Argonne Presents the Century of the City Moderated by Argonne’s Charlie Catlett, this SC17 plenary panel will discuss emerging needs and opportunities suggesting an increasing role for HPC in cities, with perspectives from city government, planning and design, and embedded urban HPC systems. Watch the video: https://www.youtube.com/watch?v=H7hYDwgc6WQ More info: https://goo.gl/FoSrpC
Star PowerScience Visualizations
The Rate of Spontaneous Plasma ReconnectionScience Visualizations
Petascale Simulation of Magnetorotational Core-Collapse SupernovaeScience Visualizations
Multiphase Simulations of Nuclear Reactor FlowsScience Visualizations
Understanding the Role of Ice Shelf-Ocean Interactions in a Changing ClimateScience Visualizations
Plastic NeocortexScience Visualizations
Earth’s Magnetic FieldsScience Visualizations
Tuesday, November 14 • 10:30AM – 11:15AM
LOCATION: Mile High Ballroom
In 2016, the U.S. Department of Energy established the Exascale Computing Project (ECP) – a joint project of the DOE Office of Science (DOE-SC) and the DOE National Nuclear Security Administration (NNSA) – that will result in a broadly usable exascale ecosystem and prepare mission critical applications to take advantage of that ecosystem.
Tuesday, November 14 • 10:30AM – 11:00AM
The U.S. Department of Energy (DOE) formed a partnership with the National Cancer Institute (NCI) to jointly develop advanced computing solutions for cancer by bringing together researchers from four DOE laboratories (Argonne, Los Alamos, Livermore, and Oak Ridge) with the Frederick National Laboratory for Cancer Research (FNLCR). This integrated team has launched three pilot projects, each addressing a major challenge problem on the forefront of precision oncology.
Wednesday, November 15 • 1:45PM – 2:30PM
LOCATION: DOE Exhibit Booth (#613)
Argonne Distinguished Fellow Ian Foster will present a talk “Going Smart and Deep on Materials at ALCF.” The presentation will include a demonstration of how researchers can use large collections of materials science data to build machine learning models that can guide simulations, improve their accuracy and reduce their cost, and launch new computations on ALCF resources.