Quantum precision reached in modeling molten salt behavior
FREDA is a new tool being developed at ORNL that will accelerate the design and testing of next-generation fusion devices. It is the first tool of its kind to combine plasma and engineering modeling capabilities and utilize high performance computing resources.
The Department of Energy’s Oak Ridge National Laboratory had a major presence at this year’s International Conference for High Performance Computing, Networking, Storage, and Analysis (SC24).
Joel Brogan, who leads the Multimodal Sensor Analytics group at Oak Ridge National Laboratory, has been elevated to senior membership in the Institute of Electrical and Electronics Engineers.
A paper written by researchers from the Department of Energy’s Oak Ridge National Laboratory was selected as the top paper of 2023 by Welding Journal that explored the feasibility of using laser-blown powder direct energy deposition, or Laser-powder DED.
ORNL has been recognized in the 21st edition of the HPCwire Readers’ and Editors’ Choice Awards, presented at the 2024 International Conference for High Performance Computing, Networking, Storage and Analysis in Atlanta, Georgia.
Two-and-a-half years after breaking the exascale barrier, the Frontier supercomputer at the Department of Energy’s Oak Ridge National Laboratory continues to set new standards for its computing speed and performance.
Researchers used the world’s fastest supercomputer, Frontier, to train an AI model that designs proteins, with applications in fields like vaccines, cancer treatments, and environmental bioremediation. The study earned a finalist nomination for the Gordon Bell Prize, recognizing innovation in high-performance computing for science.
Researchers at Oak Ridge National Laboratory used the Frontier supercomputer to train the world’s largest AI model for weather prediction, paving the way for hyperlocal, ultra-accurate forecasts. This achievement earned them a finalist nomination for the prestigious Gordon Bell Prize for Climate Modeling.
A research team led by the University of Maryland has been nominated for the Association for Computing Machinery’s Gordon Bell Prize. The team is being recognized for developing a scalable, distributed training framework called AxoNN, which leverages GPUs to rapidly train large language models.
Aditya Sundararajan of ORNL’s Grid Systems Architecture group was elevated to senior status within the Institute of Electrical and Electronics Engineers.