

June 30, 2020

The semiannual taking of HPC’s pulse by Hyperion Research – late fall at SC and early summer at ISC – is a much-watched indicator of things come. This year is no different though the conversion of ISC to a digital Read more…

By John Russell

What’s New in HPC Research: Mosquitoes, [email protected], the Last Journey & More

June 29, 2020

In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

By Oliver Peckham

AWS Solution Channel

Maxar Builds HPC on AWS to Deliver Forecasts 58% Faster Than Weather Supercomputer

When weather threatens drilling rigs, refineries, and other energy facilities, oil and gas companies want to move fast to protect personnel and equipment. And for firms that trade commodity shares in oil, precious metals, crops, and livestock, the weather can significantly impact their buy-sell decisions. Read more…

Intel® HPC + AI Pavilion

Supercomputing the Pandemic: Scientific Community Tackles COVID-19 from Multiple Perspectives

Since their inception, supercomputers have taken on the biggest, most complex, and most data-intensive computing challenges—from confirming Einstein’s theories about gravitational waves to predicting the impacts of climate change. Read more…

Racism and HPC: a Special Podcast

June 29, 2020

Promoting greater diversity in HPC is a much-discussed goal and ostensibly a long-sought goal in HPC. Yet it seems clear HPC is far from achieving this goal. Recent U.S. events, most poignantly the killing of George Floy Read more…

Hyperion Forecast – Headwinds in 2020 Won’t Stifle Cloud HPC Adoption or Arm’s Rise

June 30, 2020

The semiannual taking of HPC’s pulse by Hyperion Research – late fall at SC and early summer at ISC – is a much-watched indicator of things come. This yea Read more…

By John Russell

Racism and HPC: a Special Podcast

June 29, 2020

Promoting greater diversity in HPC is a much-discussed goal and ostensibly a long-sought goal in HPC. Yet it seems clear HPC is far from achieving this goal. Re Read more…

Top500 Trends: Movement on Top, but Record Low Turnover

June 25, 2020

The 55th installment of the Top500 list saw strong activity in the leadership segment with four new systems in the top ten and a crowning achievement from the f Read more…

By Tiffany Trader

ISC 2020 Keynote: Hope for the Future, Praise for Fugaku and HPC’s Pandemic Response

June 24, 2020

In stark contrast to past years Thomas Sterling’s ISC20 keynote today struck a more somber note with the COVID-19 pandemic as the central character in Sterling’s annual review of worldwide trends in HPC. Better known for his engaging manner and occasional willingness to poke prickly egos, Sterling instead strode through the numbing statistics associated... Read more…

By John Russell

ISC 2020’s Student Cluster Competition Winners Announced

June 24, 2020

Normally, the Student Cluster Competition involves teams of students building real computing clusters on the show floors of major supercomputer conferences and Read more…

By Oliver Peckham

Hoefler’s Whirlwind ISC20 Virtual Tour of ML Trends in 9 Slides

June 23, 2020

The ISC20 experience this year via livestreaming and pre-recordings is interesting and perhaps a bit odd. That said presenters’ efforts to condense their comments makes for economic use of your time. Torsten Hoefler’s whirlwind 12-minute tour of ML is a great example. Hoefler, leader of the planned ISC20 Machine Learning... Read more…

By John Russell

At ISC, the Fight Against COVID-19 Took the Stage – and Yes, Fugaku Was There

June 23, 2020

With over nine million infected and nearly half a million dead, the COVID-19 pandemic has seized the world’s attention for several months. It has also dominat Read more…

By Oliver Peckham

Japan’s Fugaku Tops Global Supercomputing Rankings

June 22, 2020

A new Top500 champ was unveiled today. Supercomputer Fugaku, the pride of Japan and the namesake of Mount Fuji, vaulted to the top of the 55th edition of the To Read more…

By Tiffany Trader

Supercomputer Modeling Tests How COVID-19 Spreads in Grocery Stores

April 8, 2020

In the COVID-19 era, many people are treating simple activities like getting gas or groceries with caution as they try to heed social distancing mandates and protect their own health. Still, significant uncertainty surrounds the relative risk of different activities, and conflicting information is prevalent. A team of Finnish researchers set out to address some of these uncertainties by... Read more…

By Oliver Peckham

[email protected] Turns Its Massive Crowdsourced Computer Network Against COVID-19

March 16, 2020

For gamers, fighting against a global crisis is usually pure fantasy – but now, it’s looking more like a reality. As supercomputers around the world spin up Read more…

By Oliver Peckham

[email protected] Rallies a Legion of Computers Against the Coronavirus

March 24, 2020

Last week, we highlighted [email protected], a massive, crowdsourced computer network that has turned its resources against the coronavirus pandemic sweeping the globe – but [email protected] isn’t the only game in town. The internet is buzzing with crowdsourced computing... Read more…

By Oliver Peckham

Global Supercomputing Is Mobilizing Against COVID-19

March 12, 2020

Tech has been taking some heavy losses from the coronavirus pandemic. Global supply chains have been disrupted, virtually every major tech conference taking place over the next few months has been canceled... Read more…

By Oliver Peckham

Supercomputer Simulations Reveal the Fate of the Neanderthals

May 25, 2020

For hundreds of thousands of years, neanderthals roamed the planet, eventually (almost 50,000 years ago) giving way to homo sapiens, which quickly became the do Read more…

By Oliver Peckham

DoE Expands on Role of COVID-19 Supercomputing Consortium

March 25, 2020

After announcing the launch of the COVID-19 High Performance Computing Consortium on Sunday, the Department of Energy yesterday provided more details on its sco Read more…

By John Russell

Steve Scott Lays Out HPE-Cray Blended Product Roadmap

March 11, 2020

Last week, the day before the El Capitan processor disclosures were made at HPE's new headquarters in San Jose, Steve Scott (CTO for HPC & AI at HPE, and former Cray CTO) was on-hand at the Rice Oil & Gas HPC conference in Houston. He was there to discuss the HPE-Cray transition and blended roadmap, as well as his favorite topic, Cray's eighth-gen networking technology, Slingshot. Read more…

By Tiffany Trader

Honeywell’s Big Bet on Trapped Ion Quantum Computing

April 7, 2020

Honeywell doesn’t spring to mind when thinking of quantum computing pioneers, but a decade ago the high-tech conglomerate better known for its control systems waded deliberately into the then calmer quantum computing (QC) waters. Fast forward to March when Honeywell announced plans to introduce an ion trap-based quantum computer whose ‘performance’ would... Read more…

By John Russell

Leading Solution Providers

Contributors

Neocortex Will Be First-of-Its-Kind 800,000-Core AI Supercomputer

June 9, 2020

Pittsburgh Supercomputing Center (PSC - a joint research organization of Carnegie Mellon University and the University of Pittsburgh) has won a $5 million award Read more…

By Tiffany Trader

‘Billion Molecules Against COVID-19’ Challenge to Launch with Massive Supercomputing Support

April 22, 2020

Around the world, supercomputing centers have spun up and opened their doors for COVID-19 research in what may be the most unified supercomputing effort in hist Read more…

By Oliver Peckham

Australian Researchers Break All-Time Internet Speed Record

May 26, 2020

If you’ve been stuck at home for the last few months, you’ve probably become more attuned to the quality (or lack thereof) of your internet connection. Even Read more…

By Oliver Peckham

15 Slides on Programming Aurora and Exascale Systems

May 7, 2020

Sometime in 2021, Aurora, the first planned U.S. exascale system, is scheduled to be fired up at Argonne National Laboratory. Cray (now HPE) and Intel are the k Read more…

By John Russell

Nvidia’s Ampere A100 GPU: Up to 2.5X the HPC, 20X the AI

May 14, 2020

Nvidia's first Ampere-based graphics card, the A100 GPU, packs a whopping 54 billion transistors on 826mm2 of silicon, making it the world's largest seven-nanom Read more…

By Tiffany Trader

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

By Doug Black

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

TACC Supercomputers Run Simulations Illuminating COVID-19, DNA Replication

March 19, 2020

As supercomputers around the world spin up to combat the coronavirus, the Texas Advanced Computing Center (TACC) is announcing results that may help to illumina Read more…

By Staff report

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This