: Increasing the mesh resolution is one the most important tools for
increasing the accuracy of numerical simulations. However, increasing the mesh
resolution globally increases the amount of data and computational time
substantially. In the exascale-era sub-km meshes are becoming more and more
popular for atmospheric models, making it especially difficult to manage the vast
amount of data efficiently. With dynamic adaptive mesh refinement (AMR) we
locally control the resolution of a mesh in areas of interest, using a fine resolution
only where it is explicitely needed and keeping the mesh coarse elsewhere. Thus,
we concentrate the data and computing power and significantly reduce the
simulation costs while keeping the same numerical accuracy. Vice versa, the
resolution can be increased while keeping the same runtime. Managing adaptive
meshes induces new challenges such as load-balancing, mesh management,
ghost layer computation etc. Developments in the recent years have extended the
scalable and efficicient tree-based AMR approach from quadrilaterals/hexahedra
to various element shapes such as triangles, tetrahedra, pyramids or prisms and
have been implemented in our AMR library t8code. It is a third-party library that
adresses these challenges and can be integrated by solver environments in order
to enable AMR. In our presentation we will give an introduction to AMR and how
we use it for atmospheric simulations. We will give an overview of ongoing and
past projects, such as our contributions to PilotLab Exascale Earth Sytem
Modelling (Pl-ExaESM) or the lossy data compression for data coming from
atmospheric simulations. Furthermore, we will demonstrate the efficiency of our
methods with recent benchmark results on current supercomputers, showing that
t8code scales on up to at least 1 million MPI ranks and over 1 Trillion mesh
elements
