ECP Launches ExaLearn Co-Design Center

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The DOE’s Exascale Computing Project has initiated a new Co-Design Center called ExaLearn. Led by Principal Investigator Francis J. Alexander from Brookhaven National Lab, ExaLearn is a co-design center for Exascale Machine Learning (ML) Technologies.

Our multi-laboratory team is very excited to have the opportunity to tackle some of the most important challenges in machine learning at the exascale,” Alexander said. “There is, of course, already a considerable investment by the private sector in machine learning. However, there is still much more to be done in order to enable advances in very important scientific and national security work we do at the Department of Energy. I am very happy to lead this effort on behalf of our collaborative team.”

The ExaLearn effort is part of a collaboration initially consisting of experts from eight multipurpose DOE labs:

  • Brookhaven National Laboratory (Francis J. Alexander)
  • Argonne National Laboratory (Ian Foster)
  • Lawrence Berkeley National Laboratory (Peter Nugent)
  • Lawrence Livermore National Laboratory (Brian van Essen)
  • Los Alamos National Laboratory (Aric Hagberg)
  • Oak Ridge National Laboratory (David Womble)
  • Pacific Northwest National Laboratory (James Ang)
  • Sandia National Laboratories (Michael Wolf)

Rapid growth in the amount of data and computational power is driving a revolution in machine learning (ML) and artificial intelligence (AI). Beyond the highly visible successes in machine-based natural language translation, these new ML technologies have profound implications for computational and experimental science and engineering and the exascale computing systems that DOE is deploying to support those disciplines.

To address these challenges, the ExaLearn co-design center will provide exascale ML software for use by ECP Applications projects, other ECP Co-Design Centers and DOE experimental facilities and leadership class computing facilities. The ExaLearn Co-Design Center will also collaborate with ECP PathForward vendors on the development of exascale ML software.

The timeliness of ExaLearn’s proposed work ties into the critical national need to enhance economic development through science and technology. It is increasingly clear that advances in learning technologies have profound societal implications and that continued U.S. economic leadership requires a focused effort, both to increase the performance of those technologies and to expand their applications. Linking exascale computing and learning technologies represents a timely opportunity to address those goals.

The practical end product will be a scalable and sustainable ML software framework that allows application scientists and the applied mathematics and computer science communities to engage in co-design for learning. The new knowledge and services to be provided by ExaLearn are imperative for the nation to remain competitive in computational science and engineering by making effective use of future exascale systems.

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