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  • richardmitnick 12:23 pm on December 28, 2018 Permalink | Reply
    Tags: , , , Supercomputing, , UT Students Get Bite-Sized Bits of Big Data Centers in ORNL-Led Course   

    From Oak Ridge Leadership Computing Facility: “UT Students Get Bite-Sized Bits of Big Data Centers in ORNL-Led Course” 

    i1

    Oak Ridge National Laboratory

    From Oak Ridge Leadership Computing Facility

    20 Dec, 2018
    Rachel Harken

    1
    Image Credit: Genevieve Martin, ORNL

    This fall, staff at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) once again contributed to the “Introduction to Data Centers” course at the University of Tennessee, Knoxville (UT).

    Now in its fourth year, the class had the largest and most diverse enrollment yet, with four disciplines represented: computer engineering, computer science, electrical engineering, and industrial engineering. This year’s students toured the data centers at the Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility located at ORNL, earlier this fall as part of the course.

    The multidisciplinary course, part of UT’s data center technology and management minor, introduces students to the many topics involved in building and commanding a data center. Because running a data center requires knowledge in a multitude of areas, no one discipline typically covers the broad spectrum of topics involved.

    The multidisciplinary course, part of UT’s data center technology and management minor, introduces students to the many topics involved in building and commanding a data center. Because running a data center requires knowledge in a multitude of areas, no one discipline typically covers the broad spectrum of topics involved.

    “We bring in a lot of disciplinary experts from ORNL,” said Stephen McNally, operations manager at the OLCF and the course organizer. “We cover the mechanical and electrical components, but we also focus on project management, commissioning, overall requirements-gathering, and networking.” The current curriculum was developed by McNally, UT interim dean of the College of Engineering Mark Dean, UT professor David Icove, and ORNL project specialist Jennifer Goodpasture.

    The students enrolled in the course are provided a request for proposals at the beginning of the year, and they work together throughout the semester to submit a 20- to 30-page proposal to meet the requirements. Because students are often restricted to classes within their majors, the course stresses the interplay between disciplines and showcases areas that might previously have been out of reach.

    “Hiring someone straight out of school to do what a data center person does is really difficult, because you have to understand so much about so many different disciplines,” McNally said. “This is primarily why we have such a low talent pool for data center–related jobs. We built this class to help solve that problem.”

    The course is opening new opportunities for some students. Two of the students in this year’s class received scholarships to Infrastructure Masons (iMasons), an organization that brings digital infrastructure experts together to network, learn, and collaborate. The students’ enrollment in the course through the new minor degree program qualified them to apply.

    Aside from the opportunity to apply for the iMasons scholarship, students learned from new data center professionals in industry this year. One of the course’s new speakers was Frank Hutchison of SH Data Technologies, who talked about his role in building Tennessee’s first tier 3 data center. Tier 3 data centers are available 99.9% of the time, which means they are only down for seconds at a time each year.

    “This was the most engaging class we’ve had by far,” McNally said. “These students really got to see how these different disciplines work together to run, build, and operate data centers, and we are excited to continue bringing these folks in and helping to bridge this talent gap in the workforce.”

    The team is excited that this course continues to gain traction with the students at UT and is making plans to accommodate more students next fall. The course is currently under consideration for possible expansion into a professional certification program or a distance learning course.

    In addition to McNally and Goodpasture, the ORNL team contributing to the course includes Jim Serafin, Jim Rogers, Kathlyn Boudwin, Justin Whitt, Darren Norris, David Grant, Rick Griffin, Saeed Ghezawi, Brett Ellis, Bart Hammontree, Scott Milliken, Gary Rogers, and Kris Torgerson.

    See the full article here .

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    Please help promote STEM in your local schools.

    Stem Education Coalition

    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.

    i2

    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.

    ORNL IBM AC922 SUMMIT supercomputer. Credit: Carlos Jones, Oak Ridge National Laboratory/U.S. Dept. of Energy

    With a peak performance of 200,000 trillion calculations per second—or 200 petaflops, Summit will be eight times more powerful than ORNL’s previous top-ranked system, Titan. For certain scientific applications, Summit will also be capable of more than three billion billion mixed precision calculations per second, or 3.3 exaops. Summit will provide unprecedented computing power for research in energy, advanced materials and artificial intelligence (AI), among other domains, enabling scientific discoveries that were previously impractical or impossible.

    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.

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  • richardmitnick 11:15 am on December 28, 2018 Permalink | Reply
    Tags: , , , , , , Supercomputing, The cosmos in a computer   

    From Science Node: “The cosmos in a computer” 

    Science Node bloc
    From Science Node

    28 Nov, 2018
    Ellen Glover

    How simulated galaxies could bring us one step closer to the origin of our universe.

    Thanks to telescopes like the Hubble and spacecrafts like Kepler, we know more than ever about the Milky Way Galaxy and what lies beyond. However, these observations only tell part of the story.

    NASA/ESA Hubble Telescope

    NASA/Kepler Telescope

    How did our incomprehensively vast universe come to be? What’s it going to look like millions of years from now? These age-old questions are now getting answers thanks to simulations created by supercomputers.

    One of these supercomputers is a Cray XC50, nicknamed ATERUI II and located at the National Astronomical Observatory in Japan (NAOJ).

    NAOJ ATERUI II Cray XC50 supercomputer ocated at the National Astronomical Observatory in Japan (NAOJ)

    It is the fastest supercomputer dedicated to astronomy and is ranked #83 of the top 500 most powerful supercomputers in the world.

    Named after a prominent 9th century chief, the ATERUI II is located in the same city where Aterui led his tribe in a battle against Emperor Kanmu. Despite the odds, Aterui and his people fought well. Since then, Aterui has become a symbol of intelligence, bravery, and unification.

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    100 billion. ATERUI II is able to calculate the mutual gravitational interactions between each of the more than 100 billion stars that make up our galaxy, allowing for the most detailed Milky Way simulation yet. Courtesy National Astronomical Observatory of Japan.

    “We named the supercomputer after him so that our astronomers can be brave and smart. While we are not the fastest in the world, we hope the ATERUI II can be used in a smart way to help unify us so we can better understand the universe,” says Eiichiro Kokubo, project director of the Center for Computational Astrophysics at NAOJ.

    ATERUI II was officially launched last June and serves as a bigger and better version of its decommissioned predecessor, ATERUI. With more than 40,000 processing cores and 385 Terabytes of memory, ATERUI II can perform as many as 3 quadrillion operations per second.

    In other words: it’s an incredibly powerful machine that is allowing us to boldly go where no one has ever gone before, from the Big Bang to the death of a star. It’s also exceedingly popular with researchers—150 astronomers are slated to use the supercomputer by the end of the year.

    ATERUI II’s unique power means it is capable of solving problems deemed too difficult for other supercomputers. For example, an attempt to simulate the Milky Way on a different machine meant researchers had to group the stars together in order to calculate their gravitational interactions.

    ATERUI II doesn’t have that problem. It’s able to calculate the mutual gravitational interactions between each of the more than 100 billion stars that make up our galaxy individually, allowing for the most detailed Milky Way Galaxy simulation yet.

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    The death of a star a thousand years ago left behind a superdense neutron star that expels extremely high-energy particles. By simulating events like these, ATERUI II gives astronomer’s insights that can’t be discovered through observation alone. Courtesy NASA/JPL-Caltech/ESA/CXC/Univ. of Ariz./Univ. of Szeged.

    While computational astronomy is a fairly young field, we need it in order to understand the universe beyond just observing celestial bodies. With its superior computational power, Kokubo says there are plans for ATERUI II to simulate everything from Saturn’s rings through a binary star formation to the large scale structure of the universe.

    “If we produce the universe in a computer, then we can use it to simulate the past and the future as well,” Kokubo says. “The universe exists in four dimensions: the first three are space and the last one is time. If we can capture the space, then we can better observe it through time.”

    ATERUI II isn’t only working on ways to better understand the stars and planets that make up the universe, it is also being used to explore the possibility of alien life. This starts with life on Earth.

    “If we can simulate and understand the origin of life on Earth and what it means to be habitable, we will be even closer to finding it elsewhere in the universe,” Kokubo says. “I’m interested in life and why we are here.”

    Kokubo isn’t alone. The mystery of how we came to be and what it all means has fascinated mankind for centuries. Our unknown origins have been explored in great pieces of art and literature throughout history and are at the core of every religion. Now, thanks to ATERUI II, we are one step closer to getting our answer.

    See the full article here .


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    Please help promote STEM in your local schools.

    Stem Education Coalition

    Science Node is an international weekly online publication that covers distributed computing and the research it enables.

    “We report on all aspects of distributed computing technology, such as grids and clouds. We also regularly feature articles on distributed computing-enabled research in a large variety of disciplines, including physics, biology, sociology, earth sciences, archaeology, medicine, disaster management, crime, and art. (Note that we do not cover stories that are purely about commercial technology.)

    In its current incarnation, Science Node is also an online destination where you can host a profile and blog, and find and disseminate announcements and information about events, deadlines, and jobs. In the near future it will also be a place where you can network with colleagues.

    You can read Science Node via our homepage, RSS, or email. For the complete iSGTW experience, sign up for an account or log in with OpenID and manage your email subscription from your account preferences. If you do not wish to access the website’s features, you can just subscribe to the weekly email.”

     
  • richardmitnick 9:39 am on December 13, 2018 Permalink | Reply
    Tags: , , , , HPC Spaceborne Computer, , , Spaceborne Computer is first step in helping NASA get humanity to Mars, Supercomputing   

    From Science Node: “Launching a supercomputer into space” 

    Science Node bloc
    From Science Node

    03 Dec, 2018
    Kevin Jackson

    1
    HPC Spaceborne supercomputer replica.

    Spaceborne Computer is first step in helping NASA get humanity to Mars.

    The world needs more scientists like Dr. Mark Fernandez. His southern drawl and warm personality almost make you overlook the fact that he’s probably forgotten more about high-performance computing (HPC) than you’ll ever know.


    The Spaceborne Computer is currently flying aboard the International Space Station to prove that high-performance computing hardware can survive and operate in outer space conditions. Courtesy HPE.

    Fernandez is the Americas HPC Technology Officer for Hewlett Packard Enterprise (HPE). His current baby is the Spaceborne Computer, a supercomputer that has spent more than a year aboard the International Space Station (ISS).

    In this time, the Spaceborne Computer has run through a gamut of tests to ensure it works like it’s supposed to. Now, it’s a race to accomplish as much as possible before the machine is brought home.

    Computing for the stars

    The Spaceborne Computer’s history extends well before its launch to the ISS. In fact, Fernandez explains that the project began about three years prior.

    “NASA Ames was in a meeting with us in the summer of 2014 and they said that, for a mission to Mars or for a lunar outpost, the distance was so far that they would not be able to continue their mission of supporting the space explorers,” says Fernandez. “And so they just sort of off-handedly said, ‘take part of our current supercomputer and see what it would take to get it operating in space.’ And we took up the challenge.”

    When astronauts send and receive data to and from Earth, this information is moving at the speed of light. In the ISS, which is 240 miles (400 kilometers) away from Earth, data transmission still happens very quickly. The same won’t be true when humans begin our journey into the rest of the cosmos.

    “All science and engineering done here on Earth requires some type of high performance computing to make it function,” says Fernandez. “You don’t want to be 24 minutes away and trying to do your Mars dust storm predictions. You want to be able to take those scientific and engineering computations that are currently done here on Earth and bring them with you.”

    To get ready for these kinds of tasks, the Spaceborne Computer has spent the past year performing standard benchmarking tests in what Fernandez calls the “acceptance phase.” Now that these experiments are done, it’s time to get interesting.

    The sky’s not the limit

    For traditional supercomputers, powering and cooling the machine often represents a huge cost. This isn’t true in space.

    “The Moderate Temperature Loop (MTL) is how the environment for the human astronauts is maintained at a certain temperature,” says Fernandez. “Our experiments are allowed to tap into that MTL, and that’s where we put our heat. Our heat is then expelled into the coldness of space for free. We have free electricity coming from the solar cells, and we have free cooling from the coldness of space and therefore, by definition, we have the most energy efficient supercomputer in existence anywhere on Earth or elsewhere.”

    The cost-neutral aspect of the Spaceborne Computer allows HPE to give researchers access to the machine for free before it must return to Earth. One of these experiments, announced at SC18, concerns Entry, Descent, and Landing (EDL) software.

    “If you’re going to build a Mars habitat, you need to land carefully,” says Fernandez. “This EDL software runs in real time, it’s connected to the thrusters on the spacecraft, and in real time determines where you are and adjusts your thrusters so that you can land within 50 meters of your target. Now, it’s never been tested in space, and the only place it will ever run is in space. So they’re very excited about getting it to run on the Spaceborne Computer.”

    While Fernandez is delighted that his machine will be able to test important innovations like this, he seems dismayed by all the science he won’t be able to do. The Spaceborne Computer will soon be brought back home by NASA, and he’s doing what he can to cram in as many important experiments as possible.

    Fernandez’s attitude speaks volumes about the mental outlook we’ll need to traverse the cosmos. He often uses the term “space explorers” in place of “astronauts” or even “researchers.” It’s a term that cuts to the heart of what scientists like him are attempting to do.

    “We’re proud to be good space explorers,” says Fernandez. “I say, let’s all work together. We’ve got free electricity. We have free cooling. Let’s push science as far and as hard as we can.”

    See the full article here .


    five-ways-keep-your-child-safe-school-shootings
    Please help promote STEM in your local schools.

    Stem Education Coalition

    Science Node is an international weekly online publication that covers distributed computing and the research it enables.

    “We report on all aspects of distributed computing technology, such as grids and clouds. We also regularly feature articles on distributed computing-enabled research in a large variety of disciplines, including physics, biology, sociology, earth sciences, archaeology, medicine, disaster management, crime, and art. (Note that we do not cover stories that are purely about commercial technology.)

    In its current incarnation, Science Node is also an online destination where you can host a profile and blog, and find and disseminate announcements and information about events, deadlines, and jobs. In the near future it will also be a place where you can network with colleagues.

    You can read Science Node via our homepage, RSS, or email. For the complete iSGTW experience, sign up for an account or log in with OpenID and manage your email subscription from your account preferences. If you do not wish to access the website’s features, you can just subscribe to the weekly email.”

     
  • richardmitnick 10:34 am on November 29, 2018 Permalink | Reply
    Tags: , , , , Supercomputing   

    From Science Node: “The race to exascale” 

    Science Node bloc
    From Science Node

    30 Jan, 2018
    Alisa Alering

    Who will get the first exascale machine – a supercomputer capable of 10^18 floating point operations per second? Will it be China, Japan, or the US?

    1
    When it comes to computing power you can never have enough. In the last sixty years, processing power has increased more than a trillionfold.

    Researchers around the world are excited because these new, ultra-fast computers represent a 50- to 100-fold increase in speed over today’s supercomputers and promise significant breakthroughs in many areas. That exascale supercomputers are coming is pretty clear. We can even predict the date, most likely in the mid-2020s. But the question remains as to what kind of software will run on these machines.

    Exascale computing heralds an era of ubiquitous massive parallelism, in which processors perform coordinated computations simultaneously. But the number of processors will be so high that computer scientists will have to constantly cope with failing components.

    The high number of processors will also likely slow programs tremendously. The consequence is that beyond the exascale hardware, we will also need exascale brains to develop new algorithms and implement them in exascale software.

    In 2011, the German Research Foundation established a priority program “Software for Exascale Computing”( SPPEXA ) to addresses fundamental research on various aspects of high performance computing (HPC) software, making the program the first of its kind in Germany.

    SPPEXA connects relevant sub-fields of computer science with the needs of computational science and engineering and HPC. The program provides the framework for closer cooperation and a co-design-driven approach. This is a shift from the current service-driven collaboration of groups focusing on fundamental HPC methodology (computer science or mathematics) on the one side with those working on science applications and providing the large codes (science and engineering) on the other side.

    Despite exascale computing still being several years away, SPPEXA scientists are well ahead of the game, developing scalable and efficient algorithms that will make the best use of resources when the new machines finally arrive. SPPEXA drives research towards extreme-scale computing in six areas: computational algorithms, system software, application software, data management and exploration, programming, and software tools.

    Some major projects include research on alternative sources of clean energy; stronger, lighter weight steel manufacturing; and unprecedented simulations of the earth’s convective processes:

    EXAHD supports Germany’s long-standing research into the use of plasma fusion as a clean, safe, and sustainable carbon-free energy source. One of the main goals of the EXAHD project is to develop scalable and efficient algorithms to run on distributed systems, with the aim of facilitating the progress of plasma fusion research.

    EXASTEEL is a massively parallel simulation environment for computational material science. Bringing together experts from mathematics, material and computer sciences, and engineering, EXASTEEL will serve as a virtual laboratory for testing new forms of steel with greater strengths and lower weight.

    TerraNeo addresses the challenges of understanding the convection of Earth’s mantle – the cause of most of our planet’s geological activity, from plate tectonics to volcanoes and earthquakes. Due to the sheer scale and complexity of the models, the advent of exascale computing offers a tremendous opportunity for greater understanding. But in order to take full advantage of the coming resources, TerraNeo is working to design new software with optimal algorithms that permit a scalable implementation.

    Exascale hardware is expected to have less consistent performance than current supercomputers due to fabrication, power, and heat issues. Their sheer size and unprecedented number of components will likely increase fault rates. Fast and Fault-Tolerant Microkernel-based Operating System for Exascale Computing (FFMK) aims to address these challenges through a coordinated approach that connects system software, computational algorithms, and application software.

    Mastering the various challenges related to the paradigm shift from moderately to massively parallel processing will be the key to any future capability computing application at exascale. It will also be crucial for learning how to effectively and efficiently deal with near-future commodity systems smaller-scale or capacity computing tasks. No matter who puts the first machine online, exascale supercomputing is coming. SPPEXA is making sure we are prepared to take full advantage of it.

    See the full article here .


    five-ways-keep-your-child-safe-school-shootings
    Please help promote STEM in your local schools.

    Stem Education Coalition

    Science Node is an international weekly online publication that covers distributed computing and the research it enables.

    “We report on all aspects of distributed computing technology, such as grids and clouds. We also regularly feature articles on distributed computing-enabled research in a large variety of disciplines, including physics, biology, sociology, earth sciences, archaeology, medicine, disaster management, crime, and art. (Note that we do not cover stories that are purely about commercial technology.)

    In its current incarnation, Science Node is also an online destination where you can host a profile and blog, and find and disseminate announcements and information about events, deadlines, and jobs. In the near future it will also be a place where you can network with colleagues.

    You can read Science Node via our homepage, RSS, or email. For the complete iSGTW experience, sign up for an account or log in with OpenID and manage your email subscription from your account preferences. If you do not wish to access the website’s features, you can just subscribe to the weekly email.”

     
  • richardmitnick 9:38 am on November 29, 2018 Permalink | Reply
    Tags: 1. Summit (US), 2. Sierra (US), 3. Sunway TaihuLight (China), 4. Tianhe-2 (China), 5. Piz Daint (Switzerland), , , , Supercomputing,   

    From Science Node: “The 5 fastest supercomputers in the world” 

    Science Node bloc
    From Science Node

    Countries around the world strive to reach the peak of computing power–but there can be only one.

    19 Nov, 2018
    11.29.18 update
    Kevin Jackson

    Peak performance within supercomputing is a constantly moving target. In fact, a supercomputer is defined as being any machine “that performs at or near the currently highest operational rate.” The field is a continual battle to be the best. Those who achieve the top rank may only hang on to it for a fleeting moment.

    Competition is what makes supercomputing so exciting, continually driving engineers to reach heights that were unimaginable only a few years ago. To celebrate this amazing technology, let’s take a look at the fastest computers as defined by computer ranking project TOP500—and at what these machines are used for.

    5. Piz Daint (Switzerland)

    Cray Piz Daint supercomputer of the Swiss National Supercomputing Center (CSCS)

    Named after a mountain in the Swiss Alps, Piz Daint has been Europe’s fastest supercomputer since its debut in November 2013. But a recent 40 million Euro upgrade has boosted the Swiss National Supercomputer Centre’s machine into the global top five, now running at 21.2 petaFLOPS and ­utilizing 387,872 cores.

    The machine has helped scientists at the University of Basel make discoveries about “memory molecules” in the brain. Other Swiss scientists have taken advantage of its ultra-high resolutions to set up a near-global climate simulation.

    4. Tianhe-2 (China)

    China’s Tianhe-2 Kylin Linux supercomputer at National Supercomputer Center, Guangzhou, China

    Tianhe-2, whose name translates as “MilkyWay-2,” has also seen recent updates. But despite now boasting a whopping 4,981,760 cores and running at 61.4 petaFLOPS, that hasn’t stopped it from slipping two spots in just one year—from #2 to #4.

    TOP500 reported that the machine, developed by the National University of Defense Technology (NUDT) in China, is intended mainly for government security applications. This means that much of the work done by Tianhe-2 is kept secret, but if its processing power is anything to judge by, it must be working on some pretty important projects.

    3. Sunway TaihuLight (China)

    Sunway NRCPC TaihuLight, China, US News

    A former number one, Sunway TaihuLight dominated the list since its debut in June 2016. At that time, it’s 93.01 petaFLOPS and 10,649,000 cores made it the world’s most powerful supercomputer by a wide margin, boasting more than five times the processing power of its nearest competitor (ORNL’s Titan) and nearly 19 times more cores.

    But given the non-stop pace of technological advancement, no position is ever secure for long. TaihuLight ceded the top spot to competitors in June 2018.

    Located at the National Supercomputing Center in Wuxi, China, TaihuLight’s creators are using the supercomputer for tasks ranging from climate science to advanced manufacturing. It has also found success in marine forecasting, helping ships avoid rough seas while also helping with offshore oil drilling.

    2. Sierra (US)

    LLNL IBM NVIDIA Mellanox ATS-2 Sierra Supercomputer

    Sierra initially debuted at #3 on the June 2018 list with 71.6 petaFLOPS, but optimization has since pushed the processing speed on its 1,572,480 cores to 94.6 petaFLOPS, earning it the #2 spot in November 2018.

    Incorporating both IBM central processing units (CPUs) and NVIDIA graphics processing units (GPUs), Sierra is specifically designed for modeling and simulations essential for the US National Nuclear Security Administration.

    1. Summit (US)

    ORNL IBM AC922 SUMMIT supercomputer. Credit: Carlos Jones, Oak Ridge National Laboratory/U.S. Dept. of Energy

    Showing further evidence of the US Department of Energy’s renewed commitment to supercomputing power, Oak Ridge National Laboratory’s (ORNL) Summit first claimed the #1 spot in June 2018, taking the top rank from China for the first time in 6 years. Further upgrades have cemented that spot—at least until the next list comes out in June 2019.

    In the five months since its debut on the June 2018 list, Summit has widened its lead as the number one system, improving its High Performance Linpack (HPL) performance from 122.3 to 143.5 petaFLOPS.

    Scientists are already putting the world’s most powerful computer to work. A seven-member team from ORNL won the 2018 Gordon Bell Prize for their deployment of Summit to process genetic data in order to better understand how individuals develop chronic pain and respond to opioids.

    The race to possess the most powerful supercomputer never really ends. This friendly competition between countries has propelled a boom in processing power, and it doesn’t look like it’ll be slowing down anytime soon. With scientists using supercomputers for important projects such as curing debilitating diseases, we can only hope it will continue for years to come. [Whoever thinks this is a “friendly competition between countries” is way off base. This is a part of the Chinese route to world dominance]

    See the full article here .


    five-ways-keep-your-child-safe-school-shootings
    Please help promote STEM in your local schools.

    Stem Education Coalition

    Science Node is an international weekly online publication that covers distributed computing and the research it enables.

    “We report on all aspects of distributed computing technology, such as grids and clouds. We also regularly feature articles on distributed computing-enabled research in a large variety of disciplines, including physics, biology, sociology, earth sciences, archaeology, medicine, disaster management, crime, and art. (Note that we do not cover stories that are purely about commercial technology.)

    In its current incarnation, Science Node is also an online destination where you can host a profile and blog, and find and disseminate announcements and information about events, deadlines, and jobs. In the near future it will also be a place where you can network with colleagues.

    You can read Science Node via our homepage, RSS, or email. For the complete iSGTW experience, sign up for an account or log in with OpenID and manage your email subscription from your account preferences. If you do not wish to access the website’s features, you can just subscribe to the weekly email.”

     
  • richardmitnick 9:25 pm on November 12, 2018 Permalink | Reply
    Tags: , , By the late 1980s the Argonne Computing Research Facility (ACRF) housed as many as 10 radically different parallel computer designs, Next on the horizon: exascale, Supercomputing   

    From Argonne National Laboratory ALCF: “Argonne’s pioneering computing program pivots to exascale” 

    Argonne Lab
    News from Argonne National Laboratory

    From Argonne National Laboratory ALCF

    November 12, 2018

    Laura Wolf
    Gail Pieper

    ANL ALCF Cetus IBM supercomputer

    ANL ALCF Theta Cray supercomputer

    ANL ALCF Cray Aurora supercomputer

    ANL ALCF MIRA IBM Blue Gene Q supercomputer at the Argonne Leadership Computing Facility

    When it comes to the breadth and range of the U.S. Department of Energy’s (DOE) Argonne National Laboratory’s contributions to the field of high-performance computing (HPC), few if any other organizations come close. Argonne has been building advanced parallel computing environments and tools since the 1970s. Today, the laboratory serves as both an expertise center and a world-renowned source of cutting-edge computing resources used by researchers to tackle the most pressing challenges in science and engineering.

    Since its digital automatic computer days in the early 1950s, Argonne has been interested in designing and developing algorithms and mathematical software for scientific purposes, such as the Argonne Subroutine Library in the 1960s and the so-called ​“PACKs” – e.g., EISPACK, LINPACK, MINPACK and FUNPACK – as well as Basic Linear Algebra Subprograms (BLAS) in the 1970s. In the 1980s, Argonne established a parallel computing program – nearly a decade before computational science was explicitly recognized as the new paradigm for scientific investigation and the government inaugurated the first major federal program to develop the hardware, software and workforce needed to solve ​“grand challenge” problems.

    A place for experimenting and community building

    By the late 1980s, the Argonne Computing Research Facility (ACRF) housed as many as 10 radically different parallel computer designs – nearly every emerging parallel architecture – on which applied mathematicians and computer scientists could explore algorithm interaction, program portability and parallel programming tools and languages. By 1987, Argonne was hosting a regular series of hands-on training courses on ACRF systems for attendees from universities, industry and research labs.

    In 1992, at DOE’s request, the laboratory acquired an IBM SP – the first scalable, parallel system to offer multiple levels of input/output (I/O) capability essential for increasingly complex scientific applications – and, with that system, embarked on a new focus on experimental production machines. Argonne’s High-Performance Computing Research Center (1992–1997) focused on production-oriented parallel computing for grand challenges in addition to computer science and emphasized collaborative research with computational scientists. By 1997, Argonne’s supercomputing center was recognized by the DOE as one of the nation’s four high-end resource providers.

    Becoming a leadership computing center

    In 2002, Argonne established the Laboratory Computing Resource Center and in 2004 formed the Blue Gene Consortium with IBM and other national laboratories to design, evaluate and develop code for a series of massively parallel computers. The laboratory installed a 5-teraflop IBM Blue Gene/L in 2005, a prototype and proving ground for what in 2006 would become the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science User Facility. Along with another leadership computing facility at Oak Ridge National Laboratory, the ALCF was chartered to operate some of the fastest supercomputing resources in the world dedicated to scientific discovery.

    In 2007, the ALCF installed a 100-teraflop Blue Gene/P and began to support projects under the Innovative and Novel Computational Impact on Theory and Experiment program. In 2008, ALCF’s 557-teraflop IBM Blue Gene/P, Intrepid, was named the fastest supercomputer in the world for open science (and third fastest machine overall) on the TOP500 list and, in 2009, entered production operation.

    4
    ALCF’s 557-teraflop IBM Blue Gene/P, Intrepid

    Intrepid also topped the first Graph 500 list in 2010 and again in 2011. In 2012, ALCF’s 10-petaflop IBM Blue Gene/Q, Mira [above], ranked third on the June TOP500 list and entered production operation in 2013.

    Next on the horizon: exascale

    Argonne is part of a broader community working to achieve a capable exascale computing ecosystem for scientific discoveries. The benefits of exascale computing – computing capability that can achieve at least a billion billion operations per second – is primarily in the applications it will enable. To take advantage of this immense computing power, Argonne researchers are contributing to the emerging convergence of simulation, big data analytics and machine learning across a wide variety of science and engineering domains and disciplines.

    In 2016, the laboratory launched an initiative to explore new ways to foster data-driven discoveries, with an eye to growing a new community of HPC users. The ALCF Data Science Program, the first of its kind in the leadership computing space, targets users with ​“big data” science problems and provides time on ALCF resources, staff support and training to improve computational methods across all scientific disciplines.

    In 2017, Argonne launched an Intel/Cray machine, Theta [above], doubling the ALCF’s capacity to do impactful science. The facility currently is operating at the frontier of data-centric and high-performance supercomputing.

    Argonne researchers are also getting ready for the ALCF’s future exascale system, Aurora [depicted above], expected in 2021. Using innovative technologies from Intel and Cray, Aurora will provide over 1,000 petaflops for research and development in three areas: simulation-based computational science; data-centric and data-intensive computing; and learning – including machine learning, deep learning, and other artificial intelligence techniques.

    The ALCF has already inaugurated an Early Science Program to prepare key applications and libraries for the innovative architecture. Moreover, ALCF computational scientists and performance engineers are working closely with Argonne’s Mathematics and Computer Science (MCS) division as well as its Computational Science and Data Science and Learning divisions with the aim of advancing the boundaries of HPC technologies ahead of Aurora. (The MCS division is the seedbed for such groundbreaking software as BLAS3, p4, Automatic Differentiation of Fortran Codes (ADIFOR), the PETSc toolkit of parallel computing software, and a version of the Message Passing Interface known as MPICH.)

    The ALCF also continues to add new services, helping researchers near and far to manage workflow execution of large experiments and to co-schedule jobs between ALCF systems, thereby extending Argonne’s reach even further as a premier provider of computing and data analysis resources for the scientific research community.

    See the full article here .

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    Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science. For more visit http://www.anl.gov.

    About ALCF

    The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing facilities in partnership with the computational science community.

    We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and expertise.

    ALCF projects cover many scientific disciplines, ranging from chemistry and biology to physics and materials science. Examples include modeling and simulation efforts to:

    Discover new materials for batteries
    Predict the impacts of global climate change
    Unravel the origins of the universe
    Develop renewable energy technologies

    Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

    Argonne Lab Campus

     
  • richardmitnick 2:06 pm on November 10, 2018 Permalink | Reply
    Tags: , LLNL Penguin Computing Corona AMD Mellanox high-performance computing cluster, Supercomputing   

    From Lawrence Livermore National Laboratory: “New computing cluster coming to Livermore” 

    From Lawrence Livermore National Laboratory

    Nov. 8, 2018
    Jeremy Thomas
    thomas244@llnl.gov
    925-422-5539

    LLNL Penguin Computing Corona AMD Mellanox high-performance computing cluster

    Lawrence Livermore National Laboratory, in partnership with Penguin Computing, AMD and Mellanox Technologies, will accept delivery of Corona, a new unclassified high-performance computing (HPC) cluster that will provide unique capabilities for Lab researchers and industry partners to explore data science, machine learning and big data analytics.

    The system will be provided by Penguin Computing and will be comprised of AMD EPYC™ processors and AMD Radeon™ Instinct™ GPU (graphics processing unit) accelerators connected via a Mellanox HDR 200 Gigabit InfiniBand network. The system lends itself to applying machine learning and data analysis techniques to challenging problems in HPC and big data and will be used to support the National Nuclear Security Administration’s (NNSA) Advanced Simulation and Computing (ASC) program. The system will be housed by Livermore Computing (LC) in an unclassified site adjacent to the High Performance Computing Innovation Center (HPCIC), dedicated to partnerships with American industry.

    Procured through the Commodity Technology Systems (CTS-1) contract, Corona will help NNSA assess future architectures, fill institutional and ASC needs to develop leadership in data science and machine learning capabilities at scale, provide access to HPCIC partners and extend a continuous collaboration vehicle for AMD, Penguin, Mellanox and LLNL.

    “Corona will provide an excellent platform for our research into cognitive computing algorithms and developing predictive simulations for both inertial confinement fusion applications as well as molecular dynamics simulations targeting precision medicine for oncology,” said Brian Van Essen, LLNL Informatics group leader and computer scientist. “The unique computational resources and interconnect will allow us to continue to develop leading edge algorithms for scalable distributed deep learning. As deep learning becomes an integral part of many applications at the Laboratory, computational resources like Corona are vital to our ability to develop the next generation of scientific applications.”

    Funded by the LLNL Multi-Programmatic and Institutional Computing (M&IC) program and the NNSA’s ASC program, the 383 teraFLOPS (floating point operations per second) Corona cluster will be delivered in late November and is expected to be available for limited use by December. The cluster consists of 170 two-socket nodes incorporating 24-core AMD EPYC™ 7401 processors and a PCIe 1.6 Terabyte (TB) nonvolatile (solid-state) memory device. Each Corona compute node is GPU-ready with half of those nodes utilizing four AMD Radeon Instinct™ MI25 GPUs per node, delivering 4.2 petaFLOPS of FP32 peak performance. The remaining compute nodes may be upgraded with future GPUs.

    Corona is likely to supplant the LLNL Catalyst cluster, a 150-teraFLOPS unclassified HPC cluster.

    It will run the NNSA-funded Tri-lab Open Source Software (TOSS) that provides a common user environment for Los Alamos, Sandia and Lawrence Livermore national labs.

    “We’re in a unique position working with this heterogenous architecture,” said Matt Leininger, deputy of Advanced Technology Projects for LLNL. “Corona is the next logical step in applying leading-edge technologies to the scientific discovery mission of the Laboratory. This system will be capable of generating big data from HPC simulations, while also being capable of translating that data into knowledge through the use of machine learning and data analysis.”

    The HPC Innovation Center at LLNL will offer access to Corona and the expected machine learning innovations it enables as a new option for its ongoing collaboration with American companies and research institutions.

    “Penguin Computing has been working with America’s national energy and defense labs on projects focused on open systems for almost 20 years,” said Sid Mair, senior vice president, federal systems at Penguin Computing. “During this long collaboration, we’ve been able to help them take advantage of the value, both in terms of return on investment and flexibility, that open systems provide compared to proprietary systems. Helping them deploy AI using open systems in the Corona system is an exciting new chapter in this relationship that we hope will help them execute their mission even more effectively.”

    “AMD welcomes the delivery of the Corona system to the HPCIC and the selection of high-performance AMD EPYC processors and AMD Radeon Instinct accelerators for the cluster,” said Mark Papermaster, AMD’s senior vice president and chief technology officer. “The collaboration between AMD, Penguin, Mellanox and Lawrence Livermore National Lab has built a world-class HPC system that will enable researchers to push the boundaries of science and innovation.”

    The system is interconnected via the new-generation high-performance Mellanox HDR 200G InfiniBand network, enabling the Lab to accelerate applications and increase scaling and efficiencies. The diverse mixture of computing technologies will allow LLNL and Corona partners to explore new approaches to cognitive simulation – blending machine learning and HPC – and intelligence-based data analytics.

    “HDR 200G InfiniBand brings a new level of performance and scalability needed to build the next generation of high-performance computing and artificial intelligence system,” said Gilad Shainer, vice president of marketing at Mellanox Technologies. “The collaboration between Penguin, AMD and LLNL results in a technology-leading platform that will progress science and discovery at the Lab.”

    See the full article here .


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    LLNL Campus

    Operated by Lawrence Livermore National Security, LLC, for the Department of Energy’s National Nuclear Security Administration
    Lawrence Livermore National Laboratory (LLNL) is an American federal research facility in Livermore, California, United States, founded by the University of California, Berkeley in 1952. A Federally Funded Research and Development Center (FFRDC), it is primarily funded by the U.S. Department of Energy (DOE) and managed and operated by Lawrence Livermore National Security, LLC (LLNS), a partnership of the University of California, Bechtel, BWX Technologies, AECOM, and Battelle Memorial Institute in affiliation with the Texas A&M University System. In 2012, the laboratory had the synthetic chemical element livermorium named after it.

    LLNL is self-described as “a premier research and development institution for science and technology applied to national security.” Its principal responsibility is ensuring the safety, security and reliability of the nation’s nuclear weapons through the application of advanced science, engineering and technology. The Laboratory also applies its special expertise and multidisciplinary capabilities to preventing the proliferation and use of weapons of mass destruction, bolstering homeland security and solving other nationally important problems, including energy and environmental security, basic science and economic competitiveness.

    The Laboratory is located on a one-square-mile (2.6 km2) site at the eastern edge of Livermore. It also operates a 7,000 acres (28 km2) remote experimental test site, called Site 300, situated about 15 miles (24 km) southeast of the main lab site. LLNL has an annual budget of about $1.5 billion and a staff of roughly 5,800 employees.

    LLNL was established in 1952 as the University of California Radiation Laboratory at Livermore, an offshoot of the existing UC Radiation Laboratory at Berkeley. It was intended to spur innovation and provide competition to the nuclear weapon design laboratory at Los Alamos in New Mexico, home of the Manhattan Project that developed the first atomic weapons. Edward Teller and Ernest Lawrence,[2] director of the Radiation Laboratory at Berkeley, are regarded as the co-founders of the Livermore facility.

    The new laboratory was sited at a former naval air station of World War II. It was already home to several UC Radiation Laboratory projects that were too large for its location in the Berkeley Hills above the UC campus, including one of the first experiments in the magnetic approach to confined thermonuclear reactions (i.e. fusion). About half an hour southeast of Berkeley, the Livermore site provided much greater security for classified projects than an urban university campus.

    Lawrence tapped 32-year-old Herbert York, a former graduate student of his, to run Livermore. Under York, the Lab had four main programs: Project Sherwood (the magnetic-fusion program), Project Whitney (the weapons-design program), diagnostic weapon experiments (both for the Los Alamos and Livermore laboratories), and a basic physics program. York and the new lab embraced the Lawrence “big science” approach, tackling challenging projects with physicists, chemists, engineers, and computational scientists working together in multidisciplinary teams. Lawrence died in August 1958 and shortly after, the university’s board of regents named both laboratories for him, as the Lawrence Radiation Laboratory.

    Historically, the Berkeley and Livermore laboratories have had very close relationships on research projects, business operations, and staff. The Livermore Lab was established initially as a branch of the Berkeley laboratory. The Livermore lab was not officially severed administratively from the Berkeley lab until 1971. To this day, in official planning documents and records, Lawrence Berkeley National Laboratory is designated as Site 100, Lawrence Livermore National Lab as Site 200, and LLNL’s remote test location as Site 300.[3]

    The laboratory was renamed Lawrence Livermore Laboratory (LLL) in 1971. On October 1, 2007 LLNS assumed management of LLNL from the University of California, which had exclusively managed and operated the Laboratory since its inception 55 years before. The laboratory was honored in 2012 by having the synthetic chemical element livermorium named after it. The LLNS takeover of the laboratory has been controversial. In May 2013, an Alameda County jury awarded over $2.7 million to five former laboratory employees who were among 430 employees LLNS laid off during 2008.[4] The jury found that LLNS breached a contractual obligation to terminate the employees only for “reasonable cause.”[5] The five plaintiffs also have pending age discrimination claims against LLNS, which will be heard by a different jury in a separate trial.[6] There are 125 co-plaintiffs awaiting trial on similar claims against LLNS.[7] The May 2008 layoff was the first layoff at the laboratory in nearly 40 years.[6]

    On March 14, 2011, the City of Livermore officially expanded the city’s boundaries to annex LLNL and move it within the city limits. The unanimous vote by the Livermore city council expanded Livermore’s southeastern boundaries to cover 15 land parcels covering 1,057 acres (4.28 km2) that comprise the LLNL site. The site was formerly an unincorporated area of Alameda County. The LLNL campus continues to be owned by the federal government.

    LLNL/NIF


    DOE Seal
    NNSA

     
  • richardmitnick 2:53 pm on October 25, 2018 Permalink | Reply
    Tags: , Supercomputing, Three Chinese teams join race to build the world’s fastest supercomputer   

    From Science: “Three Chinese teams join race to build the world’s fastest supercomputer” 

    AAAS
    From Science

    1
    Tianhe-1A was the world’s fastest computer in 2010. Its successor is being developed in the same building.
    VCG/GETTY IMAGES

    Oct. 24, 2018
    Dennis Normile

    TIANJIN, CHINA—In a cavernous room just off the marble floored lobby of China’s National Supercomputer Center of Tianjin stand more than 100 wardrobe-size black and gray metal cabinets, arranged in ranks like a marching army. They contain the Tianhe-1A supercomputer, which 8 years ago became the first Chinese machine to reign, briefly, as the world’s fastest computer, running at 2.57 petaflops (or quadrillion floating point operations per second). But just upstairs from Tianhe-1A—and off-limits to visitors—is a small prototype machine that, if successfully scaled up, could push China to the top of the rankings again. The goal is a supercomputer capable of 1 exaflop—1000 petaflops, five times faster than the current champion, the Summit supercomputer at Oak Ridge National Laboratory in Tennessee.

    ORNL IBM AC922 SUMMIT supercomputer. Credit: Carlos Jones, Oak Ridge National Laboratory/U.S. Dept. of Energy

    China is vying with the United States, Europe, and Japan to plant its flag in this rarefied realm, which will boost climate and weather modeling, human genetics studies, drug development, artificial intelligence, and other scientific uses. But its strategy is unique. Three teams are competing to build China’s machine; the Tianjin prototype has rivals at the National Supercomputing Center in Jinan and at Dawning Information Industry Co., a supercomputer manufacturer in Beijing. The Ministry of Science and Technology (MOST) will probably select two for expansion to exascale by the end of the year. The approach is a chance to spur innovation, says Bob Sorensen, a high-performance computing analyst at Hyperion Research in St. Paul. It “encourages vendors to experiment with a wide range of designs to distinguish themselves from their competitors,” he says.

    China may not be first to reach this computing milestone. Japan’s Post-K exascale computer could be running in 2020.

    Post-K exascale computer Japan

    The United States is aiming to deploy its first exascale system at Argonne National Laboratory in Lemont, Illinois, in 2021.

    Depiction of ANL ALCF Cray Shasta Aurora exascale supercomputer

    The European Union is ramping up its own program. China is aiming for 2020, but the date may slip.

    See the full article here .

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