Articles tagged with: Titan
A team led by ORNL’s Amit Shyam and Dongwon Shin is using Titan to explore the possibilities of designing various high-temperature–capable alloys, in hopes of changing the paradigm for current alloy design and significantly shortening the typical alloy development and deployment process.
OLCF scientific computing liaison Gustav Jansen received honorable mention for the 2017 Hermann Kümmel Early Achievement Award in Many-Body Physics. The award recognizes “young physicists whose published work is a significant contribution to quantum many-body theory.”
A team of computational scientists from the University of Illinois at Urbana-Champaign used the Titan supercomputer to model one of life’s ubiquitous molecular motors.
The OLCF played a major role in the annual American Physical Society March Meeting—the largest gathering of physicists in the world—by bringing high-performance computing talks to the meeting as part of a petascale computing focus session.
Every month the OLCF hosts webinar-based conference calls to provide system users with information about best practices, new tools, and how-to guides.
Multi-institution research team uses supercomputing to understand processes leading to increased drought resistance in food and fuel crops.
Using advanced modeling and simulation, seismic data generated by earthquakes, and one of the world’s fastest supercomputers, a team led by Jeroen Tromp of Princeton University is creating a detailed 3-D picture of Earth’s interior.
In 2016, the OLCF introduced a new runtime framework that allows users of hybrid systems—such as the OLCF’s 27-petaflop Titan—to better exploit GPU-accelerated architectures.
University of Virginia professor Leonid Zhigilei led a team that used the OLCF’s Titan supercomputer to gain deeper insights into laser interactions with metal surfaces.
As part of her team’s research into matter’s tendency to self-organize, Sharon Glotzer of the University of Michigan ran a series of hard particle simulations to study melting in two-dimensional (2-D) systems.