ISC: Extreme-Scale Requirements to Push the Frontiers of Deep Learning

By Doug Black

June 17, 2017

Deep learning is the latest and most compelling technology strategy to take aim at the decades-old “drowning in data/starving for insight” problem. But contrary to the commonly held notion, deep learning is more than a big data problem per se. Delivering on deep learning’s potential – and achieving its anticipated 50 percent annual growth rate market opportunity – involves a highly demanding scaling problem that requires overlapping computational and communications capabilities as complex as any of the classic supercomputing challenges of the past.

That’s the view of Cray senior VP and CTO Steve Scott, who will discuss “pushing the frontiers of deep learning” at ISC in Frankfurt to close out Deep Learning Day (Wednesday, June 21) at the conference.

Scott told EnterpriseTech (HPCwire‘s sister publication) the focus of his session will be on training at-scale neural networks to handle complex deep learning applications: self-driving cars, facial recognition, robots sorting mail, supply-chain optimization and aiding in the search for oil and gas, to name a few.

“The main point I’ll be making is that we see a general convergence of data analytics and classic simulation and modeling HPC problems,” he said. “Deep learning folds into that, and the training problem in particular is a classic HPC problem.”

In short, greater machine intelligence requires larger, more complex models – with billions of model weights and hundreds of layers.

Ideally, Scott said, training neural networks using the stochastic gradient descent algorithm “you’d process one sample of that training data, then update the weights of your model, and then repeat that process with the next piece of training data and then update the weights of your model again.”

Cray’s Steve Scott

The problem, he said, is that it’s an inherently serial model. So even when using a single node, Scott said, users have traditionally broken up their training data into sets – called “mini-batches” – to speed up the process. The entire training process becomes much more difficult when you want to train your network not on one GPU, or 10 GPUs, but on a hundred or thousands of GPUs.

You can simplify training by using lesser amounts of data, but that leads to deep learning systems that haven’t been trained thoroughly enough and, therefore, aren’t intelligent enough. “If you have a small amount of data and you try to use it to train a very large neural network,” Scott said, “you end up with a phenomenon called ‘overfitting,’ where the model works very well for the training data you gave it, but it can’t generalize to new data and new situations.”

So scale is essential, and scale is a big challenge.

“Scaling up this training problem to large numbers of compute nodes brings up this classic problem of convergence of your model vs. the parallel speed you can get,” Scott said. “This is a really tough problem. If you have more compute nodes working in parallel you can process more samples per second. But now you’re doing more work each time, your processing more samples before you can update the model weights. So the problems of converging to the correct model becomes much more difficult.”

Scott will discuss the kind of system architecture required to take on deep learning training at scale, an architecture that – surprise! – Cray has been working on for years.

“It calls for a very strong interconnect [the fabric, or network, connecting the processors within the system], and it also has a lot to do with turning this into an MPI [the communications software used by the programs to communicate via the fabric] problem,” Scott said. “It calls for strong synchronization, it calls for overlapping your communications and your computation.

“We think bringing supercomputing technologies, from both a hardware and a software perspective, to bear can help speed up this deep learning problem that many people don’t think of. They think of it as a big data problem, not as a classic supercomputing problem. We think the core problem here in scaling these larger models is one in which supercomputing technology is uniquely qualified to address.”

Scott said deep learning has taken root to different degrees in different parts of the market. Hyperscalers (Google, Facebook, Microsoft, AWS, etc.) have thousands of projects under development with many, in voice and image recognition in particular, fully operational.

“It’s really past the tipping point,” Scott said. “The big hyperscalers have demonstrated that this stuff works and now they’re applying it all over the place.”

But the enterprise market, lacking the data and the compute resources of hyperscalers, remains for now in the experimentation and “thinking about it” phase, he said. “The enterprise space is quite a bit further behind. But they see the potential to apply it.” Organizations that are early adopters of IoT, with its attendant volumes of machine data, are and will be the early adopters of deep learning at scale.

“We’re seeing it applied to lots of different problems,” said Scott. “Many people, including me, are optimistic that every area of industry and science and beyond is going to have problems that are amenable to deep learning. We think it’s going to be very widespread, and it’s very large organizations with large amounts of data where it will take root first.”

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!

RSC Reports 500Tflops, Hot Water Cooled System Deployed at JINR

April 18, 2018

RSC, developer of supercomputers and advanced HPC systems based in Russia, today reported deployment of “the world's first 100% ‘hot water’ liquid cooled supercomputer” at Joint Institute for Nuclear Research (JI Read more…

By Staff

New Device Spots Quantum Particle ‘Fingerprint’

April 18, 2018

Majorana particles have been observed by university researchers employing a device consisting of layers of magnetic insulators on a superconducting material. The advance opens the door to controlling the elusive particle Read more…

By George Leopold

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’s introduction of an ARM-based system (XC-50) last November. Read more…

By John Russell

HPE Extreme Performance Solutions

Hybrid HPC is Speeding Time to Insight and Revolutionizing Medicine

High performance computing (HPC) is a key driver of success in many verticals today, and health and life science industries are extensively leveraging these capabilities. Read more…

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 Symposium on Computer Architecture (ISCA) in Los Angeles. The Read more…

By Staff

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

IBM: Software Ecosystem for OpenPOWER is Ready for Prime Time

April 16, 2018

With key pieces of the IBM/OpenPOWER versus Intel/x86 gambit settling into place – e.g., the arrival of Power9 chips and Power9-based systems, hyperscaler sup Read more…

By John Russell

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

Cloud-Readiness and Looking Beyond Application Scaling

April 11, 2018

There are two aspects to consider when determining if an application is suitable for running in the cloud. The first, which we will discuss here under the title Read more…

By Chris Downing

Transitioning from Big Data to Discovery: Data Management as a Keystone Analytics Strategy

April 9, 2018

The past 10-15 years has seen a stark rise in the density, size, and diversity of scientific data being generated in every scientific discipline in the world. Key among the sciences has been the explosion of laboratory technologies that generate large amounts of data in life-sciences and healthcare research. Large amounts of data are now being stored in very large storage name spaces, with little to no organization and a general unease about how to approach analyzing it. Read more…

By Ari Berman, BioTeam, Inc.

IBM Expands Quantum Computing Network

April 5, 2018

IBM is positioning itself as a first mover in establishing the era of commercial quantum computing. The company believes in order for quantum to work, taming qu Read more…

By Tiffany Trader

FY18 Budget & CORAL-2 – Exascale USA Continues to Move Ahead

April 2, 2018

It was not pretty. However, despite some twists and turns, the federal government’s Fiscal Year 2018 (FY18) budget is complete and ended with some very positi Read more…

By Alex R. Larzelere

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

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

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

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

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

Leading Solution Providers

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

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

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

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

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

  • arrow
  • Click Here for More Headlines
  • arrow
Share This