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!

Machine Learning at HPC User Forum: Drilling into Specific Use Cases

September 22, 2017

The 66th HPC User Forum held September 5-7, in Milwaukee, Wisconsin, at the elegant and historic Pfister Hotel, highlighting the 1893 Victorian décor and art of “The Grand Hotel Of The West,” contrasted nicely with Read more…

By Arno Kolster

Google Cloud Makes Good on Promise to Add Nvidia P100 GPUs

September 21, 2017

Google has taken down the notice on its cloud platform website that says Nvidia Tesla P100s are “coming soon.” That's because the search giant has announced the beta launch of the high-end P100 Nvidia Tesla GPUs on t Read more…

By George Leopold

Cray Wins $48M Supercomputer Contract from KISTI

September 21, 2017

It was a good day for Cray which won a $48 million contract from the Korea Institute of Science and Technology Information (KISTI) for a 128-rack CS500 cluster supercomputer. The new system, equipped with Intel Xeon Scal Read more…

By John Russell

HPE Extreme Performance Solutions

HPE Prepares Customers for Success with the HPC Software Portfolio

High performance computing (HPC) software is key to harnessing the full power of HPC environments. Development and management tools enable IT departments to streamline installation and maintenance of their systems as well as create, optimize, and run their HPC applications. Read more…

Adolfy Hoisie to Lead Brookhaven’s Computing for National Security Effort

September 21, 2017

Brookhaven National Laboratory announced today that Adolfy Hoisie will chair its newly formed Computing for National Security department, which is part of Brookhaven’s new Computational Science Initiative (CSI). Read more…

By John Russell

Machine Learning at HPC User Forum: Drilling into Specific Use Cases

September 22, 2017

The 66th HPC User Forum held September 5-7, in Milwaukee, Wisconsin, at the elegant and historic Pfister Hotel, highlighting the 1893 Victorian décor and art o Read more…

By Arno Kolster

Stanford University and UberCloud Achieve Breakthrough in Living Heart Simulations

September 21, 2017

Cardiac arrhythmia can be an undesirable and potentially lethal side effect of drugs. During this condition, the electrical activity of the heart turns chaotic, Read more…

By Wolfgang Gentzsch, UberCloud, and Francisco Sahli, Stanford University

PNNL’s Center for Advanced Tech Evaluation Seeks Wider HPC Community Ties

September 21, 2017

Two years ago the Department of Energy established the Center for Advanced Technology Evaluation (CENATE) at Pacific Northwest National Laboratory (PNNL). CENAT Read more…

By John Russell

Exascale Computing Project Names Doug Kothe as Director

September 20, 2017

The Department of Energy’s Exascale Computing Project (ECP) has named Doug Kothe as its new director effective October 1. He replaces Paul Messina, who is stepping down after two years to return to Argonne National Laboratory. Kothe is a 32-year veteran of DOE’s National Laboratory System. Read more…

Takeaways from the Milwaukee HPC User Forum

September 19, 2017

Milwaukee’s elegant Pfister Hotel hosted approximately 100 attendees for the 66th HPC User Forum (September 5-7, 2017). In the original home city of Pabst Blu Read more…

By Merle Giles

Kathy Yelick Charts the Promise and Progress of Exascale Science

September 15, 2017

On Friday, Sept. 8, Kathy Yelick of Lawrence Berkeley National Laboratory and the University of California, Berkeley, delivered the keynote address on “Breakthrough Science at the Exascale” at the ACM Europe Conference in Barcelona. In conjunction with her presentation, Yelick agreed to a short Q&A discussion with HPCwire. Read more…

By Tiffany Trader

DARPA Pledges Another $300 Million for Post-Moore’s Readiness

September 14, 2017

The Defense Advanced Research Projects Agency (DARPA) launched a giant funding effort to ensure the United States can sustain the pace of electronic innovation vital to both a flourishing economy and a secure military. Under the banner of the Electronics Resurgence Initiative (ERI), some $500-$800 million will be invested in post-Moore’s Law technologies. Read more…

By Tiffany Trader

IBM Breaks Ground for Complex Quantum Chemistry

September 14, 2017

IBM has reported the use of a novel algorithm to simulate BeH2 (beryllium-hydride) on a quantum computer. This is the largest molecule so far simulated on a quantum computer. The technique, which used six qubits of a seven-qubit system, is an important step forward and may suggest an approach to simulating ever larger molecules. Read more…

By John Russell

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

IBM Clears Path to 5nm with Silicon Nanosheets

June 5, 2017

Two years since announcing the industry’s first 7nm node test chip, IBM and its research alliance partners GlobalFoundries and Samsung have developed a proces Read more…

By Tiffany Trader

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

Leading Solution Providers

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

GlobalFoundries: 7nm Chips Coming in 2018, EUV in 2019

June 13, 2017

GlobalFoundries has formally announced that its 7nm technology is ready for customer engagement with product tape outs expected for the first half of 2018. The Read more…

By Tiffany Trader

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