Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

By Rob Farber

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate scientists the ability to use machine learning to identify extreme weather events in huge climate simulation datasets. Predictive accuracies ranging from 89.4% to as high as 99.1% show that trained deep learning neural networks (DNNs) can identify weather fronts, tropical cyclones, and long narrow air flows that transport water vapor from the tropics called atmospheric rivers. As with image recognition, Michael Wehner (senior staff scientist, LBNL) noted they found the machine learning output outperforms humans. [i]

The strong relationship between ground truth and the neural network prediction can be seen in the classification plus regression results reported by Wehner at the recent Intel Developer Conference in Denver, Colorado.

Figure 1: Relation between ground truth (green boxes) and classification plus regression results (red boxes) of the DNN trained to recognize atmospheric phenomena (results courtesy NERSC)

When explaining the importance of this work, Wehner believes that the big impact lies in assessing the impact of climate change as exemplified by the recent painful experiences of hurricanes Harvey (tied with hurricane Katrina as the costliest tropical cyclone on record), Irma (the strongest storm on record to exist in the open Atlantic region), and Maria (regarded as the worst natural disaster on record in Dominica and Puerto Rico).

AI needed to evaluate faster and more accurate climate models

The performance of modern leadership class supercomputers like the CPU-based NERSC Cori system provides scientists an extraordinary tool to model climate change significantly faster and far more accurately than was possible on previous generation supercomputers. For example, Wehner believes that simulated storms can run 300x to 10,000x faster than real time[ii]. This meets the needs of climate scientists who need to run many-century long simulations to evaluate the impact of climate change far into the future.

Wehner pointed out that while humans can (and do) perform well in identifying and tracking extreme weather events in real time, they simply cannot keep up when climate models run two to five orders of magnitude faster. Thus machine learning has to be used to identify and track extreme weather events. Further, these machine learning based results can be used to validate the climate models so we have confidence in the future predictions of these models.

More powerful machines also means that scientists are able to run climate models with higher spatial resolution.

Utilizing higher spatial resolution models, Wehner points is necessary because only high resolution climate models (<25Km) can realistically reproduce extreme storms[iii]. The challenge for climate and computational scientists is that these higher spatial resolution models generate tens to hundreds of terabytes of data, thus increasing the challenges of evaluating the output of these models.

Visually, the benefits of higher spatial resolution models can be easily seen in the comparative images between a 200 km and 25 km spatial resolution simulation shown below.

Figure 2: Comparative results showing the additional detail that is modeled by a 25 Km spatial resolution model as opposed to a 200 Km model. (Image courtesy NERSC)

Valuable insight as well as confirmation of human intuition can be obtained from these long-term high resolution climate simulations. The following figure, for example, represents an assessment of what will happen to the number and intensity of hurricanes as the climate warms. The chart reflects the results of 75 million hours of climate simulation on the Cori supercomputer. The conclusion from this study is that a world that is 1.5oC and 2oC warmer that today will experience more frequent and intense hurricanes. [iv]

Figure 3: Projected increase in category 5 hurricanes and decrease in weaker tropical storms due to climate change. Number of storms is measured on the y-axis (Image courtesy NERSC)

Scaling deep learning from images to climate

Reflecting the thought behind the research, Prabhat, (Director Big Data Center, NERSC) observes that identifying phenomena in climate data is analogous to commercial vision applications as shown in the following slide from his Intel Developer Conference keynote presentation.

Figure 4: Intuition showing that conventional DNNs could potentially recognize atmospheric phenomena (Image courtesy NERSC)

Prabhat points out that initial supervised training results show that this analogy is correct in that machine learning was able to train and recognize each of three desired atmospheric phenomena with high accuracy.

Figure 5: Initial supervised learning results [v] (Courtesy NERSC).
Researchers from MILA, NERSC and Microsoft teamed up to create[vi] a novel semi-supervised convolutional DNN architecture that was able to do the work of all three individual supervised DNNs at the same time. Essentially, this novel neural network finds the bounding box size and location when it classifies the atmospheric phenomena. Further, the neural network also associates a probability with the classification. The high correspondence between prediction and ground truth are shown in Figure 1. Prabhat notes that refining the size of the bounding box is a work in progress.

Figure 6: Novel semi-supervised learning architecture used to classify atmospheric phenomena and demonstrate petascale training performance on the NERSC Intel Xeon Phi computational nodes (Image courtesy NERSC)

Training DNNs at 15 PF/s and with strong scaling

This climate problem was used in a collaborative effort between Intel, NERSC and Stanford to demonstrate the fastest and most scalable deep-learning training implementation in the world according to the authors of the paper Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data. More specifically, the authors report that a configuration of 9600 self-hosted 1.4GHz Intel Xeon Phi Processor 7250 based nodes achieved a peak rate between 11.73 and 15.07 PF/s and an average sustained performance of 11.41 to 13.47 PF/s.

The following strong scaling plots (below) show that the hybrid approach advocated by Kurth, et. al. scales well to run on the thousands of Cori nodes. Ioannis Mitliagkas (former Postdoctoral scholar at Stanford and currently Assistant Professor at the University of Montreal) emphatically states, “People typically report weak scaling, because strong scaling is hard.” He continues, “For machine learning systems, strong scaling (keeping the total amount of work constant) is more representative of actual performance.”[vii]

Figure 7: Strong scaling results for synchronous and hybrid approaches (batch size = 2048 per synchronous group).

Summary

Powerful leadership class supercomputers like the CPU-based Cori supercomputer have made fast, accurate global climate simulations possible. Innovations such as the petascale capable hybrid machine learning technique pioneered by Intel, NERSC and Stanford means those same machines can also train DNNs to evaluate the tens to hundreds of terabytes of data created by these faster and more accurate climate simulations.

About the Author

Rob Farber is a global technology consultant and author with an extensive background in HPC and in developing machine learning technology that he applies at national labs and commercial organizations. Rob can be reached at [email protected].

[i] https://software.intel.com/en-us/events/hpc-devcon/2017/keynote?multiplayer=5646473936001

[ii] ibid

[iii] ibid

[iv] ibid

[v] https://arxiv.org/abs/1605.01156

[vi] https://papers.nips.cc/paper/6932-extremeweather-a-large-scale-climate-dataset-for-semi-supervised-detection-localization-and-understanding-of-extreme-weather-events.pdf

[vii] The authors point out that weak scaling is easy to achieve! However, it is not representative of true performance as it increases the total batch size as the scale increases. They note that most people who claim to be doing large-scale ML/DL report weak scaling. Strong scaling is more representative of true performance and it is hard to achieve. In strong scaling the batch size used per synchronous group is fixed: this means that no wasted computation happens at large scales. See Ameet Talwalkar’s paper and online tool (Paleo) for a model of DL system performance and good demonstrations on how strong scaling suffers for real systems. In terms of the hardware efficiency, hybrid systems can deliver superior strong scaling (this is what the top plots are showing).

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!

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody would like more juice to run compute-intensive HPC simulatio Read more…

By Alex Woodie

Is Data Science the Fourth Pillar of the Scientific Method?

April 18, 2019

Nvidia CEO Jensen Huang revived a decade-old debate last month when he said that modern data science (AI plus HPC) has become the fourth pillar of the scientific method. While some disagree with the notion that statistic Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing the bounds of what's possible in business and science, in w Read more…

By Alex Woodie with Doug Black and Tiffany Trader

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

Bridging HPC and Cloud Native Development with Kubernetes

The HPC community has historically developed its own specialized software stack including schedulers, filesystems, developer tools, container technologies tuned for performance and large-scale on-premises deployments. Read more…

Google Open Sources TensorFlow Version of MorphNet DL Tool

April 18, 2019

Designing optimum deep neural networks remains a non-trivial exercise. “Given the large search space of possible architectures, designing a network from scratch for your specific application can be prohibitively expens Read more…

By John Russell

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing Read more…

By Alex Woodie with Doug Black and Tiffany Trader

Interview with 2019 Person to Watch Michela Taufer

April 18, 2019

Today, as part of our ongoing HPCwire People to Watch focus series, we are highlighting our interview with 2019 Person to Watch Michela Taufer. Michela -- the Read more…

By HPCwire Editorial Team

Intel Gold U-Series SKUs Reveal Single Socket Intentions

April 18, 2019

Intel plans to jump into the single socket market with a portion of its just announced Cascade Lake microprocessor line according to one media report. This isn Read more…

By John Russell

BSC Researchers Shrink Floating Point Formats to Accelerate Deep Neural Network Training

April 15, 2019

Sometimes calculating solutions as precisely as a computer can wastes more CPU resources than is necessary. A case in point is with deep learning. In early stag Read more…

By Ken Strandberg

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

Nvidia Doubles Down on Medical AI

April 9, 2019

Nvidia is collaborating with medical groups to push GPU-powered AI tools into clinical settings, including radiology and drug discovery. The GPU leader said Monday it will collaborate with the American College of Radiology (ACR) to provide clinicians with its Clara AI tool kit. The partnership would allow radiologists to leverage AI techniques for diagnostic imaging using their own clinical data. Read more…

By George Leopold

Digging into MLPerf Benchmark Suite to Inform AI Infrastructure Decisions

April 9, 2019

With machine learning and deep learning storming into the datacenter, the new challenge is optimizing infrastructure choices to support diverse ML and DL workfl Read more…

By John Russell

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

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

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

Intel Launches Cascade Lake Xeons with Up to 56 Cores

April 2, 2019

At Intel's Data-Centric Innovation Day in San Francisco (April 2), the company unveiled its second-generation Xeon Scalable (Cascade Lake) family and debuted it Read more…

By Tiffany Trader

France to Deploy AI-Focused Supercomputer: Jean Zay

January 22, 2019

HPE announced today that it won the contract to build a supercomputer that will drive France’s AI and HPC efforts. The computer will be part of GENCI, the Fre Read more…

By Tiffany Trader

Oil and Gas Supercloud Clears Out Remaining Knights Landing Inventory: All 38,000 Wafers

March 13, 2019

The McCloud HPC service being built by Australia’s DownUnder GeoSolutions (DUG) outside Houston is set to become the largest oil and gas cloud in the world th Read more…

By Tiffany Trader

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

UC Berkeley Paper Heralds Rise of Serverless Computing in the Cloud – Do You Agree?

February 13, 2019

Almost exactly ten years to the day from publishing of their widely-read, seminal paper on cloud computing, UC Berkeley researchers have issued another ambitious examination of cloud computing - Cloud Programming Simplified: A Berkeley View on Serverless Computing. The new work heralds the rise of ‘serverless computing’ as the next dominant phase of cloud computing. Read more…

By John Russell

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