  Modern supercomputers allow the simulation of complex phenomena with
increased accuracy. Eventually, this requires finer geometric discretizations
with larger numbers of mesh elements. In this context, and extrapolating to the
Exascale paradigm, meshing operations such as generation, adaptation or
partition, become a critical bottleneck within the simulation workflow. In this
paper, we focus on mesh partitioning. In particular, we present some
improvements carried out on an in-house parallel mesh partitioner based on the
Hilbert Space-Filling Curve.
  Additionally, taking advantage of its performance, we present the application
of the SFC-based partitioning for dynamic load balancing. This method is based
on the direct monitoring of the imbalance at runtime and the subsequent
re-partitioning of the mesh. The target weights for the optimized partitions
are evaluated using a least-squares approximation considering all measurements
from previous iterations. In this way, the final partition corresponds to the
average performance of the computing devices engaged.
