Larger supercomputers allow the simulation of more complex phenomena with increased accuracy. Eventually this requires finer and thus also larger geometric discretizations. In this context, and extrapolating to the Exascale paradigm, meshing operations such as generation, deformation, adaptation/regeneration or partition/load balance, become a critical issue within the simulation workflow. In this paper we focus on mesh partitioning. In particular, we present a fast and scalable geometric partitioner based on Space Filling Curves (SFC), as an alternative to the standard graph partitioning approach. We have avoided any computing or memory bottleneck in the algorithm, while we have imposed that the solution achieved is independent (discounting rounding off errors) of the number of parallel processes used to compute it. The performance of the SFC-based partitioner presented has been demonstrated using up to 4096 CPU-cores in the Blue Waters supercomputer.

The research leading to these results has received funding from the European Union Horizon 2020 Programme (2014-2020) and the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (grant agreement No. 689772). This work is also part of the PRAC "Simulations of Aircraft Engines" supported by the National Science Foundation. It has also been financially supported by the PRACE preparatory access projects funded in part by the EU Horizon 2020 research and innovation programme (2014-2020) under grant agreement 653838. J.C. Cajas acknowledges the nancial sup- port of the `Consejo Nacional de Ciencia y Tecnolog a (CONACyT, M exico)' grant number 231588 290790. Ricard Borrell and Daniel Mira acknowledge the Juan de la Cierva postdoctoral grants with codes IJCI-2014-21034 and IJCI-2015-26686, respectively.