Nvidia Sees Bright Future for AI Supercomputing

By Tiffany Trader

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Most prominent wins were achieving the number one spot on the Green500 list with new in-house DGX-1 supercomputer, SaturnV, and partnering with the National Cancer Institute, the U.S. Department of Energy (DOE) and several national laboratories to accelerate cancer research as part of the Cancer Moonshot initiative.

The company kicked off its SC activities with a press briefing on Monday (Nov. 14), during which CEO Jen-Hsun Huang characterized 2016 as a tipping point for the GPU computing approach popularized by Nvidia for over a decade.

Not surprisingly, Huang’s main message was that the GPU computing era has arrived. Throughout the hour-long talk, Huang would revisit the theme of deep learning as both a supercomputing problem and a supercomputing opportunity.

“We believe that supercomputers ought to be designed as AI supercomputers – meaning it has to be good at both computational science as well as data science – that building a machine that’s only good at data science doesn’t make sense and building a supercomputer that’s only good at computational science doesn’t make sense,” he said.

“On the one hand, deep learning requires an enormous amount of data throughput processing – this way of developing software where the computers write software themselves inspired by a lot of data processing behind it is a very important approach to computing but it also has the wonderful opportunity to benefit supercomputing as well, solving problems for science that hasn’t been possible before today,” said Huang.

Huang’s view is that traditional numerical HPC is not going anywhere, but will exist side by side with machine learning methods.

“I’m a big fan of using math when you can; we should use AI when you can’t,” he said. “For example what’s the equation of a cat? It’s probably very similar to the equation for a dog – two ears, four legs, a tail. And so there are a lot of areas where equations don’t work and that’s where I see AI – search problems, recommendation problems, likelihood problems, where there’s either too much data, incomplete data, or no laws of physics that support it. So where do I feel like eating tonight – there’s no laws of physics for that. There’s a lot of these type of problems that we simply can’t solve – I think that they’re going to coexist.”

While Nvidia is enabling parallel computing via thousands of CUDA cores combined with the CUDA programing framework, the CEO emphasized the necessity of a performant central processing unit. “Almost everything we do we start with a strong CPU,” said Huang. “We still believe in Amdahl’s law; we believe that code has a lot of single threaded parts to it and this is an area that we want to continue to be good at.”

nvidia-nvlink-dgx-1-ibm-p8

The two servers currently shipping with the NVLink P100 GPU – Nvidia’s DGX-1 server and IBM’s Minsky platform – speak to this goal. The DGX-1 connects eight NVLink’d Pascal P100s to two 20-core Intel Xeon E5-2698 v4 chips. The IBM Minsky server leverages two Power8 CPUs and four P100 GPUs connected by NVlink up to the CPUs.

Nvidia’s 124-node supercomputer, SaturnV plays a crucial role in Nvidia’s plans to usher in AI supercomputing. The machine debuted on the 48th TOP500 list at number 28 with 3.3 petaflops Linpack (4.9 petaflops peak). Even more impressively, it nabbed the number one spot on the Green500 list achieving more than 8.17 gigaflops/watt. That’s a 42 percent improvement from the 6.67 gigaflops/watt delivered by the most efficient machine on the previous TOP500 list. Extrapolating to exascale gives us 105.7 MW. If we go with a semi-“relaxed” exascale power allowance of 30 MW (the original DARPA target was 20 MW), this is less than one-fourth the planned power consumption of US exascale systems. Three years ago, the extrapolated delta was over a 7X.

SaturnV – its name inspired by the original Moonshot – will be a critical part of the CANDLE (CANcer Distributed Learning Environment) project (covered here). Announced last month, CANDLE’s mission is to exploit high performance computing (HPC), machine learning and data analytics technologies to advance precision oncology. Huang said the partners will be working together to develop “the world’s first deep learning framework designed for exascale.”

“It’s going to be really hard,” he added. “That’s why we’re working with the four DOE labs and have all standardized on the same architecture – SaturnV is the biggest one of them but we’re all using exactly the same architecture and it’s all GPU accelerated and we’re going to develop a framework that allows us to scale to get to exascale.”

Huang noted that when you apply deep learning FLOPS math – aka 16-bit floating point operations as opposed to the HPC norm of 64-bit FLOPS, exascale is not far away at all.

The [IBM/Nvidia] CORAL machines are on track for 2018 with 300 petaflops peak FP64, which comes out to 1,200 peak FP16, Huang pointed out. “For AI, FP16 is fine, now in some areas we need FP32, we need variable precision, but that’s the point,” he said. “I think CORAL is going to be the world’s fastest AI supercomputer [and] I think that we didn’t know it then but I believe that we are building an exascale machine already.”

It’s a fair point that dialing down the bits increases data throughput (boosting FLOPS), but as one analyst at the event said, “calling it exascale is changing the rules.”

Lending more insight to Nvidia’s plans was Solutions Architect Louis Capps, who presented at the Green500 BoF on November 16.

“This is completely a research platform,” he said of SaturnV. “We’re going to have academics using it. We’re going to have partnerships, collaborations, and internally, we’re working on our deep learning research and our HPC research.”

Embedded, robotics, automotive, and hyperscale computing are all major focus areas, but Capps and Huang both were most effusive about the opportunities at the convergence of data science and HPC. “We’re just now starting to bridge where real HPC work is converging with deep learning,” said Capps.

nvidia_dgx_saturnv-800xSaturnV is organized into five 3U boxes per rack, with 15 kilowatt of power on each rack and some 25 racks total. While the press photo of SaturnV indicates 10 servers per rack, this is not reflective of what’s inside. “We could not put that many in ours,” said Capps. “We put this in a datacenter which is not HPC. It was an IT datacenter originally.”

SaturnV was one of two systems on the newly published TOP500 list to employ the Pascal-based P100 GPUs. The number two greenest super, Piz Daint is using the PCIe variants. Installed at the Swiss National Supercomputing Centre, Piz Daint delivers an energy-efficiency rating of 7.45 gigaflops/watt. Refreshed with the new P100 hardware, Piz Daint achieved 9.8 petaflops on the Linpack benchmark, securing it the eighth spot on the latest list.

Notably, every single one of the top ten systems on the Green500 list is using some flavor of acceleration or manycore. There is no pure-play traditional x86 in the bunch.

green500-nov-2016-top-10
Source: Top500/Green500

A compelling testament to this approach came from Thomas Schulthess, director of the Swiss National Supercomputing Centre, where Nvidia K80 GPUs have been used for operational weather forecasting for over a year now. “I know the HPC community has a problem with the heterogeneous approach,” he said. “We’ve done a lot of analysis on this issue. We asked, what would the goals we have at exascale look like if we build a homogeneous Xeon-based system, and there’s no way that you will run significant problems that are significantly bigger and faster than we do today in 5-6 years at exascale if you build it based on a Xeon system.

“The message to the application folks is, you’ve had time to think about it now, but now there is no more choice. If you want to run at exascale, it is going to be on Xeon Phi or GPU-accelerated or the lightweight core, almost Cell-like architectures that we see on TaihuLight.”

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!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference phase of neural networks (NN). Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. 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

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Leading Solution Providers

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

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