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!

Tribute: Dr. Bob Borchers, 1936-2018

June 21, 2018

Dr. Bob Borchers, a leader in the high performance computing community for decades, passed away peacefully in Maui, Hawaii, on June 7th. His memorial service will be held on June 22nd in Reston, Virginia. Dr. Borchers Read more…

By Ann Redelfs

ISC 2018 Preview from @hpcnotes

June 21, 2018

Prepare for your social media feed to be saturated with #HPC, #ISC18, #Top500, etc. Prepare for your mainstream media to talk about supercomputers (in between the hourly commentary on Brexit, the FIFA World Cup, or US pr Read more…

By Andrew Jones

AMD’s EPYC Road to Redemption in Six Slides

June 21, 2018

A year ago AMD returned to the server market with its EPYC processor line. The earth didn’t tremble but folks took notice. People remember the Opteron fondly but later versions of the Bulldozer line not so much. Fast f Read more…

By John Russell

HPE Extreme Performance Solutions

HPC and AI Convergence is Accelerating New Levels of Intelligence

Data analytics is the most valuable tool in the digital marketplace – so much so that organizations are employing high performance computing (HPC) capabilities to rapidly collect, share, and analyze endless streams of data. Read more…

IBM Accelerated Insights

Preview the World’s Smartest Supercomputer at ISC 2018

Introducing an accelerated IT infrastructure for HPC & AI workloads Read more…

Why Student Cluster Competitions are Better than World Cup

June 21, 2018

My last article about the ISC18 Student Cluster Competition, titled “World Cup is Lame Compared to This Competition”, may have implied that I believe Student Cluster Competitions are better than World Cup soccer in s Read more…

By Dan Olds

ISC 2018 Preview from @hpcnotes

June 21, 2018

Prepare for your social media feed to be saturated with #HPC, #ISC18, #Top500, etc. Prepare for your mainstream media to talk about supercomputers (in between t Read more…

By Andrew Jones

AMD’s EPYC Road to Redemption in Six Slides

June 21, 2018

A year ago AMD returned to the server market with its EPYC processor line. The earth didn’t tremble but folks took notice. People remember the Opteron fondly Read more…

By John Russell

European HPC Summit Week and PRACEdays 2018: Slaying Dragons and SHAPEing Futures One SME at a Time

June 20, 2018

The University of Ljubljana in Slovenia hosted the third annual EHPCSW18 and fifth annual PRACEdays18 events which opened May 29, 2018. The conference was chair Read more…

By Elizabeth Leake (STEM-Trek for HPCwire)

Cray Introduces All Flash Lustre Storage Solution Targeting HPC

June 19, 2018

Citing the rise of IOPS-intensive workflows and more affordable flash technology, Cray today introduced the L300F, a scalable all-flash storage solution whose p Read more…

By John Russell

Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

The Machine Learning Hype Cycle and HPC

June 14, 2018

Like many other HPC professionals I’m following the hype cycle around machine learning/deep learning with interest. I subscribe to the view that we’re probably approaching the ‘peak of inflated expectation’ but not quite yet starting the descent into the ‘trough of disillusionment. This still raises the probability that... Read more…

By Dairsie Latimer

Xiaoxiang Zhu Receives the 2018 PRACE Ada Lovelace Award for HPC

June 13, 2018

Xiaoxiang Zhu, who works for the German Aerospace Center (DLR) and Technical University of Munich (TUM), was awarded the 2018 PRACE Ada Lovelace Award for HPC for her outstanding contributions in the field of high performance computing (HPC) in Europe. Read more…

By Elizabeth Leake

U.S Considering Launch of National Quantum Initiative

June 11, 2018

Sometime this month the U.S. House Science Committee will introduce legislation to launch a 10-year National Quantum Initiative, according to a recent report by Read more…

By John Russell

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Hennessy & Patterson: A New Golden Age for Computer Architecture

April 17, 2018

On Monday June 4, 2018, 2017 A.M. Turing Award Winners John L. Hennessy and David A. Patterson will deliver the Turing Lecture at the 45th International Sympo Read more…

By Staff

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

Google I/O 2018: AI Everywhere; TPU 3.0 Delivers 100+ Petaflops but Requires Liquid Cooling

May 9, 2018

All things AI dominated discussion at yesterday’s opening of Google’s I/O 2018 developers meeting covering much of Google's near-term product roadmap. The e Read more…

By John Russell

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

Nvidia Ups Hardware Game with 16-GPU DGX-2 Server and 18-Port NVSwitch

March 27, 2018

Nvidia unveiled a raft of new products from its annual technology conference in San Jose today, and despite not offering up a new chip architecture, there were still a few surprises in store for HPC hardware aficionados. Read more…

By Tiffany Trader

Part One: Deep Dive into 2018 Trends in Life Sciences HPC

March 1, 2018

Life sciences is an interesting lens through which to see HPC. It is perhaps not an obvious choice, given life sciences’ relative newness as a heavy user of H Read more…

By John Russell

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Google Charts Two-Dimensional Quantum Course

April 26, 2018

Quantum error correction, essential for achieving universal fault-tolerant quantum computation, is one of the main challenges of the quantum computing field and it’s top of mind for Google’s John Martinis. At a presentation last week at the HPC User Forum in Tucson, Martinis, one of the world's foremost experts in quantum computing, emphasized... Read more…

By Tiffany Trader

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’ Read more…

By John Russell

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