A Runtime-Based Dynamic Mesh-Partitioning Approach

Large-scale parallel numerical simulations are fundamental for the understanding of a wide variety of aeronautical problems. Mesh decomposition is applied to make use of parallel hardware. In particular, when using a massively parallel architecture, not only the final quality of the mesh subdivision is relevant. Also the partitioning algorithm itself needs to be robust as well as efficient. A strategy for dynamic mesh partitioning based on runtime measurements is presented. We integrate the "Geometric Mesh Partitioner" (GeMPa), which is a partitioning library based on Hilbert Space-Filling Curve (HSFC), in the FlowSimulator (FS) software. FS is a platform designed to run multi-disciplinary simulations on massively parallel cluster architectures. The algorithm performance is evaluated on an unstructured mesh representing the ONERA M6 wing. In particular, the load imbalance among processes is evaluated and compared with a well-known graph-based partitioning approach. Finally, we analyze how the number of processes influences the load imbalance

A Runtime-Based Dynamic Mesh-Partitioning Approach

Large-scale parallel numerical simulations are fundamental for the understanding of a wide variety of aeronautical problems. Mesh decomposition is applied to make use of parallel hardware. In particular, when using a massively parallel architecture, not only the final quality of the mesh subdivision is relevant. Also the partitioning algorithm itself needs to be robust as well as efficient. A strategy for dynamic mesh partitioning based on runtime measurements is presented. We integrate the "Geometric Mesh Partitioner" (GeMPa), which is a partitioning library based on Hilbert Space-Filling Curve (HSFC), in the FlowSimulator (FS) software. FS is a platform designed to run multi-disciplinary simulations on massively parallel cluster architectures. The algorithm performance is evaluated on an unstructured mesh representing the ONERA M6 wing. In particular, the load imbalance among processes is evaluated and compared with a well-known graph-based partitioning approach. Finally, we analyze how the number of processes influences the load imbalance