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Researchers from ORNL have developed a new application to increase efficiency in memory systems for high performance computing. Rather than allow data to bog down traditional memory systems in supercomputers and impact performance, the team from ORNL, along with researchers from the University of Tennessee, Knoxville, created a framework to manage data more efficiently with memory systems that employ more complex structures. 

This is a simulated image of the project to build a new network that artificial intelligence and machine learning to steer experiments and analyze data faster and more accurately. will enable

To bridge the gap between experimental facilities and supercomputers, experts from SLAC National Accelerator Laboratory are teaming up with other DOE national laboratories to build a new data streaming pipeline. The pipeline will allow researchers to send their data to the nation’s leading computing centers for analysis in real time even as their experiments are taking place. 

Man in blue shirt and grey pants holds laptop and poses next to a green plant in a lab.

John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.

Architects of the Adaptable IO System, seen here with Frontier's Orion file system: Scott Klasky, left, heads the ADIOS project and leads ORNL's Workflow Systems group, and Norbert Podhorszki, an ORNL computer scientist, oversees ADIOS's continuing development. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Integral to the functionality of ORNL's Frontier supercomputer is its ability to store the vast amounts of data it produces onto its file system, Orion. But even more important to the computational scientists running simulations on Frontier is their capability to quickly write and read to Orion along with effectively analyzing all that data. And that’s where ADIOS comes in.

Alex May, pictured above, is the first and only full-time data curator at the Department of Energy’s Oak Ridge Leadership Computing Facility. Credit: Carlos Jones and Wikimedia Commons, background/ORNL, U.S. Dept. of Energy
Alex May is the first and only full-time data curator at the Department of Energy’s Oak Ridge Leadership Computing Facility, evaluating datasets developed by computational scientists before they are made public through the OLCF’s Constellation portal for open data exchange.
CFM’s RISE open fan engine architecture. Image: GE Aerospace

To support the development of a revolutionary new open fan engine architecture for the future of flight, GE Aerospace has run simulations using the world’s fastest supercomputer capable of crunching data in excess of exascale speed, or more than a quintillion calculations per second.

A simulation of the planet from the DOE Energy Exascale Earth System Model, one of the large-scale models incorporated in the Earth System Grid Federation led by DOE’s Oak Ridge, Argonne and Lawrence Livermore national laboratories. Credit: LLNL, U.S. Dept. of Energy

The Earth System Grid Federation, a multi-agency initiative that gathers and distributes data for top-tier projections of the Earth’s climate, is preparing a series of upgrades.

ORNL will use its land surface modeling tools to determine Baltimore’s climate risk and analyze green infrastructure improvements that can help mitigate impacts on underserved communities as part of a DOE Urban Integrated Field Laboratory project. Source: Google Earth, accessed Sept. 12, 2022

ORNL researchers are deploying their broad expertise in climate data and modeling to create science-based mitigation strategies for cities stressed by climate change as part of two U.S. Department of Energy Urban Integrated Field Laboratory projects.