Load Balancing For High Performance Computing Using Quantum Annealing

Abstract With the advent of exascale computing, effective load balancing in massively parallel software applications is critically importantfor leveraging the full potential of high performance computing systems. Load balancing is the distribution of computational workbetween available processors. Here, we investigate the application of quantum annealing to load balance two paradigmaticalgorithms in high performance computing. Namely, adaptive mesh refinement and smoothed particle hydrodynamics arechosen as representative grid and off-grid target applications. While the methodology for obtaining real simulation data topartition is application specific, the proposed balancing protocol itself remains completely general. In a grid based context,quantum annealing is found to outperform classical methods such as the round robin protocol but lacks a decisive advantageover more advanced methods such as steepest descent or simulated annealing despite remaining competitive. The primaryobstacle to scalability is found to be limited coupling on current quantum annealing hardware. However, for the more complexparticle formulation, approached as a multi-objective optimization, quantum annealing solutions are demonstrably Paretodominant to state of the art classical methods across both objectives. This signals a noteworthy advancement in solution qualitywhich can have a large impact on effective CPU usage.