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  • richardmitnick 1:27 pm on October 24, 2018 Permalink | Reply
    Tags: Biermann battery effect, , , ORNL Cray XK7 Titan supercomputer, ,   

    From COSMOS Magazine: “Supercomputer finds clues to violent magnetic events” 

    Cosmos Magazine bloc

    From COSMOS Magazine

    24 October 2018
    Phil Dooley

    1
    An aurora over Iceland, the product of sudden magnetic reconnection. Credit Natthawat/Getty Images

    Researchers are a step closer to understanding the violent magnetic events that cause the storms on the sun’s surface and fling clouds of hot gas out into space, thanks to colossal computer simulations at Princeton University in the US.

    The disruptions in the magnetic field, known as magnetic reconnections, are common in the universe – the same process causes the aurora in high latitude skies – but existing models are unable to explain how they happen so quickly.

    A team led by Jackson Matteucci decided to investigate by building a full three-dimensional simulation of the ejected hot gas, something that required enormous computing power. The results are published in the journal Physical Review Letters.

    The researchers modelled more than 200 million particles using Titan, the biggest supercomputer [no longer true, the writer should have known that] in the US.

    ORNL Cray Titan XK7 Supercomputer, once the fastest in the world.

    They discovered that a three-dimensional interaction called the Biermann battery effect was at the heart of the sudden reconnection process.

    Discovered in the fifties by German astrophysicist Ludwig Biermann, the Biermann battery effect shows how magnetic fields can be generated in charged gases, known as plasma.

    In such plasmas, if a region develops in which there is a temperature gradient at right angles to a density gradient, a magnetic field is created that encircles it.

    Astrophysicists propose that this effect might take place in interstellar plasma clouds, such as nebulae, and generate the cosmic magnetic fields that we see throughout the universe.

    In contrast with the huge scale of cosmic plasma clouds, magnetic reconnection happens at a scale of microns when two magnetic fields collide, says Matteucci.

    He likens the process to collisions between two sizable handfuls of rubber bands. In stable circumstances the magnetic field lines are loops, like the bands. But sometimes turbulence in the plasma pushes these band analogues together so forcefully that they sever and reconnect to different ones, thus forming loops at different orientations.

    Some of the new loops are stretched taut and snap back, providing the energy that ejects material so violently, and causes magnetic storms or glowing auroras.

    The Princeton simulation showed that as the fields collide there is a sudden spike in the temperature in a very localised region, which sets off the Biermann battery effect, suddenly creating a new magnetic field in the midst of the collision. It’s this newly-appearing field that severs the lines and allows them to reconfigure.

    Although Matteucci’s simulations are for tiny plasma clouds generated by lasers hitting foil, he says they could help us understand large-scale processes in the atmosphere.

    “If you do a back of the envelope calculation, you find it could play an important role in reconnection in the magnetosphere, where the solar wind collides with the Earth’s magnetic field,” he says.

    See the full article here .


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  • richardmitnick 12:11 pm on February 9, 2018 Permalink | Reply
    Tags: , GM, ORNL Cray XK7 Titan supercomputer, ,   

    From OLCF: “GM Revs up Diesel Combustion Modeling on Titan Supercomputer” 

    i1

    Oak Ridge National Laboratory

    OLCF

    1
    In a model of a 1.6 liter engine cylinder, liquid fuel (shown in red and orange) is converted to fuel vapor under high temperatures during ignition. Image courtesy of Ronald Grover.

    Running more detailed chemistry models on GPUs, researchers improve predictions for nitrogen oxides.

    Most car owners in the United States do not think twice about passing over the diesel pump at the gas station. Instead, diesel fuel mostly powers our shipping trucks, boats, buses, and generators—and that is because diesel engines are about 10 percent more fuel-efficient than gasoline, saving companies money transporting large deliveries.

    The downside to diesel engines is that they produce more emissions, like soot and nitrogen oxides, than gasoline engines because of how they combust fuel and air. A gasoline engine uses a spark plug to ignite a fuel-air mixture. A diesel engine compresses air until it is hot enough to ignite diesel fuel sprayed into the cylinder, using more air than necessary to burn all the fuel in a process called lean mixing-controlled combustion.

    “We can generally clean up emissions for a gasoline engine with a three-way catalyst,” said Ronald Grover, staff researcher at General Motors (GM) Research and Development. “The problem with diesel is that when you operate lean, you can’t use the conventional three-way catalysts to clean up all the emissions suitably, so you have to add a lot of complexity to the after-treatment system.”

    That complexity makes diesel engines heavier and more expensive upfront.

    Grover and GM colleagues Jian Gao, Venkatesh Gopalakrishnan, and Ramachandra Diwakar are using the Titan supercomputer at the Oak Ridge Leadership Computing Facility (OLCF), a US Department of Energy (DOE) Office of Science User Facility at DOE’s Oak Ridge National Laboratory (ORNL), to improve combustion models for diesel passenger car engines with an ultimate goal of accelerating innovative engine designs while meeting strict emissions standards.

    A multinational corporation that delivered 10 million vehicles to market last year, GM runs its research side of the house, Global R&D Laboratories, to develop new technologies for engines and powertrains.

    “We work from a clean sheet of paper, asking ‘What if?’” Grover said. From there, ideas move up to advanced engineering, then to product organization where technology is vetted before it goes into the production pipeline.

    For every engine design, GM must balance cost and performance for customers while working within the constraints of emissions regulations. The company also strives to develop exciting new ideas.

    “The customer is our compass. We’re always trying to design and improve the engine,” Grover said. “We see constraint, and we’re trying to push that boundary.”

    But testing innovative engine designs can run up a huge bill.

    “One option is to try some designs, make some hardware, go test it, make some more hardware, go test it, and you continue to do this iterative process until you eventually reach the design that you like,” he said. “But obviously, every design iteration costs money because you’re cutting new hardware.”

    Meanwhile, competitors might put their own new designs on the market. To reduce R&D costs, automakers use virtual engine models to computationally simulate and calibrate, or adjust, new designs so that only the best designs are built as prototypes for testing in the real world.

    Central to engine design is the combustion process, but studying the intricacies of combustion in a laboratory is difficult and significant computational resources are required to simulate it in a virtual environment.

    Combustion is critical to drivability and ensuring seamless operation on the road, but combustion also affects emissions production because emissions are chemical byproducts of combustion’s main ingredients: fuel, air, and heat.

    “There are hundreds of thousands of chemical species to be measured that you have to track and tens of thousands of reactions that you need to simulate,” Grover said. “We have to simplify the chemistry to the point that we can handle it for computational modeling, and to simplify it, sometimes you have to make assumptions. So sometimes we find the model works well in some areas and doesn’t work well in others.”

    The combustion process in a car engine—from burning the first drop of fuel to emitting the last discharge of exhaust—can create many thousands of chemical species, including regulated emissions. However, sensors used in experimental testing allow researchers to track only a limited number of species over the combustion process.

    “You’re missing a lot of detail in the middle,” Grover said.

    Grover’s team wanted to increase the number of species to better understand the chemical reactions taking place during combustion, but in-house computational resources could not compute such complex chemical changes with high accuracy within a reasonable time frame.

    To test the limits of their in-house resources, Grover’s team increased the number of chemical species to 766 and planned to simulate combustion across a span of 280 crank angle degrees, which is a measure of engine-cycle progress. An entire engine cycle, with one combustion event, equals 720 crank angle degrees.

    “It took 15 days just to compute 150 crank angle degrees. So, we didn’t even finish the calculation in over 2 weeks,” he said. “But we still wanted to model the highest fidelity chemistry package that we could.”

    To reduce computing time while increasing the complexity of the chemistry calculations, the GM team would need an extremely powerful computer and a new approach.

    A richer recipe for combustion

    Grover and the GM team turned to DOE for assistance. Through DOE’s Advanced Scientific Computing Research (ASCR) Leadership Computing Challenge (ALCC), a competitive peer-reviewed program, they successfully applied for and were awarded time on Titan during 2015 and 2016.

    A 27-petaflop Cray XK7 supercomputer with a hybrid CPU–GPU architecture, Titan is the nation’s most powerful computer for open scientific research. To make the most of the computing allocation, Grover’s team worked with Dean Edwards, Wael Elwasif, and Charles Finney at ORNL’s National Transportation Research Center to optimize combustion models for Titan’s architecture and add chemical species. They also partnered with Russell Whitesides at DOE’s Lawrence Livermore National Laboratory (LLNL). Whitesides is a developer of a chemical-kinetics solver called Zero-RK, which can use GPUs to accelerate computations. Both the ORNL and LLNL efforts are funded by DOE’s Vehicle Technologies Office (VTO).

    The team combined Zero-RK with the CONVERGE computational fluid dynamics (CFD) software that Grover uses in-house. CONVERGE is the product of a small-business CFD software company called Convergent Science.

    The GM team set out to accomplish three things: use Titan’s GPUs so they could increase the complexity of the chemistry in their combustion models, compare the results of Titan simulations with GM experimental data to measure accuracy, and identify other areas for improvement in the combustion model.

    “Their goal was to be able to better simulate what actually happens in the engine,” said Edwards, the ORNL principal investigator.

    ORNL’s goal was to help the GM team improve the accuracy of the combustion model, an exercise that could benefit other combustion research down the road. “The first step was to improve the emissions predictions by adding detail back into the simulation,” Edwards said.

    “And the bigger the recipe, the longer it takes the computer to solve it,” Finney said.

    This was also a computationally daunting step because chemistry does not happen in a vacuum.

    “On top of chemical kinetics, for our engine work, we have to model the movement of the piston, the movement of the valves, the spray injection, the turbulent flow—all of these things in addition to the chemistry,” Grover said.

    The combustion model also needed to accurately simulate the many different operating conditions created in the engine. To simulate combustion under realistic conditions, GM brought experimental data for about 600 operating conditions—points measuring the balance of engine load (a measure of work output from the engine) and engine speed (revolutions per minute) that mimic realistic driving conditions in which a driver is braking, accelerating, driving uphill or downhill, idling in traffic, and more.

    The team simulated a baseline model of 50 chemical species that matched what GM routinely computed in-house, then added 94 chemical species for a total of 144.

    “On Titan, we almost tripled our number of species,” Grover said. “We found that by using the Zero-RK GPU solver for chemistry, the chemistry computations ran about 33 percent faster.”

    These encouraging results led the team to increase the number of chemical species to 766. What had taken the team over 2 weeks to do in-house—modeling 766 species across 150 crank angle degrees—was completed in 5 days on Titan.

    In addition, the team was able to complete the calculations over the desired 280 crank angle degrees, something that wasn’t possible using in-house resources.

    “We gathered a lot of success here,” Grover said.

    With the first objective met—to see if they could increase simulation detail within a manageable compute time by using Titan’s GPUs—they moved on to compare accuracy against the experimental data.

    They measured emissions including nitrogen oxides, carbon monoxide, soot, and unburned hydrocarbons (fuel that did not burn completely).

    “Nitrogen oxide emissions in particular are tied to temperature and how a diesel engine combustion system operates,” Edwards said. “Diesel engines tend to operate at high temperatures and create a lot of nitrogen oxides.”

    Compared with the baseline Titan simulation, the refined Titan simulation with 766 species improved nitrogen oxide predictions by 10–20 percent.

    “That was one of our objectives: Can we model bigger chemistry and learn anything? Yes, we can,” Grover said, noting that the team saw some improvements for soot predictions as well but still struggled with increasing predictive accuracy for carbon monoxide and unburned hydrocarbon emissions.

    “That’s not a bad result because we were able to see that maybe there’s something we’re missing other than chemistry,” Grover said.

    “We need to spend more time evaluating the validity of those wall temperatures,” Grover said. “We’re actually going to compute the wall temperatures by simulating the effect of the coolant flow around the engine. We’re hoping better heat transfer predictions will give us a big jump in combination with better chemistry.”

    Another result was the demonstration of the GPUs’ ability to solve new problems.

    The parallelism boosted by Titan’s GPUs enabled the throughput necessary to calculate hundreds of chemical species across hundreds of operating points. “Applying GPUs for computer-aided engineering could open up another benefit,” Grover said.

    If GPUs can help reduce design time, that could boost business.

    “That’s faster designs to market,” Grover said. “Usually a company will go through a vehicle development process from end-to-end that could take 4 or 5 years. If you could develop the powertrain faster, then you could get cars to market faster and more reliably.”

    Science paper:
    Steady-State Calibration of a Diesel Engine in CFD Using a GPU-based Chemistry Solver ASME

    See the full article here .

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    ORNL is managed by UT-Battelle for the Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time.

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    The Oak Ridge Leadership Computing Facility (OLCF) was established at Oak Ridge National Laboratory in 2004 with the mission of accelerating scientific discovery and engineering progress by providing outstanding computing and data management resources to high-priority research and development projects.

    ORNL’s supercomputing program has grown from humble beginnings to deliver some of the most powerful systems in the world. On the way, it has helped researchers deliver practical breakthroughs and new scientific knowledge in climate, materials, nuclear science, and a wide range of other disciplines.

    The OLCF delivered on that original promise in 2008, when its Cray XT “Jaguar” system ran the first scientific applications to exceed 1,000 trillion calculations a second (1 petaflop). Since then, the OLCF has continued to expand the limits of computing power, unveiling Titan in 2013, which is capable of 27 petaflops.


    ORNL Cray XK7 Titan Supercomputer

    Titan is one of the first hybrid architecture systems—a combination of graphics processing units (GPUs), and the more conventional central processing units (CPUs) that have served as number crunchers in computers for decades. The parallel structure of GPUs makes them uniquely suited to process an enormous number of simple computations quickly, while CPUs are capable of tackling more sophisticated computational algorithms. The complimentary combination of CPUs and GPUs allow Titan to reach its peak performance.

    The OLCF gives the world’s most advanced computational researchers an opportunity to tackle problems that would be unthinkable on other systems. The facility welcomes investigators from universities, government agencies, and industry who are prepared to perform breakthrough research in climate, materials, alternative energy sources and energy storage, chemistry, nuclear physics, astrophysics, quantum mechanics, and the gamut of scientific inquiry. Because it is a unique resource, the OLCF focuses on the most ambitious research projects—projects that provide important new knowledge or enable important new technologies.

     
  • richardmitnick 1:37 pm on January 12, 2017 Permalink | Reply
    Tags: Argo project, , , , Hobbes project, , ORNL Cray XK7 Titan supercomputer, XPRESS project   

    From ASCRDiscovery via D.O.E. “Upscale computing” 

    DOE Main

    Department of Energy

    ASCRDiscovery

    ASCRDiscovery

    January 2017
    No writer credit

    National labs lead the push for operating systems that let applications run at exascale.

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    Image courtesy of Sandia National Laboratories.

    For high-performance computing (HPC) systems to reach exascale – a billion billion calculations per second – hardware and software must cooperate, with orchestration by the operating system (OS).

    But getting from today’s computing to exascale requires an adaptable OS – maybe more than one. Computer applications “will be composed of different components,” says Ron Brightwell, R&D manager for scalable systems software at Sandia National Laboratories.

    “There may be a large simulation consuming lots of resources, and some may integrate visualization or multi-physics.” That is, applications might not use all of an exascale machine’s resources in the same way. Plus, an OS aimed at exascale also must deal with changing hardware. HPC “architecture is always evolving,” often mixing different kinds of processors and memory components in heterogeneous designs.

    As computer scientists consider scaling up hardware and software, there’s no easy answer for when an OS must change. “It depends on the application and what needs to be solved,” Brightwell explains. On top of that variability, he notes, “scaling down is much easier than scaling up.” So rather than try to grow an OS from a laptop to an exascale platform, Brightwell thinks the other way. “We should try to provide an exascale OS and runtime environment on a smaller scale – starting with something that works at a higher scale and then scale down.”

    To explore the needs of an OS and conditions to run software for exascale, Brightwell and his colleagues conducted a project called Hobbes, which involved scientists at four national labs – Oak Ridge (ORNL), Lawrence Berkeley, Los Alamos and Sandia – plus seven universities. To perform the research, Brightwell – with Terry Jones, an ORNL computer scientist, and Patrick Bridges, a University of New Mexico associate professor of computer science – earned an ASCR Leadership Computing Challenge allocation of 30 million processor hours on Titan, ORNL’s Cray XK7 supercomputer.

    ORNL Cray Titan Supercomputer
    ORNL Cray XK7 Titan Supercomputer

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    The Hobbes OS supports multiple software stacks working together, as indicated in this diagram of the Hobbes co-kernel software stack. Image courtesy of Ron Brightwell, Sandia National Laboratories.

    Brightwell made a point of including the academic community in developing Hobbes. “If we want people in the future to do OS research from an HPC perspective, we need to engage the academic community to prepare the students and give them an idea of what we’re doing,” he explains. “Generally, OS research is focused on commercial things, so it’s a struggle to get a pipeline of students focusing on OS research in HPC systems.”

    The Hobbes project involved a variety of components, but for the OS side, Brightwell describes it as trying to understand applications as they become more sophisticated. They may have more than one simulation running in a single OS environment. “We need to be flexible about what the system environment looks like,” he adds, so with Hobbes, the team explored using multiple OSs in applications running at extreme scale.

    As an example, Brightwell notes that the Hobbes OS envisions multiple software stacks working together. The OS, he says, “embraces the diversity of the different stacks.” An exascale system might let data analytics run on multiple software stacks, but still provide the efficiency needed in HPC at extreme scales. This requires a computer infrastructure that supports simultaneous use of multiple, different stacks and provides extreme-scale mechanisms, such as reducing data movement.

    Part of Hobbes also studied virtualization, which uses a subset of a larger machine to simulate a different computer and operating system. “Virtualization has not been used much at extreme scale,” Brightwell says, “but we wanted to explore it and the flexibility that it could provide.” Results from the Hobbes project indicate that virtualization for extreme scale can provide performance benefits at little cost.

    Other HPC researchers besides Brightwell and his colleagues are exploring OS options for extreme-scale computing. For example, Pete Beckman, co-director of the Northwestern-Argonne Institute of Science and Engineering at Argonne National Laboratory, runs the Argo project.

    A team of 25 collaborators from Argonne, Lawrence Livermore National Laboratory and Pacific Northwest National Laboratory, plus four universities created Argo, an OS that starts with a single Linux-based OS and adapts it to extreme scale.

    When comparing the Hobbes OS to Argo, Brightwell says, “we think that without getting in that Linux box, we have more freedom in what we do, other than design choices already made in Linux. Both of these OSs are likely trying to get to the same place but using different research vehicles to get there.” One distinction: The Hobbes project uses virtualization to explore the use of multiple OSs working on the same simulation at extreme scale.

    As the scale of computation increases, an OS must also support new ways of managing a systems’ resources. To explore some of those needs, Thomas Sterling, director of Indiana University’s Center for Research in Extreme Scale Technologies, developed ParalleX, an advanced execution model for computations. Brightwell leads a separate project called XPRESS to support the ParalleX execution model. Rather than computing’s traditional static methods, ParalleX implementations use dynamic adaptive techniques.

    More work is always necessary as computation works toward extreme scales. “The important thing in going forward from a runtime and OS perspective is the ability to evaluate technologies that are developing in terms of applications,” Brightwell explains. “For high-end applications to pursue functionality at extreme scales, we need to build that capability.” That’s just what Hobbes and XPRESS – and the ongoing research that follows them – aim to do.

    See the full article here .

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    The mission of the Energy Department is to ensure America’s security and prosperity by addressing its energy, environmental and nuclear challenges through transformative science and technology solutions.

     
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