Presentation
Leveraging Hybrid Classical-Quantum Methods for Efficient Load Rebalancing in HPC
DescriptionLoad balancing (LB) is a challenge for parallel applications in High Performance Computing (HPC). Depending on various constraints, LB is an optimization problem. This paper focuses on the context of a given task distribution in distributed memory systems, where load imbalance might happen at runtime due to a weak performance model. In this imbalance context, LB refers to the Load Rebalancing Problem (LRP). Tasks should be migrated from one machine to another to improve the load. Our paper presents a formulation of LRP to be solved in a hybrid classical-quantum approach. We compare the quantum-based methods with the classical methods using heuristic algorithms. The experiments revolve around imbalance ratio and speedup based on the results of the applied methods, where the number of migrated tasks is a concern because task migration overhead is expensive. The quantum-based methods show positive performance gain even better than classical methods.
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