Balancing conflicting requirements for grid and particle decomposition in continuum-Lagrangian solvers

• Load balancing strategies for particle/grid interactions are studied in this work
• Particle spatial decomposition strategy is compared with equal sharing among cores
• Strong scaling study is performed to 32,000 compute cores
• Particle sharing strategy needs reimplementation of MPI collectives for performance
• An equalization method with particle sharing improves data locality and load balance

Load balancing strategies for hybrid solvers that involve grid based partial differential equation solution coupled with particle tracking are presented in this paper. A typical Message Passing Interface (MPI) based parallelization of grid based solves are done using a spatial domain decomposition while particle tracking is primarily done using either of the two techniques. One of the techniques is to distribute the particles to MPI ranks to whose grid they belong to while the other is to share the particles equally among all ranks, irrespective of their spatial location. The former technique provides spatial locality for field interpolation but cannot assure load balance in terms of number of particles, which is achieved by the latter. The two techniques are compared for a case of particle tracking in a homogeneous isotropic turbulence box as well as a turbulent jet case. A strong scaling study is performed to more than 32,000 cores, which results in particle densities representative of anticipated exascale machines. The use of alternative implementations of MPI collectives and efficient load equalization strategies are studied to reduce data communication overheads.