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!

Global QC Market Projected to Grow to More Than $800 million by 2024

September 28, 2020

The Quantum Economic Development Consortium (QED-C) and Hyperion Research are projecting that the global quantum computing (QC) market - worth an estimated $320 million in 2020 - will grow at an anticipated 27% CAGR betw Read more…

By Staff Reports

DoE’s ASCAC Backs AI for Science Program that Emulates the Exascale Initiative

September 28, 2020

Roughly a year after beginning formal efforts to explore an AI for Science initiative the Department of Energy’s Advanced Scientific Computing Advisory Committee last week accepted a subcommittee report calling for a t Read more…

By John Russell

Supercomputer Research Aims to Supercharge COVID-19 Antiviral Remdesivir

September 25, 2020

Remdesivir is one of a handful of therapeutic antiviral drugs that have been proven to improve outcomes for COVID-19 patients, and as such, is a crucial weapon in the fight against the pandemic – especially in the abse Read more…

By Oliver Peckham

NOAA Announces Major Upgrade to Ensemble Forecast Model, Extends Range to 35 Days

September 23, 2020

A bit over a year ago, the United States’ Global Forecast System (GFS) received a major upgrade: a new dynamical core – its first in 40 years – called the finite-volume cubed-sphere, or FV3. Now, the National Oceanic and Atmospheric Administration (NOAA) is bringing the FV3 dynamical core to... Read more…

By Oliver Peckham

AI Silicon Startup Graphcore Launches Channel Partner Program

September 23, 2020

AI compute platform vendor Graphcore has launched its first formal global channel partner program to promote and boost the sales of its AI processors and blade computing products. The formalized, all-new Graphcore Elite Partner Program follows the company’s past history of working with several... Read more…

By Todd R. Weiss

AWS Solution Channel

The Water Institute of the Gulf runs compute-heavy storm surge and wave simulations on AWS

The Water Institute of the Gulf (Water Institute) runs its storm surge and wave analysis models on Amazon Web Services (AWS)—a task that sometimes requires large bursts of compute power. Read more…

Intel® HPC + AI Pavilion

Berlin Institute of Health: Putting HPC to Work for the World

Researchers from the Center for Digital Health at the Berlin Institute of Health (BIH) are using science to understand the pathophysiology of COVID-19, which can help to inform the development of targeted treatments. Read more…

Arm Targets HPC with New Neoverse Platforms

September 22, 2020

UK-based semiconductor design company Arm today teased details of its Neoverse roadmap, introducing V1 (codenamed Zeus) and N2 (codenamed Perseus), Arm’s second generation N-series platform. The chip IP vendor said the new platforms will deliver 50 percent and 40 percent more... Read more…

By Tiffany Trader

DoE’s ASCAC Backs AI for Science Program that Emulates the Exascale Initiative

September 28, 2020

Roughly a year after beginning formal efforts to explore an AI for Science initiative the Department of Energy’s Advanced Scientific Computing Advisory Commit Read more…

By John Russell

NOAA Announces Major Upgrade to Ensemble Forecast Model, Extends Range to 35 Days

September 23, 2020

A bit over a year ago, the United States’ Global Forecast System (GFS) received a major upgrade: a new dynamical core – its first in 40 years – called the finite-volume cubed-sphere, or FV3. Now, the National Oceanic and Atmospheric Administration (NOAA) is bringing the FV3 dynamical core to... Read more…

By Oliver Peckham

Arm Targets HPC with New Neoverse Platforms

September 22, 2020

UK-based semiconductor design company Arm today teased details of its Neoverse roadmap, introducing V1 (codenamed Zeus) and N2 (codenamed Perseus), Arm’s second generation N-series platform. The chip IP vendor said the new platforms will deliver 50 percent and 40 percent more... Read more…

By Tiffany Trader

Oracle Cloud Deepens HPC Embrace with Launch of A100 Instances, Plans for Arm, More 

September 22, 2020

Oracle Cloud Infrastructure (OCI) continued its steady ramp-up of HPC capabilities today with a flurry of announcements. Topping the list is general availabilit Read more…

By John Russell

European Commission Declares €8 Billion Investment in Supercomputing

September 18, 2020

Just under two years ago, the European Commission formalized the EuroHPC Joint Undertaking (JU): a concerted HPC effort (comprising 32 participating states at c Read more…

By Oliver Peckham

Google Hires Longtime Intel Exec Bill Magro to Lead HPC Strategy

September 18, 2020

In a sign of the times, another prominent HPCer has made a move to a hyperscaler. Longtime Intel executive Bill Magro joined Google as chief technologist for hi Read more…

By Tiffany Trader

Future of Fintech on Display at HPC + AI Wall Street

September 17, 2020

Those who tuned in for Tuesday's HPC + AI Wall Street event got a peak at the future of fintech and lively discussion of topics like blockchain, AI for risk man Read more…

By Alex Woodie, Tiffany Trader and Todd R. Weiss

IBM’s Quantum Race to One Million Qubits

September 15, 2020

IBM today outlined its ambitious quantum computing technology roadmap at its virtual Quantum Summit. The eye-popping million qubit number is still far out, agrees IBM, but perhaps not that far out. Just as eye-popping is IBM’s nearer-term plan for a 1,000-plus qubit system named Condor... Read more…

By John Russell

Supercomputer-Powered Research Uncovers Signs of ‘Bradykinin Storm’ That May Explain COVID-19 Symptoms

July 28, 2020

Doctors and medical researchers have struggled to pinpoint – let alone explain – the deluge of symptoms induced by COVID-19 infections in patients, and what Read more…

By Oliver Peckham

Nvidia Said to Be Close on Arm Deal

August 3, 2020

GPU leader Nvidia Corp. is in talks to buy U.K. chip designer Arm from parent company Softbank, according to several reports over the weekend. If consummated Read more…

By George Leopold

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

Intel’s 7nm Slip Raises Questions About Ponte Vecchio GPU, Aurora Supercomputer

July 30, 2020

During its second-quarter earnings call, Intel announced a one-year delay of its 7nm process technology, which it says it will create an approximate six-month shift for its CPU product timing relative to prior expectations. The primary issue is a defect mode in the 7nm process that resulted in yield degradation... Read more…

By Tiffany Trader

Google Hires Longtime Intel Exec Bill Magro to Lead HPC Strategy

September 18, 2020

In a sign of the times, another prominent HPCer has made a move to a hyperscaler. Longtime Intel executive Bill Magro joined Google as chief technologist for hi Read more…

By Tiffany Trader

HPE Keeps Cray Brand Promise, Reveals HPE Cray Supercomputing Line

August 4, 2020

The HPC community, ever-affectionate toward Cray and its eponymous founder, can breathe a (virtual) sigh of relief. The Cray brand will live on, encompassing th Read more…

By Tiffany Trader

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

European Commission Declares €8 Billion Investment in Supercomputing

September 18, 2020

Just under two years ago, the European Commission formalized the EuroHPC Joint Undertaking (JU): a concerted HPC effort (comprising 32 participating states at c Read more…

By Oliver Peckham

Leading Solution Providers

Contributors

Oracle Cloud Infrastructure Powers Fugaku’s Storage, Scores IO500 Win

August 28, 2020

In June, RIKEN shook the supercomputing world with its Arm-based, Fujitsu-built juggernaut: Fugaku. The system, which weighs in at 415.5 Linpack petaflops, topp Read more…

By Oliver Peckham

Google Cloud Debuts 16-GPU Ampere A100 Instances

July 7, 2020

On the heels of the Nvidia’s Ampere A100 GPU launch in May, Google Cloud is announcing alpha availability of the A100 “Accelerator Optimized” VM A2 instance family on Google Compute Engine. The instances are powered by the HGX A100 16-GPU platform, which combines two HGX A100 8-GPU baseboards using... Read more…

By Tiffany Trader

DOD Orders Two AI-Focused Supercomputers from Liqid

August 24, 2020

The U.S. Department of Defense is making a big investment in data analytics and AI computing with the procurement of two HPC systems that will provide the High 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

Microsoft Azure Adds A100 GPU Instances for ‘Supercomputer-Class AI’ in the Cloud

August 19, 2020

Microsoft Azure continues to infuse its cloud platform with HPC- and AI-directed technologies. Today the cloud services purveyor announced a new virtual machine Read more…

By Tiffany Trader

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

Joliot-Curie Supercomputer Used to Build First Full, High-Fidelity Aircraft Engine Simulation

July 14, 2020

When industrial designers plan the design of a new element of a vehicle’s propulsion or exterior, they typically use fluid dynamics to optimize airflow and in Read more…

By Oliver Peckham

Intel Speeds NAMD by 1.8x: Saves Xeon Processor Users Millions of Compute Hours

August 12, 2020

Potentially saving datacenters millions of CPU node hours, Intel and the University of Illinois at Urbana–Champaign (UIUC) have collaborated to develop AVX-512 optimizations for the NAMD scalable molecular dynamics code. These optimizations will be incorporated into release 2.15 with patches available for earlier versions. Read more…

By Rob Farber

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