Implementation in the APES Framework

In distributed computations it is important to avoid any kind of bottleneck, as the local computing resources in each computing unit are usually quite limited in comparison to the total computing power. For large scale computations that aim to use the full system for their simulation, it therefore is essential that the complete processing chain is distributable. Otherwise any step from the mesh generation over the simulation to the post-processing might prohibit a successful simulation. The TreElM library builds the basis for a completely integrated framework that allows the distributed processing of all necessary simulation steps. To enable a flexible and convenient user configuration, the Aotus library (aotus_module) is not only used in TreElM header files, but also exposed to the calling applications. The layout of the overall framework with the central TreElM library is shown by the schematic figure in the Motivation.

The solvers read the mesh data, using TreElM functions and process the mesh into a solver specific data structure with attached simulation data. Results are written in the form of restart files, that follow the elemental design of the TreElM mesh by writing the elemental solution following the same ordering to disk. With the help of tracking objects it is possible to write subsets of the mesh with attached simulation data in arbitrary intervals to disk. The TreElM library not only provides the means to identify the halos to be exchanged between partitions but also provides various communication pattern that can be used for the actual exchange. Due to this encapsulation of the communication, the communication layout can be easily exchanged. It is even possible to replace the complete parallel paradigm in the deployment with only few changes to the code.

Aside from these basic functionalities the library provides several auxiliary routines, that provide for example logging and debugging facilities. Finally the post processing tool Harvester is used to visualize the results of the simulation. Since Harvester is a stand alone tool, it can be deployed on specialized machines, that are more suited for visualization. Additionally, the separated design ensures the independence of the solvers from any third party libraries, that are typically required for visualization outputs.