Climate Modelers Have Insatiable Appetite for HPC

By Steve Conway

March 14, 2011

Since the dawn of high performance computing, climate modeling has been one of its most demanding domains. The hunger for computational capability is unending, as researchers work to incorporate more of nature’s complexity into their models at higher resolutions. HPCwire talked with NOAA/GFDL Deputy Director Brian Gross and Venkatramani Balaji, head of the lab’s Modeling Systems Group.

HPCwire: How important have HPC-based modeling and simulation been in increasing human understanding of climate behavior and climate change?

Brian Gross: The climate system is inherently complex, measured by the number of processes and feedbacks between climate variables. It has interactions at all time and space scales, from minutes to millennia, and from millimeters to planet-scale. The role of HPC in addressing these inherent computational challenges to achieve the tremendous advances in our understanding of the Earth System cannot be overstated.

Venkatramani Balaji: In fact, Nature listed the first ocean-atmosphere coupled model — achieved by Suki Manabe, Kirk Bryan, and their collaborators at NOAA/GFDL in 1969 — as a milestone in scientific computing. That model, run on the HPC of the ’60s, was the first to show that adding CO2 to the atmosphere changes the radiative balance so as to increase surface temperatures. HPC-based modeling is the only science-based method to project future climate change.

HPCwire: In the 1990s, US climate researchers published a paper lamenting the lack of access to the most powerful supercomputers for climate modeling, which at that time were vector systems. Has anything been lost in the transition to non-vector supercomputers?

Gross: It turns out, no. On a scientific level, US labs without vector supercomputers kept pace with European and Japanese labs with vector machines. There is no evidence in hindsight that being denied access to vector machines hurt the US labs, whether measured in terms of scientific breakthroughs, or publications, or metrics of model skill.

Balaji: This is not to say that we went through the transition with no pain! The switch from vector to distributed memory machines was certainly disruptive and required a thorough technology refresh of the models. Labs had to expend a lot of effort recoding and then verifying that the new codes were capable of reproducing proven results.

Gross: We used the occasion also to instill better software engineering practices, and I think most people will agree that we’re the better for it. The models today are more agile and more configurable. We can build more complexity into our models than we were able to in the ’90s because of component-based design. We are now able to include atmospheric chemistry, aerosols, dynamic ecosystems on land and ocean, and we can study the complete Earth system. We couldn’t have done this very easily with models of the 1990 vintage.

HPCwire: What are the biggest challenges facing the climate modeling community today?

Gross: The principal challenges we face in climate modeling today remain the same as they have for decades: our limited understanding of the way the Earth System works, how accurately we can translate what we do know into computational algorithms and numerical models, quantifying uncertainty, and efficiently running our increasingly computationally intensive climate models on the largest HPC systems in the world.

It is worth pointing out that the direction of technology today, using more processors rather than faster processors, greatly favors weak scaling over strong scaling. The consequence is that we can often execute more complex, higher-resolution models at a fixed rate, as measured by, say, model years per day.

Balaji: But it’s much more difficult to execute a given model at a faster rate. This can often impede our scientific progress, given the very long time scales associated with some climate processes, such as the global ocean circulation and long-lived greenhouse gases like carbon dioxide. We’ll return to these challenges in a minute.

HPCwire: In the next few years, what are the goals for increased resolution of coupled earth system models?

Gross: The question of anthropogenic climate change on the scale of the planet is settled from a purely scientific viewpoint. However, understanding the details of climate change on a regional scale is harder. We’re not yet at a point where we can attribute local or regional climate change to human actions with the same confidence.

The goal for the current generation of IPCC-class models is to see if higher resolution yields better skill on regional scales. This is not a given. As processes that are currently unresolved become resolved, their representation in models changes from “parameterized” to “simulated.”

Balaji: There are key processes — for instance mesoscale eddies in the ocean, and deep convection in the atmosphere — that will undergo this transition over the next 5-10 years. Some current problems, such as cloud-climate feedback and ocean mixing, will be solved, but new ones might emerge. But certainly cloud-resolving and ocean-eddy-resolving coupled models promise to yield qualitatively new and exciting science.

HPCwire: What are the biggest barriers to greater scalability? Is it the codes, the models, or the limitations of the known science?

Balaji: All of these are barriers, but this list is incomplete. Why are hardware and system software not on your list? Our main difficulty is the speed of a single operation has not got faster for a while and is likely to become slower on the many-core and GPU cluster type systems. Compilers have not got any better for a long time at interpreting our codes, and are even more immature on the novel architectures.

Gross: The expectation had been that a given model at a given resolution would get faster over time just by advances in technology. We’ve just had a rude awakening.

Balaji: As an aside, I’d focus on time-to-solution rather than scalability per se. We all know tricks that make models run on more processors, yet take longer to reach the same solution. We class our models as 1 year/day models, 10 year/day models, and so on. Each can be used for a different class of scientific problem.

HPCwire: It seems that generational advances in computing power reduce uncertainty by enabling greater resolution, but adding new components to coupled models, such as for the carbon cycle, can offset these gains by increasing the complexity of the models? How do you balance these choices?

Gross: Good question. Our feeling is that the complexity comes first. When we feel we’ve reached a level of understanding of some process — say aerosol-cloud interactions, or dynamic vegetation — they get added to the models, and a new realm of scientific problem opens up. We then look at what hardware we can get with our computing budget, and that tells what resolutions we can use while achieving the target model years/day pace necessary for useful science.

HPCwire: How well do the atmospheric, oceanic and other components of coupled models and ensemble models “talk to” each other? How compatible are the physics and the scales in these models?

Balaji: We typically change one component at a time, so that you can do careful comparisons with previous results and trace differences back to a single component. But resolutions stay close, usually within a factor of two or so.

Not to say that the grids are the same. Atmosphere and ocean modelers have taken different routes to avoiding grid singularities and other numerical issues. Coupling technology is stable and mature. There are good, efficient, scalable, conservative coupling and regridding methods, but there’s always an open question as to whether they’ll keep scaling as we add resolution. Also, we’re not well situated to take advantage of AMR [adaptive mesh refinement], and so on. These methods are not much in use in the climate field today.

HPCwire: The goal is for the “Gaea” Cray XT6 supercomputer at ORNL to grow to a 720-teraflop Cray XE6 system in mid-2011. The plan is for “Gaea” to expand to 1.1 petaflops later on. What will these increases make possible?

Gross: Gaea puts within reach the eddy-resolving ocean models and cloud-resolving models we just spoke about. Separately, we’re already there. We believe we’ll be doing useful science with these models in coupled mode shortly after we get the full petaflop machine. Okay, maybe not cloud-resolving, but tropical storm-resolving.

Balaji: Additionally, we’re exploring predictability issues with our models. How sensitive are predictions to initial conditions? These studies explore probability distributions across ensembles of runs initialized with an advanced coupled data assimilation system. These will also stress the capacity of the machine.

Putting these two together, for predictability changes as a function of resolution, we could use up these cycles many times over. And I haven’t even mentioned the Earth System models, which apply this unique resource to substantially increase complexity,adding in atmospheric chemistry, fully interactive land-based ecosystem dynamics and carbon, nitrogen, and other biogeochemical cycles.

HPCwire: What elements of this supercomputer are especially important for weather and climate modeling?

Gross: We hope we’ve made it clear that we can now envision an unprecedented set of exciting science that was out of our reach before. The Cray SeaStar interconnect allows extraordinary levels of scaling, and we’re looking forward to seeing results on the Gemini upgrade, which should be even better.

HPCwire: How much of NOAA’s focus is on modeling weather and climate phenomena in the US, versus other areas of the world?

Balaji: All of our models are global, and the processes and feedbacks are linked on the planetary scale. It’s generally found that to get the climate right over the US, you do need to worry about clouds off the coast of Peru, or you need to get North Atlantic sea surface temperatures right to simulate drought in the Sahel, to take some prominent examples of global linkages. Some short runs are undertaken with regional models, but the fundamental basis of all research and operations is global models.

Gross: We are now configuring some variable resolution models such as the stretched cubed sphere, where resolution can be focused on the US, for instance.

HPCwire: There’s considerable pressure to reduce federal spending in every area possible. Why should strong funding for weather and climate modeling continue?

Gross: Just check out NOAA’s Next-Generation Strategic Plan. Climate change has already had profound implications for society, and climate model predictions and projections foretell a host of additional significant impacts both nationally and internationally.

We need the best possible science-based information on future climate so that decision-makers can develop and evaluate options that mitigate the human causes of climate change and allow society to adapt to foreseeable climate impacts. This information can only be obtained through state-of-the-science climate models. The cost of the associated HPC is trivial compared to the social gains from mitigation and adaptation.

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!

AI-Focused ‘Genius’ Supercomputer Installed at KU Leuven

April 24, 2018

Hewlett Packard Enterprise has deployed a new approximately half-petaflops supercomputer, named Genius, at Flemish research university KU Leuven. The system is built to run artificial intelligence (AI) workloads and, as Read more…

By Tiffany Trader

New Exascale System for Earth Simulation Introduced

April 23, 2018

After four years of development, the Energy Exascale Earth System Model (E3SM) will be unveiled today and released to the broader scientific community this month. The E3SM project is supported by the Department of Energy Read more…

By Staff

RSC Reports 500Tflops, Hot Water Cooled System Deployed at JINR

April 18, 2018

RSC, developer of supercomputers and advanced HPC systems based in Russia, today reported deployment of “the world's first 100% ‘hot water’ liquid cooled supercomputer” at Joint Institute for Nuclear Research (JI Read more…

By Staff

HPE Extreme Performance Solutions

Hybrid HPC is Speeding Time to Insight and Revolutionizing Medicine

High performance computing (HPC) is a key driver of success in many verticals today, and health and life science industries are extensively leveraging these capabilities. Read more…

New Device Spots Quantum Particle ‘Fingerprint’

April 18, 2018

Majorana particles have been observed by university researchers employing a device consisting of layers of magnetic insulators on a superconducting material. The advance opens the door to controlling the elusive particle Read more…

By George Leopold

AI-Focused ‘Genius’ Supercomputer Installed at KU Leuven

April 24, 2018

Hewlett Packard Enterprise has deployed a new approximately half-petaflops supercomputer, named Genius, at Flemish research university KU Leuven. The system is Read more…

By Tiffany Trader

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’ Read more…

By John Russell

IBM: Software Ecosystem for OpenPOWER is Ready for Prime Time

April 16, 2018

With key pieces of the IBM/OpenPOWER versus Intel/x86 gambit settling into place – e.g., the arrival of Power9 chips and Power9-based systems, hyperscaler sup Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Cloud-Readiness and Looking Beyond Application Scaling

April 11, 2018

There are two aspects to consider when determining if an application is suitable for running in the cloud. The first, which we will discuss here under the title Read more…

By Chris Downing

Transitioning from Big Data to Discovery: Data Management as a Keystone Analytics Strategy

April 9, 2018

The past 10-15 years has seen a stark rise in the density, size, and diversity of scientific data being generated in every scientific discipline in the world. Key among the sciences has been the explosion of laboratory technologies that generate large amounts of data in life-sciences and healthcare research. Large amounts of data are now being stored in very large storage name spaces, with little to no organization and a general unease about how to approach analyzing it. Read more…

By Ari Berman, BioTeam, Inc.

IBM Expands Quantum Computing Network

April 5, 2018

IBM is positioning itself as a first mover in establishing the era of commercial quantum computing. The company believes in order for quantum to work, taming qu Read more…

By Tiffany Trader

FY18 Budget & CORAL-2 – Exascale USA Continues to Move Ahead

April 2, 2018

It was not pretty. However, despite some twists and turns, the federal government’s Fiscal Year 2018 (FY18) budget is complete and ended with some very positi Read more…

By Alex R. Larzelere

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

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

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

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Leading Solution Providers

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

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