Maintaining the Nation’s Power Grid with Exascale Computing
The ExaSGD project uses models and data analytics to understand power grid failures and look for ways to prevent them.
The ExaSGD project uses models and data analytics to understand power grid failures and look for ways to prevent them.
The ExaSGD project wants to enable the day-to-day operation of the national power grid to hold a large number of renewable power sources.
ExaGraph aims to leave a lasting legacy of algorithms, implementations, and graph-enabled applications for scientific discovery.
An article by Pacific Northwest National Laboratories on Technology.org shares how machine learning algorithms, the basis of neural networks, are bringing new scientific discoveries closer to reality.
Machine learning, artificial intelligence, and data analytics are converging with high-performance computing to advance scientific discovery.
A collaborative team is working to get NWChem ready to run on exascale machines and to provide a starting point for future code development.
New ExaLearn Co-Design Center to be led by Brookhaven National Laboratory's Francis (Frank) Alexander. The Exascale Computing Project has initiated its sixth Co-Design Center, ExaLearn, to be led by Principal
Paul Messina, director of the Exascale Computing Project, on August 9 delivered the keynote presentation at the Workshop on Modeling & Simulation of Systems, ModSim 2017, in Seattle, Washington.