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DOE to Provide $57.5 Million for Multidisciplinary Science Computing Teams to Advance AI and Machine Learning

DOE to Provide $57.5 Million for Multidisciplinary Science Computing Teams to Advance AI and Machine Learning

The U.S. Department of Energy (DOE) will provide $57.5 million over five years to establish two multidisciplinary teams led by national laboratories to develop new tools and techniques to harness supercomputing for scientific discovery, specifically in relation to artificial intelligence (AI) and machine learning. “The need for such multi-disciplinary teams is growing as the world moves into the age of Exascale computing, with advanced systems embodying entirely novel architectures coming online.” Argonne National Laboratory and Lawrence Berkeley National Laboratory will lead the teams comprised of computer scientists, software developers, mathematicians, and other experts.
According to the department, the teams will provide expertise and develop tools to enable scientists to take full advantage of DOE’s high-performance computing capabilities. Under Secretary for Science Paul Dabbar comments that, ““The Department of Energy is home to some of the world’s fastest supercomputers, and as we move into the Exascale computing era, these resources are continuing to rapidly advance in performance, architecture, and design. These Argonne and Lawrence Berkeley-led teams are comprised of experts in computing and applied mathematics and will ensure that the American scientific community can fully harness our country’s leading capabilities in high performance computing.”
The teams will be known as “SciDAC Institutes” and will be part of the SciDAC, or Scientific Discovery through Advanced Computing, program. “SciDAC addresses problems in disciplines including high energy and nuclear physics, condensed matter physics, materials science, chemistry, fusion energy sciences, Earth systems research, and nuclear energy.” The teams will have access to all DOE supercomputing facilities, including those at Argonne, Oak Ridge, and Lawrence Berkeley.
The two teams are:
  • RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence (the Argonne-led team); focuses on community outreach to support scientists with application development.
  • Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) Institute (the Lawrence Berkeley-led team); focuses on the development of new mathematical techniques.
A list of lead and partner institutions can be found on the DOE’s webpage.

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