Highlights include power grid challenges, performance analysis, complex boundary conditions, and a novel multiscale modeling approach.
Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.
Proxy apps serve as specific targets for testing and simulation without the time, effort, and expertise that porting or changing most production codes would require.
The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.
LLNL heads to the SIAM Conference on Computational Science and Engineering (CSE19) in Spokane, Washington, on February 25 to March 1, 2019.
High-resolution finite volume methods are being developed for solving problems in complex phase space geometries, motivated by kinetic models of fusion plasmas.
LLNL researchers are testing and enhancing a neutral particle transport code and the algorithm on which the code relies to ensure that they successfully scale to larger and more complex computing systems.