Energy allocates $16M for high-performance machine learning research

Sixteen million dollars in federal investments have been allocated toward advancements in predictive modeling and simulation technologies, part of ongoing research efforts led by the Department of Energy to further advanced computing innovation in the U.S.

Announced on Thursday, the funding will be dispersed across a series of domestic projects focused on high-performance computing to advance new scientific concepts and developments, such as in materials sciences and atmospheric predictions.

“Basic research for scientific computing and machine learning continues to have a wide impact across a range of applications,” Ceren Susut, Energy’s acting associate director of science for Advanced Scientific Computing Research, said in a press release. “These projects are important for advancing our modeling, simulation and data analysis capabilities for scientific discovery and innovation.”

Seven individual projects will share the total funding award, with several of the winning projects based out of national laboratories: the Pacific Northwest National Laboratory in Washington, the Lawrence Berkeley National Laboratory in California and the Sandia National Laboratories in New Mexico.

Universities and colleges also received federal support — Johns Hopkins University, Spelman College and the trustees of the University of Pennsylvania — who will also work on leveraging supercomputation in experimentation. 

Supercomputing and exascale supercomputer development are among the current research priorities helmed by Energy, and this funding opportunity comes from the department’s Advanced Scientific Computing Research program, which focuses specifically on deploying predictive, modeling and simulation capabilities within supercomputing systems.