Deep Learning for Science: A Q&A with NERSC’s Prabhat

By Kathy Kincade

November 7, 2017

Deep learning is enjoying unprecedented success in a variety of commercial applications, but it is also beginning to find its footing in science. Just a decade ago, few practitioners could have predicted that deep learning-powered systems would surpass human-level performance in computer vision and speech recognition tasks.

These tools are now poised to help scientists contend with some of the most challenging data analytics problems in a number of domains. For example, extreme weather events pose great potential risk on ecosystem, infrastructure and human health. Analyzing extreme weather data from satellites and weather stations and characterizing changes in extremes in simulations is an important task. Similarly, upcoming astronomical sky surveys will obtain measurements of tens of billions of galaxies, enabling precision measurements of the parameters that describe the nature of dark energy. But in each case, analyzing the mountains of resulting data poses a daunting challenge.

Prabhat, NERSC

A growing number of scientists are already employing HPC systems for data analytics, and many are now beginning to apply deep learning and other types of machine learning to their large datasets. Toward this end, in 2016 the U.S. Department of Energy’s National Energy Research Scientific Computing Center (NERSC) expanded its support for deep learning and began forming hands-on collaborations with scientists and industry. NERSC users from science domains such as geosciences, high energy physics, earth systems modeling, fusion and astrophysics are now working with NERSC staff, software tools and services to explore how deep learning can improve their ability to solve challenging science problems.

In this Q&A with Prabhat, who leads the Data and Analytics Services Group at NERSC, he talks about the history of deep learning and machine learning and the unique challenges of applying these data analytics tools to science. Prabhat is also an author on two related technical papers being presented at SC17, “Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data” and “Galactos: Computing the 3-pt Anisotropic Correlation for 2 Billion Galaxies,” and is conducting two deep learning roundtables in the DOE Booth (#613) at SC17. He is also giving a plenary talk on deep learning for science on Sunday, November 12 at the Intel HPC Developer Conference held in conjunction with SC17.

How do you define deep learning, and how does it differ from machine learning?

At the Department of Energy, we tackle inference problems across numerous domains. Given a noisy observation, you would like to infer properties of the object of interest. The discipline of statistics is ideally suited to solve inference problems. The discipline of Machine Learning lies at the intersection of statistics and computer science, wherein core statistical methods were employed by computer scientists to solve applied problems in computer vision and speech recognition. Machine learning has been around for more than 40 years, and there have been a number of different techniques that have fallen in and out of favor: linear regression, k-means, support vector machines and random forests. Neural networks have always been part of machine learning – they were developed at MIT starting in the 1960s – there was the major development of the back-propagation algorithm in the mid-1980s, but they never really picked up until 2012. That is when the new flavor of neural networks – that is, deep learning – really gained prominence and finally started working. So the way I think of deep learning is as a subset of machine learning, which in turn is closely related to the field of statistics, and all of them have to do with solving inference problems of one kind or another.

What technological changes occurred that enabled deep learning to finally start working?

Three important trends have happened over the last 20 years or so. First, thanks to the internet, “big Data,” or large archives of labeled and unlabeled datasets, has become readily accessible. Second, thanks to Moore’s Law, computers have become extremely powerful. A laptop featuring a GPU and a CPU is more capable than supercomputers from previous decades. These two trends were prerequisites for enabling the third wave of modern neural nets, deep learning, to take off. The basic machinery and algorithms have been in existence for three decades, but it is only the unique confluence of large datasets and massive computational horsepower that enabled us to explore the expressive capabilities of Deep Networks.

What are some of the leading types of deep learning methods used today for scientific applications?

As we’ve gone about systematically exploring the application of deep learning to scientific problems over the last four years, what we have found is that there are two dominant architectures that are relevant to science problems. The first is called the convolutional network. This architecture is widely applicable because a lot of the data that we obtain from experimental and observational sources (telescopes and microscopes) and simulations – tend to be in the form of a grid or an image. Similar to commodity cameras, we have 2D images, but we also typically deal with 3D, 4D and multi-channel images. Supervised pattern classification is a common task shared across commercial and scientific use cases; applications include face detection, face recognition, object detection and object classification.

The second approach is more sophisticated and has to do with the recurrent neural network: the long short-term memory (LSTM) architecture. In commercial applications, LSTMs are used for translating speech by learning the sequence-to-sequence mapping between one language and another. In our science cases, we also have sequence-to-sequence mapping problems, such as gene sequencing, for example, or in earth systems modeling, where you are tracking storms in space and time. There are also problems in neuroscience that take recordings from the brain and use LSTM to predict speech. So broadly those two flavors of architectures – convolutional networks and LSTMs – are the dominant deep learning methodologies for science today.

In recent years, we have also explored auto-encoder architectures, which can be used for unsupervised clustering of datasets. We have had some success in applying such methods for analysis of galaxy images in astronomy, and Data Bay sensor data for neutrino discovery. The latest trend in deep learning is the generative adversarial network (GAN). This architecture can be used for creating synthetic data. You can feed in examples from a certain domain, say cosmology images or Large Hadron Collider (LHC) images, and the network will essentially learn a process that can explain these images. Then you can ask that same network to produce more synthetic data that is consistent with other images it has seen. We have empirical evidence that you can use GANs to produce synthetic cosmology or synthetic LHC data without resorting to expensive computational simulations.

What is driving NERSC’s growing deep learning efforts, and how did you come to lead these efforts?

I have a long-standing interest in image processing and computer vision. During my undergrad at IIT Delhi, and grad studies at Brown, I was intrigued by object recognition problems, which seemed to be fairly hard to solve. There was incremental progress in the field through the 1990s and 2000s, and then suddenly in 2012 and 2013 you see this breakthrough performance in solving real problems on real datasets. At that point, the MANTISSA collaboration – a research project originally begun when I was part of Berkeley Lab’s Computational Research Division – was exploring similar pattern detection problems, and it was natural for us to explore whether deep learning could be applied to science problems. We spent the next three to four years exploring applications in earth systems modeling, neuroscience, astronomy and high energy physics.

When a new method/technology comes along, one has to make a judgment call on how long you want to wait before investing time and energy in exploring the possibilities. I think the DAS group at NERSC was one of the early adopters. We recognized the importance of this technique and demonstrated that it could work for science. In the experimental and observational data community, there are a lot of examples of domain scientists who have been struggling with pattern recognition problems for a long time. And now the broader science community is waking up to the possibilities of machine learning to help them solve these problems.

What is NERSC’s current strategy for bringing deep learning capabilities to its users?

Since NERSC is a DOE Office of Science national user facility, we listen to our users, track their emerging requirements and respond to their needs. Our users are telling us that they would like to explore machine learning/deep learning and see what it can do for them. We currently have about 70 users who are actively using deep learning software at NERSC, and we want to make sure that our software, hardware, policies and documentation are all up to speed. Over the past two years, we have worked with the vendor community and identified a few popular deep learning frameworks (TensorFlow, Caffe, Theano and Torch) and have deployed them on Cori. In addition to making the software available, we have documentation and case studies in place. We also have in-depth collaborations in about a dozen areas where NERSC staff, mostly from the DAS group, have worked with scientists to help them explore the application of deep learning. And we are forming strategic relationships with commercial vendors and other research partners in the community to explore the frontier of deep learning for science.

Do certain areas of scientific research lend themselves more than others to applying deep learning?

Right now our success stories span research sponsored by several DOE Office of Science program offices, including BER, HEP and NP. In earth systems modeling, we have shown that convolutional architectures can extract extreme weather patterns in large simulations datasets. In cosmology, we have shown that CNNs can predict cosmological constants, and GANs can be potentially used to supplement existing cosmology simulations.  In astronomy, the Celeste project has effectively used auto-encoders for modeling galaxy shapes. In high energy physics, we are using convolutional architectures for discriminating between different models of particle physics, exploring LSTM architectures for particle tracking. We’ve also shown that deep learning can be used for clustering and classifying various event types at the Daya Bay experiment.

So the big takeaway here is that for the tasks involving pattern classification, regression and creating fast simulators, deep learning seems to do a good job – IF you can find training data. That’s the big catch – if you have labeled data, you can employ deep learning. But it can be a challenge to find training data in some domain sciences.

Looking ahead, what are some of the challenges in developing deep learning tools for science and applying them to research projects at NERSC and other scientific supercomputing facilities?

We can see a range of short-term and long-term challenges in deep learning for science. The short-term challenges are mostly pragmatic issues pertaining to development, enhancement and deployment of tools. These include handling complex data; scientific data tends to be very diverse (compared to images and speech), we are working with 2D, 3D, even 4D data and the datasets can be sparse or dense and defined over a regular, or irregular grid. Deep learning frameworks will need to account for this diversity going forward. Performance and scaling are also barriers. Our current networks can take several days to converge on O(10) GB datasets, but several scientific domains would like to apply deep learning to 10TB-100TB datasets. Thankfully, this problem is right up our alley at HPC centers.

Another important challenge faced by domain scientists is hyper-parameter tuning: Which network architecture do you start with? How do you choose an optimization algorithm? How do you get the network to converge? Unfortunately, only a few deep learning experts know how to address this problem; we need automated strategies/tools. Finally, once scientific communities realize that deep learning can work for them, and access to labeled datasets is the key barrier to entry, they will need to self-organize and conduct labeling campaigns.

The longer-term challenges for deep learning in science are harder, by definition, and include a lack of theory, interpretability, uncertainty quantification and the need for a formal protocol. I believe it’s very early days in the application of deep learning to scientific problems. There’s a lot of low-hanging fruit in publishing easy papers that demonstrate state-of-the-art accuracy for classification, regression and clustering problems. But in order to ensure that the domain science community truly embraces the power of deep learning methods, we have to keep the longer term, harder challenges in mind.

About the Author

Kathy Kincade is a science & technology writer and editor with the Berkeley Lab Computing Sciences Communications Group.

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!

Arm Targets HPC with New Neoverse Platforms

September 22, 2020

UK-based semiconductor design company Arm today teased details of its Neoverse roadmap, introducing V1 (codenamed Zeus) and N2 (codenamed Perseus), Arm’s second generation N-series platform. The chip IP vendor said the Read more…

By Tiffany Trader

Microsoft’s Azure Quantum Platform Now Offers Toshiba’s ‘Simulated Bifurcation Machine’

September 22, 2020

While pure-play quantum computing (QC) gets most of the QC-related attention, there’s also been steady progress adapting quantum methods for select use on classical computers. Today, Microsoft announced that Toshiba’ Read more…

By John Russell

Oracle Cloud Deepens HPC Embrace with Launch of A100 Instances, Plans for Arm, More 

September 22, 2020

Oracle Cloud Infrastructure (OCI) continued its steady ramp-up of HPC capabilities today with a flurry of announcements. Topping the list is general availability of instances with Nvidia’s newest GPU, the A100. OCI als Read more…

By John Russell

IBM, CQC Enable Cloud-based Quantum Random Number Generation

September 21, 2020

IBM and Cambridge Quantum Computing (CQC) have partnered to achieve progress on one of the major business aspirations for quantum computing – the goal of generating verified, truly random numbers that can be used for a Read more…

By Todd R. Weiss

European Commission Declares €8 Billion Investment in Supercomputing

September 18, 2020

Just under two years ago, the European Commission formalized the EuroHPC Joint Undertaking (JU): a concerted HPC effort (comprising 32 participating states at current count) across the European Union and supplanting HPC Read more…

By Oliver Peckham

AWS Solution Channel

Next-generation aerospace modeling and simulation: benchmarking Amazon Web Services High Performance Computing services

The aerospace industry has been using Computational Fluid Dynamics (CFD) for decades to create and optimize designs digitally, from the largest passenger planes and fighter jets to gliders and drones. Read more…

Intel® HPC + AI Pavilion

Berlin Institute of Health: Putting HPC to Work for the World

Researchers from the Center for Digital Health at the Berlin Institute of Health (BIH) are using science to understand the pathophysiology of COVID-19, which can help to inform the development of targeted treatments. Read more…

Google Hires Longtime Intel Exec Bill Magro to Lead HPC Strategy

September 18, 2020

In a sign of the times, another prominent HPCer has made a move to a hyperscaler. Longtime Intel executive Bill Magro joined Google as chief technologist for high-performance computing, a newly created position that is a Read more…

By Tiffany Trader

Arm Targets HPC with New Neoverse Platforms

September 22, 2020

UK-based semiconductor design company Arm today teased details of its Neoverse roadmap, introducing V1 (codenamed Zeus) and N2 (codenamed Perseus), Arm’s seco Read more…

By Tiffany Trader

Oracle Cloud Deepens HPC Embrace with Launch of A100 Instances, Plans for Arm, More 

September 22, 2020

Oracle Cloud Infrastructure (OCI) continued its steady ramp-up of HPC capabilities today with a flurry of announcements. Topping the list is general availabilit Read more…

By John Russell

European Commission Declares €8 Billion Investment in Supercomputing

September 18, 2020

Just under two years ago, the European Commission formalized the EuroHPC Joint Undertaking (JU): a concerted HPC effort (comprising 32 participating states at c Read more…

By Oliver Peckham

Google Hires Longtime Intel Exec Bill Magro to Lead HPC Strategy

September 18, 2020

In a sign of the times, another prominent HPCer has made a move to a hyperscaler. Longtime Intel executive Bill Magro joined Google as chief technologist for hi Read more…

By Tiffany Trader

Future of Fintech on Display at HPC + AI Wall Street

September 17, 2020

Those who tuned in for Tuesday's HPC + AI Wall Street event got a peak at the future of fintech and lively discussion of topics like blockchain, AI for risk man Read more…

By Alex Woodie, Tiffany Trader and Todd R. Weiss

IBM’s Quantum Race to One Million Qubits

September 15, 2020

IBM today outlined its ambitious quantum computing technology roadmap at its virtual Quantum Summit. The eye-popping million qubit number is still far out, agrees IBM, but perhaps not that far out. Just as eye-popping is IBM’s nearer-term plan for a 1,000-plus qubit system named Condor... Read more…

By John Russell

Nvidia Commits to Buy Arm for $40B

September 14, 2020

Nvidia is acquiring semiconductor design company Arm Ltd. for $40 billion from SoftBank in a blockbuster deal that catapults the GPU chipmaker to a dominant position in the datacenter while helping troubled SoftBank reverse its financial woes. The deal, which has been rumored for... Read more…

By Todd R. Weiss and George Leopold

AMD’s Massive COVID-19 HPC Fund Adds 18 Institutions, 5 Petaflops of Power

September 14, 2020

Almost exactly five months ago, AMD announced its COVID-19 HPC Fund, an ongoing flow of resources and equipment to research institutions studying COVID-19 that began with an initial donation of $15 million. In June, AMD announced major equipment donations to several major institutions. Now, AMD is making its third major COVID-19 HPC Fund... Read more…

By Oliver Peckham

Supercomputer-Powered Research Uncovers Signs of ‘Bradykinin Storm’ That May Explain COVID-19 Symptoms

July 28, 2020

Doctors and medical researchers have struggled to pinpoint – let alone explain – the deluge of symptoms induced by COVID-19 infections in patients, and what Read more…

By Oliver Peckham

Nvidia Said to Be Close on Arm Deal

August 3, 2020

GPU leader Nvidia Corp. is in talks to buy U.K. chip designer Arm from parent company Softbank, according to several reports over the weekend. If consummated Read more…

By George Leopold

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

By Doug Black

Intel’s 7nm Slip Raises Questions About Ponte Vecchio GPU, Aurora Supercomputer

July 30, 2020

During its second-quarter earnings call, Intel announced a one-year delay of its 7nm process technology, which it says it will create an approximate six-month shift for its CPU product timing relative to prior expectations. The primary issue is a defect mode in the 7nm process that resulted in yield degradation... Read more…

By Tiffany Trader

HPE Keeps Cray Brand Promise, Reveals HPE Cray Supercomputing Line

August 4, 2020

The HPC community, ever-affectionate toward Cray and its eponymous founder, can breathe a (virtual) sigh of relief. The Cray brand will live on, encompassing th Read more…

By Tiffany Trader

Google Hires Longtime Intel Exec Bill Magro to Lead HPC Strategy

September 18, 2020

In a sign of the times, another prominent HPCer has made a move to a hyperscaler. Longtime Intel executive Bill Magro joined Google as chief technologist for hi Read more…

By Tiffany Trader

Neocortex Will Be First-of-Its-Kind 800,000-Core AI Supercomputer

June 9, 2020

Pittsburgh Supercomputing Center (PSC - a joint research organization of Carnegie Mellon University and the University of Pittsburgh) has won a $5 million award Read more…

By Tiffany Trader

Supercomputer Modeling Tests How COVID-19 Spreads in Grocery Stores

April 8, 2020

In the COVID-19 era, many people are treating simple activities like getting gas or groceries with caution as they try to heed social distancing mandates and protect their own health. Still, significant uncertainty surrounds the relative risk of different activities, and conflicting information is prevalent. A team of Finnish researchers set out to address some of these uncertainties by... Read more…

By Oliver Peckham

Leading Solution Providers

Contributors

Australian Researchers Break All-Time Internet Speed Record

May 26, 2020

If you’ve been stuck at home for the last few months, you’ve probably become more attuned to the quality (or lack thereof) of your internet connection. Even Read more…

By Oliver Peckham

Oracle Cloud Infrastructure Powers Fugaku’s Storage, Scores IO500 Win

August 28, 2020

In June, RIKEN shook the supercomputing world with its Arm-based, Fujitsu-built juggernaut: Fugaku. The system, which weighs in at 415.5 Linpack petaflops, topp Read more…

By Oliver Peckham

European Commission Declares €8 Billion Investment in Supercomputing

September 18, 2020

Just under two years ago, the European Commission formalized the EuroHPC Joint Undertaking (JU): a concerted HPC effort (comprising 32 participating states at c Read more…

By Oliver Peckham

Google Cloud Debuts 16-GPU Ampere A100 Instances

July 7, 2020

On the heels of the Nvidia’s Ampere A100 GPU launch in May, Google Cloud is announcing alpha availability of the A100 “Accelerator Optimized” VM A2 instance family on Google Compute Engine. The instances are powered by the HGX A100 16-GPU platform, which combines two HGX A100 8-GPU baseboards using... Read more…

By Tiffany Trader

DOD Orders Two AI-Focused Supercomputers from Liqid

August 24, 2020

The U.S. Department of Defense is making a big investment in data analytics and AI computing with the procurement of two HPC systems that will provide the High Read more…

By Tiffany Trader

Microsoft Azure Adds A100 GPU Instances for ‘Supercomputer-Class AI’ in the Cloud

August 19, 2020

Microsoft Azure continues to infuse its cloud platform with HPC- and AI-directed technologies. Today the cloud services purveyor announced a new virtual machine Read more…

By Tiffany Trader

Japan’s Fugaku Tops Global Supercomputing Rankings

June 22, 2020

A new Top500 champ was unveiled today. Supercomputer Fugaku, the pride of Japan and the namesake of Mount Fuji, vaulted to the top of the 55th edition of the To Read more…

By Tiffany Trader

Joliot-Curie Supercomputer Used to Build First Full, High-Fidelity Aircraft Engine Simulation

July 14, 2020

When industrial designers plan the design of a new element of a vehicle’s propulsion or exterior, they typically use fluid dynamics to optimize airflow and in Read more…

By Oliver Peckham

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