Berkeley Lab, Oak Ridge, NVIDIA Team Breaks Exaop Barrier With Deep Learning Application

October 8, 2018

Oct. 8, 2018 — A team of computational scientists from Lawrence Berkeley National Laboratory (Berkeley Lab) and Oak Ridge National Laboratory (ORNL) and engineers from NVIDIA has, for the first time, demonstrated an exascale-class deep learning application that has broken the exaop barrier.

Using a climate dataset from Berkeley Lab on ORNL’s Summit system at the Oak Ridge Leadership Computing Facility (OLCF), they trained a deep neural network to identify extreme weather patterns from high-resolution climate simulations. Summit is an IBM Power Systems AC922 supercomputer powered by more than 9,000 IBM POWER9 CPUs and 27,000 NVIDIA® Tesla® V100 Tensor Core GPUs. By tapping into the specialized NVIDIA Tensor Cores built into the GPUs at scale, the researchers achieved a peak performance of 1.13 exaops and a sustained performance of 0.999 – the fastest deep learning algorithm reported to date and an achievement that earned them a spot on this year’s list of finalists for the Gordon Bell Prize.

High-quality segmentation results produced by deep learning on climate datasets.

“This collaboration has produced a number of unique accomplishments,” said Prabhat, who leads the Data & Analytics Services team at Berkeley Lab’s National Energy Research Scientific Computing Center and is a co-author on the Gordon Bell submission. “It is the first example of deep learning architecture that has been able to solve segmentation problems in climate science, and in the field of deep learning, it is the first example of a real application that has broken the exascale barrier.”

These achievements were made possible through an innovative blend of hardware and software capabilities. On the hardware side, Summit has been designed to deliver 200 petaflops of high-precision computing performance and was recently named the fastest computer in the world, capable of performing more than three exaops (3 billion billion calculations) per second. The system features a hybrid architecture; each of its 4,608 compute nodes contains two IBM POWER9 CPUs and six NVIDIA Volta Tensor Core GPUs, all connected via the NVIDIA NVLink high-speed interconnect.The NVIDIA GPUs are a key factor in Summit’s performance, enabling up to 12 times higher peak teraflops for training and 6 times higher peak teraflops for inference in deep learning applications compared to its predecessor, the Tesla P100.

“Our partnering with Berkeley Lab and Oak Ridge National Laboratory showed the true potential of NVIDIA Tensor Core GPUs for AI and HPC applications,” said Michael Houston, senior distinguished engineer of deep learning at NVIDIA. “To make exascale a reality, our team tapped into the multi-precision capabilities packed into the thousands of NVIDIA Volta Tensor Core GPUs on Summit to achieve peak performance in training and inference in deep learning applications.”

Improved Scalability and Communication

On the software side, in addition to providing the climate dataset, the Berkeley Lab team developed pattern-recognition algorithms for training the DeepLabv3+ neural network to extract pixel-level classifications of extreme weather patterns, which could aid in the prediction of how extreme weather events are changing as the climate warms. According to Thorsten Kurth, an application performance specialist at NERSC who led this project, the team made modifications to DeepLabv3+ that improved the network’s scalability and communications capabilities and made the exaops achievement possible. This included tweaking the network to train it to extract pixel-level features and per-pixel classification and improve node-to-node communication.

“What is impressive about this effort is that we could scale a high-productivity framework like TensorFlow, which is technically designed for rapid prototyping on small to medium scales, to 4,560 nodes on Summit,” he said. “With a number of performance enhancements, we were able to get the framework to run on nearly the entire supercomputer and achieve exaop-level performance, which to my knowledge is the best achieved so far in a tightly coupled application.”

Other innovations included high-speed parallel data staging, an optimized data ingestion pipeline and multi-channel segmentation. Traditional image segmentation tasks work on three-channel red/blue/green images. But scientific datasets often comprise many channels; in climate, for example, these can include temperature, wind speeds, pressure values and humidity. By running the optimized neural network on Summit, the additional computational capabilities allowed the use of all 16 available channels, which dramatically improved the accuracy of the models.

“We have shown that we can apply deep-learning methods for pixel-level segmentation on climate data, and potentially on other scientific domains,” said Prabhat. “More generally, our project has laid the groundwork for exascale deep learning for science, as well as commercial applications.”

In addition to Prabhat, Houston and Kurth, the research team included Jack Deslippe, Mayur Mudigonda and Ankur Mahesh of Berkeley Lab; Michael Matheson of ORNL; and Sean Treichler, Joshua Romero, Nathan Luehr, Everett Phillips and Massimiliano Fatica of NVIDIA.

Established more than three decades ago by the Association for Computing Machinery, the Gordon Bell award recognizes outstanding achievement in the field of computing for applications in science, engineering and large-scale data science. This year’s winner will be announced at SC18 in November in Dallas, TX.

NERSC and OLCF are both DOE Office of Science User Facilities.


Source: Kathy Kincade, Berkeley Lab

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!

CFD on ORNL’s Titan Simulates Cleaner, Low-MPG ‘Opposed Piston’ Engine

December 13, 2018

Pinnacle Engines is out to substantially improve vehicle gasoline efficiency and cut greenhouse gas emissions with a new motor based on an “opposed piston” design that the company hopes will be widely adopted while t Read more…

By Doug Black

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC) is procuring from Atos in two phases over the next year-an Read more…

By Tiffany Trader

Nvidia Leads Alpha MLPerf Benchmarking Round

December 12, 2018

Seven months after the launch of its AI benchmarking suite, the MLPerf consortium is releasing the first round of results based on submissions from Nvidia, Google and Intel. Of the seven benchmarks encompassed in version Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

AI Can Be Scary. But Choosing the Wrong Partners Can Be Mortifying!

As you continue to dive deeper into AI, you will discover it is more than just deep learning. AI is an extremely complex set of machine learning, deep learning, reinforcement, and analytics algorithms with varying compute, storage, memory, and communications needs. Read more…

IBM Accelerated Insights

4 Ways AI Analytics Projects Fail — and How to Succeed

“How do I de-risk my AI-driven analytics projects?” This is a common question for organizations ready to modernize their analytics portfolio. Here are four ways AI analytics projects fail—and how you can ensure success. Read more…

Neural Network ‘Synapse’ Technology Showcased at IEEE Meeting

December 12, 2018

There’s nice snapshot of advancing work to develop improved neural network “synapse” technologies posted yesterday on IEEE Spectrum. Lower power, ease of use, manufacturability, and performance are all key paramete Read more…

By John Russell

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

Nvidia Leads Alpha MLPerf Benchmarking Round

December 12, 2018

Seven months after the launch of its AI benchmarking suite, the MLPerf consortium is releasing the first round of results based on submissions from Nvidia, Goog Read more…

By Tiffany Trader

IBM, Nvidia in AI Data Pipeline, Processing, Storage Union

December 11, 2018

IBM and Nvidia today announced a new turnkey AI solution that combines IBM Spectrum Scale scale-out file storage with Nvidia’s GPU-based DGX-1 AI server to pr Read more…

By Doug Black

Mellanox Uses Univa to Extend Silicon Design HPC Operation to Azure

December 11, 2018

Call it a corollary to Murphy’s Law: When a system is most in demand, when end users are most dependent on the system performing as required, when it’s crunch time – that’s when the system is most likely to blow up. Or make you wait in line to use it. Read more…

By Doug Black

Topology Can Help Us Find Patterns in Weather

December 6, 2018

Topology--the study of shapes--seems to be all the rage. You could even say that data has shape, and shape matters. Shapes are comfortable and familiar concepts, so it is intriguing to see that many applications are being recast to use topology. For instance, looking for weather and climate patterns. Read more…

By James Reinders

Zettascale by 2035? China Thinks So

December 6, 2018

Exascale machines (of at least a 1 exaflops peak) are anticipated to arrive by around 2020, a few years behind original predictions; and given extreme-scale performance challenges are not getting any easier, it makes sense that researchers are already looking ahead to the next big 1,000x performance goal post: zettascale computing. Read more…

By Tiffany Trader

Robust Quantum Computers Still a Decade Away, Says Nat’l Academies Report

December 5, 2018

The National Academies of Science, Engineering, and Medicine yesterday released a report – Quantum Computing: Progress and Prospects – whose optimism about Read more…

By John Russell

Revisiting the 2008 Exascale Computing Study at SC18

November 29, 2018

A report published a decade ago conveyed the results of a study aimed at determining if it were possible to achieve 1000X the computational power of the the Read more…

By Scott Gibson

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

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

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

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

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba 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

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

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

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

Google Releases Machine Learning “What-If” Analysis Tool

September 12, 2018

Training machine learning models has long been time-consuming process. Yesterday, Google released a “What-If Tool” for probing how data point changes affect a model’s prediction. The new tool is being launched as a new feature of the open source TensorBoard web application... Read more…

By John Russell

The Convergence of Big Data and Extreme-Scale HPC

August 31, 2018

As we are heading towards extreme-scale HPC coupled with data intensive analytics like machine learning, the necessary integration of big data and HPC is a curr Read more…

By Rob Farber

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