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!

Russian and American Scientists Achieve 50% Increase in Data Transmission Speed

September 20, 2018

As high-performance computing becomes increasingly data-intensive and the demand for shorter turnaround times grows, data transfer speed becomes an ever more important bottleneck. Now, in an article published in IEEE Tra Read more…

By Oliver Peckham

IBM to Brand Rescale’s HPC-in-Cloud Platform

September 20, 2018

HPC (or big compute)-in-the-cloud platform provider Rescale has formalized the work it’s been doing in partnership with public cloud vendors by announcing its Powered by Rescale program – with IBM as its first named Read more…

By Doug Black

Democratization of HPC Part 1: Simulation Sheds Light on Building Dispute

September 20, 2018

This is the first of three articles demonstrating the growing acceptance of High Performance Computing especially in new user communities and application areas. Major reasons for this trend are the ongoing improvements i Read more…

By Wolfgang Gentzsch

HPE Extreme Performance Solutions

Introducing the First Integrated System Management Software for HPC Clusters from HPE

How do you manage your complex, growing cluster environments? Answer that big challenge with the new HPC cluster management solution: HPE Performance Cluster Manager. Read more…

IBM Accelerated Insights

Clouds Over the Ocean – a Healthcare Perspective

Advances in precision medicine, genomics, and imaging; the widespread adoption of electronic health records; and the proliferation of medical Internet of Things (IoT) and mobile devices are resulting in an explosion of structured and unstructured healthcare-related data. Read more…

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 Gordon Bell Prize used Summit in their work. That’s impres Read more…

By John Russell

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

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Nvidia Accelerates AI Inference in the Datacenter with T4 GPU

September 14, 2018

Nvidia is upping its game for AI inference in the datacenter with a new platform consisting of an inference accelerator chip--the new Turing-based Tesla T4 GPU- Read more…

By George Leopold

DeepSense Combines HPC and AI to Bolster Canada’s Ocean Economy

September 13, 2018

We often hear scientists say that we know less than 10 percent of the life of the oceans. This week, IBM and a group of Canadian industry and government partner Read more…

By Tiffany Trader

Rigetti (and Others) Pursuit of Quantum Advantage

September 11, 2018

Remember ‘quantum supremacy’, the much-touted but little-loved idea that the age of quantum computing would be signaled when quantum computers could tackle Read more…

By John Russell

How FPGAs Accelerate Financial Services Workloads

September 11, 2018

While FSI companies are unlikely, for competitive reasons, to disclose their FPGA strategies, James Reinders offers insights into the case for FPGAs as accelerators for FSI by discussing performance, power, size, latency, jitter and inline processing. Read more…

By James Reinders

Update from Gregory Kurtzer on Singularity’s Push into FS and the Enterprise

September 11, 2018

Container technology is hardly new but it has undergone rapid evolution in the HPC space in recent years to accommodate traditional science workloads and HPC systems requirements. While Docker containers continue to dominate in the enterprise, other variants are becoming important and one alternative with distinctly HPC roots – Singularity – is making an enterprise push targeting advanced scale workload inclusive of HPC. Read more…

By John Russell

At HPC on Wall Street: AI-as-a-Service Accelerates AI Journeys

September 10, 2018

AIaaS – artificial intelligence-as-a-service – is the technology discipline that eases enterprise entry into the mysteries of the AI journey while lowering Read more…

By Doug Black

TACC Wins Next NSF-funded Major Supercomputer

July 30, 2018

The Texas Advanced Computing Center (TACC) has won the next NSF-funded big supercomputer beating out rivals including the National Center for Supercomputing Ap Read more…

By John Russell

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

Requiem for a Phi: Knights Landing Discontinued

July 25, 2018

On Monday, Intel made public its end of life strategy for the Knights Landing "KNL" Phi product set. The announcement makes official what has already been wide Read more…

By Tiffany Trader

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

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

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

By John Russell

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

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

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

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

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

D-Wave Breaks New Ground in Quantum Simulation

July 16, 2018

Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that 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

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

GPUs Power Five of World’s Top Seven Supercomputers

June 25, 2018

The top 10 echelon of the newly minted Top500 list boasts three powerful new systems with one common engine: the Nvidia Volta V100 general-purpose graphics proc 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

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