AUTHOR(S):Carsten Burstedde, Omar Ghattas, Michael Gurnis, Tobin Isaac, Georg Stadler, Tim Warburton, Lucas Wilcox
ABSTRACT: Many problems are characterized by dynamics occurring on a wide range of length and time scales. One approach to overcoming the tyranny of scales is adaptive mesh refinement, which dynamically adapts the mesh to resolve features of interest. However, the benefits of AMR are difficult to achieve in practice, particularly on the petascale computers that are essential for difficult problems. Due to the complex dynamic data structures and frequent load balancing, scaling dynamic AMR to hundreds of thousands of cores has long been considered a challenge. Another difficulty is extending parallel AMR techniques to high-order-accurate, complex-geometry-respecting methods that are favored for many classes of problems.
We present new parallel algorithms and data structures for dynamic mesh refinement/coarsening on forest-of-octree-based geometries with arbitrarily high order continuous and discontinuous finite/spectral element discretizations. Our AMR algorithms exhibit excellent weak and strong scaling to over 224,000 Cray XT5 cores for several multiscale geophysics problems.
Michael Norman (Chair) - University of California, San Diego
Carsten Burstedde - University of Texas at Austin
Omar Ghattas - University of Texas at Austin
Michael Gurnis - California Institute of Technology