Abstract
This paper presents a scalable dynamic load balancing scheme for a parallel front-tracking method based multiphase flow simulation. In this simulation employing both Lagrangian and Eulerian grids, processes operating on Lagrangian grid are susceptible to load imbalance due to moving Lagrangian grid points (bubbles) and load distribution based on spatial location of bubbles. To load balance these processes, we distribute load keeping in view both current processor load distribution and bubble spatial locality and remap interprocess communication. The result is a uniform processor load distribution and predictable and less expensive communication scheme. Scalability studies on the Hazel Hen supercomputer demonstrate excellent scaling with exponential savings in execution time as the problem size becomes increasingly large. While moderate speedup is observed for strong scaling, speedup of up to 30% is achieved over nonload-balanced version when simulating 13824 bubbles on 4096 cores for weak scaling studies.
Highlights
Multiphase flow in fluid mechanics refers to the simultaneous flow of a mixture of materials with different chemical properties or different phases
We developed a load balancing strategy for a front-tracking method based parallel multiphase flow simulation
Instead of fixed load assignment of movable Lagrangian grid to processors, we perform dynamic and adaptive load balancing resulting in uniform load distribution
Summary
Multiphase flow in fluid mechanics refers to the simultaneous flow of a mixture of materials with different chemical properties or different phases (e.g., solid, liquid, or gas). One of the parallel implementations of multiphase flow based on front-tracking method is presented in [11] Authors parallelize both Eulerian and Lagrangian grid computations with Lagrangian grid computations performed by a separate group of processes. This work is built upon the implementation discussed in [11] with an emphasis on load balancing and devises a more predictable communication scheme for subfront processes.
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