At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

By Doug Black

June 22, 2017

I’ve seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network designed to emulate and compete with the human brain. In this game, the brain doesn’t stand a chance.

Scoff at such talk as farfetched or far off in a hazy utopic/dystopic future. Roll your eyes and say we’ve heard the hype before (some of us remember a supercomputer company 25 years ago with the inflated name of “Thinking Machines,” long defunct). But it’s neither futuristic nor hype, it’s happening now, the technology pieces are taking shape, and the implications for business, for the work world and for our everyday lives – for good or ill – are as staggering as they are real.

Aside: It’s somewhat unsettling that conference attendees here in Frankfurt don’t seem particularly interested in those implications. For the moment, ISC is at the gathering point of computer scientists bringing about massive technological change, but nearly all the talk here is about the “how” of AI systems, not the “what then?” But there’s one anecdote making the rounds that has raised eyebrows: when Google engineers were asked to how its AlphaGo machine the winning move against the world champion of Go (the world’s most complex board game), the answer was: “We don’t know” (more on this below).

Quite consciously, engineers are architecting HPC systems along the lines of our brain. The new architecture is an emerging style of computing called “data intensive” or “data centric.” It replaces processing with memory (i.e., data) at the center of the computing universe. Combined with advanced algorithms, new memory and processor technologies are coming on line to make the new architecture a practical reality. Once the pieces are in place, the next step will be to scale these systems beyond all measure of human brain capacity.

What does data centric computing mean? How does it work? Why does it represent a major shift in advanced scale computing?

Let’s start answering those questions by first looking at how data centric systems are measured. The benchmark for new AI systems isn’t how fast they solve linear algebra problems (i.e., Linpack). That’s how processor-centric systems have been measured for decades, and considering the capabilities of data-centric systems under development, that benchmark seems wholly inadequate.

Rather than throughput, AI-based systems are measured in relation to people: their ability to compete with humans at our most intellectually challenging games of reason – checkers, chess, Go, poker. The standard of success isn’t training the system to become perfect at it, or to “solve” the game (i.e., work out every possible combination of moves). The benchmark is playing the game better than any human.

That’s the objective. Once the system is better than any of us, it’s ready to move into an advisory role, providing guidance and suggestions, augmenting our capabilities. For now. In a decade or so, these systems will take over tasks for us altogether.

Driving is a prime example. If driving were a game, humans would still beat machines – even though statistics show we’re getting worse at it (according to Dr. Pradeep Dubey, Intel Fellow, Intel Labs & Director, Parallel Computing Lab, who presented at ISC on autonomous vehicle technology). Around the world, two people are killed in car accidents each minute. In the U.S., 40,000 people are killed annually and 2 million suffer permanent injuries.

Meanwhile, AI is enabling machines to get better at driving. A convergence point is coming. For now, the car’s intelligence is limited to navigating, warning us about traffic conditions and setting off beepers when we get close to curbs and other cars.

The next step: our roads will have special lanes where we’ll temporarily hand over operation of the car to itself. A few years after that, we won’t drive at all. Driving is a game in which machines will soon be much better than we are.

Dr. Eng Lim Goh, Vice President of HPE and an industry visionary for decades, is a prime driver of new AI system development. At ISC this week, he discussed why AI in all its forms – machine learning, deep learning, strategic reasoning, etc. – is the driving force bringing about “data intensive” computing architectures.

Here’s his schema for the data intensive computer:

The left side of the diagram is old-style, LINPACK-benchmarked, processor-centric computing. That’s where HPC happens. The processor is at the center. Data is sent to the CPU, simulations are run, and new, and more, data comes out. These systems have hit a wall of their own making. The problem occurs when HPC systems run their simulations, generating exponentially more machine-generated data than they started with. They’re producing data beyond the capability of data scientists to analyze. Big data isn’t big enough.

“For 30 years we’ve lived in this world where small amounts of data go in, and we apply supercomputing power onto our partial differential equations, or our models, to generate lots of data,” he said.

Already, Goh pointed out, there aren’t enough data scientists to meet demand for today’s data analytics requirements. For the torrents of machine-generated data to come, there’s an overwhelming need to automate how data is analyzed.

Take for example seismic exploration.

For exploration of energy reserves at sea, ships drag cables with hydrophones, fire shots into the ocean floor and collect the echo on sensors. Goh said for every 10TB of data collected by the sensors, 1PB of simulation data is produced – 100X the original data.

That’s where the right side of the diagram comes in: high performance analytics (HPA), self-learning AI systems that can take voluminous amounts of data produced by HPC, put it in memory, and work up answers to questions.

Dr. Eng Lim Goh

The key to the data-centric system of the future is the border area in the middle of the diagram. That’s where memory (i.e., data) resides, like a queen bee. It will be surrounded by a variety of processors (CPUs, GPUs, FPGAs, ASICs, assigned jobs appropriate for their capabilities) operating around the data, like drones.

Looked at this way, in a world where most companies have analyzed only about 3 percent of their data on average, traditional HPC systems seem glaringly incomplete. Combining the left side of the diagram and the right, integrating HPC with HPA – that takes supercomputing somewhere new. That’s a machine with a new soul.

But Goh conceded there are barriers to HPC and HPA joining forces.

“The two worlds are very different,” Goh said. “The HPC world where I lived, I’m guilty of this. All these years we assumed data movement was free. Guess what? When Linpack started 20 years ago we didn’t consider data movement. Yet we’re still ranking our Top500 systems that way. We’re still guilty that way.

“But the data scientists of the world also have something to say about us,” he added. “They assume compute is free. Take Hadoop. Hadoop is a technique where you map your data out onto compute nodes, then do your computation, then you reduce the data you bring back. The data world called this MapReduce. So we have to bring the two worlds together. More and more now, people should be investing in one system of left and right, not just the left.”

Goh pointed to the middle of his diagram and said that’s where the big architectural challenge lies. “If you have to move an exabyte of data between system A and B, if they are two different systems, it will be impractical. The world will come to this (integration of HPC and HPA).”

That’s why the U.S. effort to develop a “capable” exascale computer by the early 2020’s puts as much emphasis on compute power as memory capacity. A mission document issued by the Exascale Computing Project said its intent to build a system not just with an exaflop of processing power but one that also can handle an Exabyte of data in memory.

Goh described HPE’s “Bridges” system at the Pittsburgh Supercomputer Center as a data-centric supercomputer that incorporates HPC and HPA, designed specifically for “scalable deep learning.”

“Essentially, it’s a bandwidth machine,” Goh said. “It’s a supercomputer, but really it’s a data mover. Not only are NVlinks all connected, they’re also GPU-connected, so clumps of four GPUs can talk to other clumps of four GPUs directly. Then we have four OPA’s coming out of each node, giving one OPA per GPU. So this is really a data machine.”

The Bridges supercomputer pulled off one of the most impressive game wins of the emerging AI era when it defeated four of the world’s top poker players earlier this year. Actually, the competition stretched across two years, Goh said, with the AI system losing $700,000 to the players the first year they played. The second year, with 10X more compute from the Bridges computer, the AI system (“Libratus”) took the four humans for $1.7 million, a classic hustle.

While IBM Deep Blue (chess) and Google’s AlphaGo have grabbed most of the machine-defeated-human headlines of late, it’s less well known that machines have beaten humans at checkers, which has 1020 “naïve” (or possible) combinations, since the early 1990s, several years before IBM beat the world’s top chess player. Chess has 1047 naïve combinations. How big is 1047? An exascale machine running for 100 years would complete only 1028 combinations. The point being that without integrated AI techniques, processing only gets you so far.

Go, meanwhile, has 10171 combinations. Poker, with “only” 10160 combinations, offers up the added complexity of “incomplete information.” By contrast with the three board games, in which you can see the pieces held by your opponent, in poker, you don’t know what your opponents have in their hands.

“So we didn’t solve chess, machines didn’t solve chess,” Goh said, “all they did was be good enough to be superhuman – to beat any human. That’s a term were going to hear more and more now.”

After Goh’s presentation, he was asked to response to Google not understanding how AlpaGo won the Go tournament. The issue, he said, is overcoming opacity.

“We’re working very hard to increasing transparency,” he said. “Some people have discussed the idea that there are many stages in a neural network, to intercept it in between those stages, and take its output and see if you can make sense of it.”

Leaving a strong role for human supervision also is important. He pointed out that since the Industrial Revolution, workers get promoted from first operating a machine to supervising machines.

He also discussed the distinction between the “correct” and the “right” answer. An AI-based system may deliver a correct answer, but whether it’s “right” – acceptable within human social mores, the bounds of business ethics, or even an aesthetic judgment – is something only humans can decide.

“Societal values need to be applied, human values need to be applied,” he said.

 

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!

ASC18: Final Results Revealed & Wrapped Up

May 17, 2018

It was an exciting week at ASC18 in Nanyang, China. The student teams braved extreme heat, extremely difficult applications, and extreme competition in order to cross the cluster competition finish line. The gala awards ceremony took place on Wednesday. The auditorium was packed with student teams, various dignitaries, the media, and other interested parties. So what happened? Read more…

By Dan Olds

ASC18: Tough Applications & Tough Luck

May 17, 2018

The applications at the ASC18 Student Cluster Competition were tough. Tougher than the $3.99 steak special at your local greasy spoon restaurant. The apps are so tough that even Chuck Norris backs away from them slowly. Read more…

By Dan Olds

Spring Meetings Underscore Quantum Computing’s Rise

May 17, 2018

The month of April 2018 saw four very important and interesting meetings to discuss the state of quantum computing technologies, their potential impacts, and the technology challenges ahead. These discussions happened in Read more…

By Alex R. Larzelere

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

Mastering the Big Data Challenge in Cognitive Healthcare

Patrick Chain, genomics researcher at Los Alamos National Laboratory, posed a question in a recent blog: What if a nurse could swipe a patient’s saliva and run a quick genetic test to determine if the patient’s sore throat was caused by a cold virus or a bacterial infection? Read more…

Quantum Network Hub Opens in Japan

May 17, 2018

Following on the launch of its Q Commercial quantum network last December with 12 industrial and academic partners, the official Japanese hub at Keio University is now open to facilitate the exploration of quantum applications important to science and business. The news comes a week after IBM announced that North Carolina State University was the first U.S. university to join its Q Network. Read more…

By Tiffany Trader

ASC18: Final Results Revealed & Wrapped Up

May 17, 2018

It was an exciting week at ASC18 in Nanyang, China. The student teams braved extreme heat, extremely difficult applications, and extreme competition in order to cross the cluster competition finish line. The gala awards ceremony took place on Wednesday. The auditorium was packed with student teams, various dignitaries, the media, and other interested parties. So what happened? Read more…

By Dan Olds

Spring Meetings Underscore Quantum Computing’s Rise

May 17, 2018

The month of April 2018 saw four very important and interesting meetings to discuss the state of quantum computing technologies, their potential impacts, and th Read more…

By Alex R. Larzelere

Quantum Network Hub Opens in Japan

May 17, 2018

Following on the launch of its Q Commercial quantum network last December with 12 industrial and academic partners, the official Japanese hub at Keio University is now open to facilitate the exploration of quantum applications important to science and business. The news comes a week after IBM announced that North Carolina State University was the first U.S. university to join its Q Network. Read more…

By Tiffany Trader

Democratizing HPC: OSC Releases Version 1.3 of OnDemand

May 16, 2018

Making HPC resources readily available and easier to use for scientists who may have less HPC expertise is an ongoing challenge. Open OnDemand is a project by t Read more…

By John Russell

PRACE 2017 Annual Report: Exascale Aspirations; Industry Collaboration; HPC Training

May 15, 2018

The Partnership for Advanced Computing in Europe (PRACE) today released its annual report showcasing 2017 activities and providing a glimpse into thinking about Read more…

By John Russell

US Forms AI Brain Trust

May 11, 2018

Amid calls for a U.S. strategy for promoting AI development, the Trump administration is forming a senior-level panel to help coordinate government and industry research efforts. The Select Committee on Artificial Intelligence was announced Thursday (May 10) during a White House summit organized by the Office of Science and Technology Policy (OSTP). Read more…

By George Leopold

Emerging Advanced Scale Tech Trends Focus of Annual Tabor Conference

May 9, 2018

At Tabor Communications' annual Advanced Scale Forum (ASF) held this week in Austin, the focus was on enterprise adoption of HPC-class technologies and high performance data analytics (HPDA). It’s a confab that brings together end users (CIOs, IT planners, department heads) and vendors and encourages... Read more…

By the Editorial Team

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

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

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

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

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

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

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

Leading Solution Providers

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

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

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

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

HPE Wins $57 Million DoD Supercomputing Contract

February 20, 2018

Hewlett Packard Enterprise (HPE) today revealed details of its massive $57 million HPC contract with the U.S. Department of Defense (DoD). The deal calls for HP Read more…

By Tiffany Trader

CFO Steps down in Executive Shuffle at Supermicro

January 31, 2018

Supermicro yesterday announced senior management shuffling including prominent departures, the completion of an audit linked to its delayed Nasdaq filings, and Read more…

By John Russell

Deep Learning Portends ‘Sea Change’ for Oil and Gas Sector

February 1, 2018

The billowing compute and data demands that spurred the oil and gas industry to be the largest commercial users of high-performance computing are now propelling Read more…

By Tiffany Trader

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

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