With the advent of exascale computing, effective load balancing in massively parallel software applications is critically important for leveraging the full potential of high performance computing systems. Load balancing is the distribution of computational work between available processors. Here, we investigate the application of quantum annealing to load balance two paradigmatic algorithms in high performance computing. Namely, adaptive mesh refinement and smoothed particle hydrodynamics are chosen as representative grid and off-grid target applications. While the methodology for obtaining real simulation data to partition 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 advantage over more advanced methods such as steepest descent or simulated annealing despite remaining competitive. The primary obstacle to scalability is found to be limited coupling on current quantum annealing hardware. However, for the more complex particle formulation, approached as a multi-objective optimization, quantum annealing solutions are demonstrably Pareto dominant to state of the art classical methods across both objectives. This signals a noteworthy advancement in solution quality which can have a large impact on effective CPU usage.

翻译：随着百亿亿次计算时代的到来，大规模并行软件应用中有效的负载均衡对于充分发挥高性能计算系统的潜力至关重要。负载均衡是将计算任务分配给可用处理器的过程。本文研究了量子退火在高性能计算中两类典型算法负载均衡中的应用，具体选择自适应网格细化和光滑粒子流体动力学作为基于网格和非网格的代表性目标应用。尽管获取用于划分的真实模拟数据的方法因具体应用而异，但所提出的均衡协议本身完全具有普适性。对于基于网格的场景，量子退火在性能上优于轮询协议等传统方法，但与最速下降法或模拟退火等更先进的方法相比，虽然仍具竞争力，却缺乏决定性优势。研究发现，当前量子退火硬件的有限耦合能力是阻碍可扩展性的主要障碍。然而，对于采用多目标优化处理的更复杂的粒子形式，量子退火方案在两个优化目标上均明显帕累托优于最先进的经典方法。这标志着解质量取得了显著进步，将有效提升CPU利用率。