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Air Force, ORNL partner in high-performance computing & weather modeling system

Posted at 12:50 pm September 5, 2018
By Oak Ridge Today Staff Leave a Comment

To extend its weather modeling capabilities, the U.S. Air Force has joined the computing experts at the U.S. Department of Energy’s Oak Ridge National Laboratory in a strategic collaboration that includes procurement and operation of a new high-performance weather modeling computer system. Key members of the Air Force and ORNL teams gathered on July 10, 2018, to kick off the project and tour the facilities supporting the new system. (Photo courtesy ORNL)

To extend its weather modeling capabilities, the U.S. Air Force has joined the computing experts at the U.S. Department of Energy’s Oak Ridge National Laboratory in a strategic collaboration that includes procurement and operation of a new high-performance weather modeling computer system. Key members of the Air Force and ORNL teams gathered on July 10, 2018, to kick off the project and tour the facilities supporting the new system. (Photo courtesy ORNL)

 

For the U.S. military, accurate weather prediction is vital to both the planning and execution of worldwide missions. To extend its weather modeling capabilities, the U.S. Air Force has joined the computing experts at the U.S. Department of Energy’s Oak Ridge National Laboratory in a strategic collaboration that includes procurement and operation of a new high-performance weather modeling computer system. Key members of the Air Force and ORNL teams, including computing and global security team members, gathered on July 10 to kick off this project and tour the facilities supporting the new system, according to a story posted by ORNL.

The Air Force 557th Weather Wing provides the Air Force and Army with global- and regional-level numerical weather model forecasts. With the increasing scale of the requirements for the new system, the Air Force and ORNL identified an opportunity to take advantage of the capabilities of ORNL’s National Center for Computational Sciences—including expertise in high-performance computing facilities and infrastructure, systems administration, computing procurement and acquisition, and system operations, the story said.

“We learned about the Air Force’s needs, and it was immediately clear that ORNL could help them solve their problems,” said Jim Rogers, NCCS director of computing and facilities. “We can integrate the Air Force weather team’s needs into our facilities in a cost-effective way, leveraging our capabilities to deliver exceedingly high availability to support their mission.” [Read more…]

Filed Under: Federal, Government, Oak Ridge National Laboratory, ORNL, Slider, U.S. Department of Energy, Weather Tagged With: Air Force 557th Weather Wing, Air Force Weather, DOE, Earth system modeling, GALWEM, Global Air-Land Weather Exploitation Model, high-performance computing, high-performance weather modeling, Jeff Nichols, Jim Rogers, Kate Evans, machine learning, National Center for Computational Sciences, Oak Ridge National Laboratory, ORNL, Ralph Stoffler, U.S. Air Force, U.S. Department of Energy, weather, weather model, weather modeling

ORNL names director of artificial intelligence

Posted at 9:23 am November 22, 2017
By John Huotari 1 Comment

David Womble

David Womble

 

Oak Ridge National Laboratory has hired David Womble, a leader in high-performance computing and former executive at Sandia National Laboratories, to direct its artificial intelligence work.

Womble began as AI Program Director on October 30, a press release said. AI stands for artificial intelligence.

Womble’s responsibilities include guiding ORNL’s AI and machine learning strategy for high-performance computing, ensuring broad scientific impacts to the missions of the U.S. Department of Energy’s Office of Science, and providing long-range program planning and project leadership, the press release said.

“In more than three decades in computing, Womble has won two R&D100 awards and the Association for Computing Machinery’s Gordon Bell Prize, awarded each year ‘to recognize outstanding achievement in high-performance computing,'” the press release said. [Read more…]

Filed Under: Front Page News, Oak Ridge National Laboratory, U.S. Department of Energy Tagged With: Advanced Computational Technologies Initiative, advanced simulation and computing, AI, artificial intelligence, Computer Science Research Institute, data analytics, David Womble, DOE, high-performance computing, machine learning, Oak Ridge National Laboratory, Office of Science, ORNL, Sandia National Laboratories, summit, Titan, U.S. Department of Energy

ORNL researchers turn to ‘deep learning’ to solve science’s big data problem

Posted at 2:56 pm September 3, 2017
By Oak Ridge Today Staff Leave a Comment

Scientists will use Oak Ridge National Laboratory’s computing resources such as the Titan supercomputer to develop deep learning solutions for data analysis. (Photo credit: Jason Richards/Oak Ridge National Laboratory, U.S. Department of Energy)

Scientists will use Oak Ridge National Laboratory’s computing resources such as the Titan supercomputer to develop deep learning solutions for data analysis. (Photo credit: Jason Richards/Oak Ridge National Laboratory, U.S. Department of Energy)

 

By Scott Jones, Oak Ridge National Laboratory

A team of researchers from Oak Ridge National Laboratory has been awarded nearly $2 million over three years from the U.S. Department of Energy to explore the potential of machine learning in revolutionizing scientific data analysis.

The Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond (ASCEND) project aims to use deep learning to assist researchers in making sense of massive datasets produced at the world’s most sophisticated scientific facilities. Deep learning is an area of machine learning that uses artificial neural networks to enable self-learning devices and platforms. The team, led by ORNL’s Thomas Potok, includes Robert Patton, Chris Symons, Steven Young, and Catherine Schuman.

While deep learning has long been used to classify relatively simple data such as photographs, today’s scientific data presents a much greater challenge because of its size and complexity. Deep learning offers the potential to truly change the way in which researchers use massive datasets to solve challenges spanning the scientific spectrum.

For example, neutron scattering data collected at ORNL’s Spallation Neutron Source contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help researchers better understand the link between experimental data and materials properties.

“This understanding can help scientists build and support new scientific theories, and help to design better materials,” Potok said. [Read more…]

Filed Under: Front Page News, Oak Ridge National Laboratory, Top Stories, U.S. Department of Energy Tagged With: A study of complex deep learning networks on high performance neuromorphic and quantum computers, Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond, artificial neural networks, ASCEND, Catherine Schuman, Chris Symons, deep learning, machine learning, massive datasets, Oak Ridge Leadership Computing Facility, Oak Ridge National Laboratory, Office of Science, ORNL, Proceedings of the Workshop on Machine Learning in High Performance Computing Environments, Robert Patton, Scott Jones, self-learning devices, Spallation Neutron Source, Steven Young, Thomas Potok, Titan supercomputer

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