Topology, Physics & Machine Learning Take on Climate Research Data Challenges

September 7, 2018

Sept. 7, 2018 — Two PhD students who first came to Lawrence Berkeley National Laboratory (Berkeley Lab) as summer interns in 2016 are spending six months a year at the lab through 2020 developing new data analytics tools that could dramatically impact climate research and other large-scale science data projects.

Grzegorz Muszynski is a PhD student at the University of Liverpool, U.K. studying with Vitaliy Kurlin, an expert in topology and computational geometry. Adam Rupe is pursuing his PhD at the University of California at Davis under the supervision of Jim Crutchfield, an expert in dynamical systems, chaos, information theory and statistical mechanics. Both are also currently working in the National Energy Research Scientific Computing Center’s (NERSC) Data & Analytics Services (DAS) group, and their PhDs are being funded by the Big Data Center (BDC), a collaboration between NERSC, Intel and five Intel Parallel Computing Centers launched in 2017 to enable capability data-intensive applications on NERSC’s supercomputing platforms.

During their first summer at the lab, Muszynski and Rupe so impressed their mentors that they were invited to stay on another six months, said Karthik Kashinath, a computer scientist and engineer in the DAS group who leads multiple BDC climate science projects. Their research also fits nicely with the goals of the BDC, which was just getting off the ground when they first came on board. Muszynski and Rupe are now in the first year of their respective three-year BDC-supported projects, splitting time between their PhD studies and their research at the lab.

A Grand Challenge in Climate Science

From the get-go their projects have been focused on addressing a grand challenge in climate science: finding more effective ways to detect and characterize extreme weather events in the global climate system across multiple geographical regions and developing more efficient methods for analyzing the ever-increasing amount of simulated and observational data. Automated pattern recognition is at the heart of both efforts, yet the two researchers are approaching the problem in distinctly different ways: Muszynski is using various combinations of topology, applied math and machine learning to detect, classify and characterize weather and climate patterns, while Rupe has developed a physics-based mathematical model that enables unsupervised discovery of coherent structures characteristic of the spatiotemporal patterns found in the climate system.

“When you are investigating extreme weather and climate events and how they are changing in a warming world, one of the challenges is being able to detect, identify and characterize these events in large data sets,” Kashinath said. “Historically we have not been very good at pulling out these events from very large data sets. There isn’t a systematic way to do it, and there is no consensus on what the right approaches are.”

This is why the DAS group and the BDC are so enthusiastic about the work Muszynski and Rupe are doing. In their time so far at the lab, both students have been extremely productive in terms of research progress, publications, presentations and community outreach, Kashinath noted. Together, their work has resulted in six articles, eight poster presentations and nine conference talks over the last two years, which has fueled interest within the climate science community—and for good reason, he emphasized. In particular, Muszynski’s work was noted as novel and powerful at the Atmospheric Rivers Tracking Method Intercomparison Project (ARTMIP), an international community of researchers investigating Atmospheric Rivers.

“The volume at which climate data is being produced today is just insane,” he said. “It’s been going up at an exponential pace ever since climate models came out, and these models have only gotten more complex and more sophisticated with much higher resolution in space and time. So there is a strong need to automate the process of discovering structures in data.”

There is also a desire to find climate data analysis methods that are reliable across different models, climates and variables. “We need automatic techniques that can mine through large amounts of data and that works in a unified manner so it can be deployed across different data sets from different research groups,” Kashinath said.

Using Geometry to Reveal Topology

Muszynski and Rupe are both making steady progress toward meeting these challenges. Over his two years at the lab so far, Muszynski has developed a framework of tools from applied topology and machine learning that are complementary to existing tools and methods used by climate scientists and can be mixed and matched depending on the problem to be solved. As part of this work, Kashinath noted, Muszynski parallelized his codebase on several nodes on NERSC’s Cori supercomputer to accelerate the machine learning training process, which often requires hundreds to thousands of examples to train a model that can classify events accurately.

His topological methods also benefited from the guidance of Dmitriy Morozov, a computational topologist and geometer at CRD. In a paper submitted earlier this year to the journal Geoscientific Model Development, Muszynski and his co-authors used topological data analysis and machine learning to recognize atmospheric rivers in climate data, demonstrating that this automated method is “reliable, robust and performs well” when tested on a range of spatial and temporal resolutions of CAM5.1 climate model output. They also tested the method on MERRA-2, a climate reanalysis product that incorporates observational data that makes pattern detection even more difficult. In addition, they noted, the method is “threshold-free”, a key advantage over existing data analysis methods used in climate research.

“Most existing methods use empirical approaches where they set arbitrary thresholds on different physical variables, such as temperature and wind speed,” Kashinath explained. “But these thresholds are highly dependent on the climate we are living in right now and cannot be applied to different climate scenarios. Furthermore, these thresholds often depend on the type of dataset and spatial resolution. With Grzegorz’s method, because it is looking for underlying shapes (geometry and topology) of these events in the data, they are inherently free of the threshold problem and can be seamlessly applied across different datasets and climate scenarios. We can also study how these shapes are changing over time that will be very useful to understand how these events are changing with global warming.”

While topology has been applied to simpler, smaller scientific problems, this is one of the first attempts to apply topological data analysis to large climate data sets. “We are using topological data analysis to reveal topological properties of structures in the data and machine learning to classify these different structures in large climate datasets,” Muszynski said.

The results so far have been impressive, with notable reductions in computational costs and data extraction times. “I only need a few minutes to extract topological features and classify events using a machine learningclassifier, compared to days or weeks needed to train a deep learning model for the same task,” he said. “This method is orders of magnitude faster than traditional methods or deep learning. If you were using vanilla deep learning on this problem, it would take 100 times the computational time.”

Another key advantage of Muszynski’s framework is that “it doesn’t really care where you are on the globe,” Kashinath said. “You can apply it to atmospheric rivers in North America, South America, Europe – it is universal and can be applied across different domains, models and resolutions. And this idea of going after the underlying shapes of events in large datasets with a method that could be used for various classes of climate and weather phenomena and being able to work across multiple datasets—that becomes a very powerful tool.”

To read the full article, click here.

Source: Kathy Kincade, NERSC

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!

Migration Tools Needed to Shift ML to Production

September 20, 2018

The confluence of accelerators like cloud GPUs along with the ability to handle data-rich HPC workloads will help push more machine learning projects into production, concludes a new study that also stresses the importan Read more…

By George Leopold

Kyoto University ACCMS Implements Fine-grained Power Management

September 19, 2018

Data center power management is a ubiquitous challenge and in few places is it more so than at Kyoto University Academic Center for Computing and Media Studies (ACCMS)) where power consumption limits were imposed followi Read more…

By Staff

What’s New in HPC Research: September (Part 1)

September 18, 2018

In this new bimonthly feature, HPCwire will highlight newly published research in the high-performance computing community and related domains. From exascale to quantum computing, the details are here. Check back every Read more…

By Oliver Peckham

HPE Extreme Performance Solutions

Introducing the First Integrated System Management Software for HPC Clusters from HPE

How do you manage your complex, growing cluster environments? Answer that big challenge with the new HPC cluster management solution: HPE Performance Cluster Manager. Read more…

IBM Accelerated Insights

A Crystal Ball for HPC

People are notoriously bad at predicting the future.  This very much includes experts. In the Forbes article “Why Most Predictions Are So Bad” Philip Tetlock discusses the largest and best-known test of the accuracy of expert predictions which show that any experts would do better if they make random guesses. Read more…

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 development. Among other things it would establish a National Quantu Read more…

By John Russell

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

Nvidia Accelerates AI Inference in the Datacenter with T4 GPU

September 14, 2018

Nvidia is upping its game for AI inference in the datacenter with a new platform consisting of an inference accelerator chip--the new Turing-based Tesla T4 GPU- Read more…

By George Leopold

DeepSense Combines HPC and AI to Bolster Canada’s Ocean Economy

September 13, 2018

We often hear scientists say that we know less than 10 percent of the life of the oceans. This week, IBM and a group of Canadian industry and government partner Read more…

By Tiffany Trader

Rigetti (and Others) Pursuit of Quantum Advantage

September 11, 2018

Remember ‘quantum supremacy’, the much-touted but little-loved idea that the age of quantum computing would be signaled when quantum computers could tackle Read more…

By John Russell

How FPGAs Accelerate Financial Services Workloads

September 11, 2018

While FSI companies are unlikely, for competitive reasons, to disclose their FPGA strategies, James Reinders offers insights into the case for FPGAs as accelerators for FSI by discussing performance, power, size, latency, jitter and inline processing. Read more…

By James Reinders

Update from Gregory Kurtzer on Singularity’s Push into FS and the Enterprise

September 11, 2018

Container technology is hardly new but it has undergone rapid evolution in the HPC space in recent years to accommodate traditional science workloads and HPC systems requirements. While Docker containers continue to dominate in the enterprise, other variants are becoming important and one alternative with distinctly HPC roots – Singularity – is making an enterprise push targeting advanced scale workload inclusive of HPC. Read more…

By John Russell

At HPC on Wall Street: AI-as-a-Service Accelerates AI Journeys

September 10, 2018

AIaaS – artificial intelligence-as-a-service – is the technology discipline that eases enterprise entry into the mysteries of the AI journey while lowering Read more…

By Doug Black

No Go for GloFo at 7nm; and the Fujitsu A64FX post-K CPU

September 5, 2018

It’s been a news worthy couple of weeks in the semiconductor and HPC industry. There were several HPC relevant disclosures at Hot Chips 2018 to whet appetites Read more…

By Dairsie Latimer

TACC Wins Next NSF-funded Major Supercomputer

July 30, 2018

The Texas Advanced Computing Center (TACC) has won the next NSF-funded big supercomputer beating out rivals including the National Center for Supercomputing Ap Read more…

By John Russell

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

Requiem for a Phi: Knights Landing Discontinued

July 25, 2018

On Monday, Intel made public its end of life strategy for the Knights Landing "KNL" Phi product set. The announcement makes official what has already been wide Read more…

By Tiffany Trader

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

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

By John Russell

AMD’s EPYC Road to Redemption in Six Slides

June 21, 2018

A year ago AMD returned to the server market with its EPYC processor line. The earth didn’t tremble but folks took notice. People remember the Opteron fondly 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

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17


AMD @ SC17


ASRock Rack @ SC17

ASRock Rack



DDN Storage @ SC17

DDN Storage

Huawei @ SC17


IBM @ SC17


IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17


Lenovo @ SC17


Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17


Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17


Tyan @ SC17


Univa @ SC17


Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

D-Wave Breaks New Ground in Quantum Simulation

July 16, 2018

Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that Read more…

By John Russell

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. 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

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

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

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

GPUs Power Five of World’s Top Seven Supercomputers

June 25, 2018

The top 10 echelon of the newly minted Top500 list boasts three powerful new systems with one common engine: the Nvidia Volta V100 general-purpose graphics proc Read more…

By Tiffany Trader

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