Optimizing resource utilization in high-performance computing (HPC) clusters is essential for maximizing both system efficiency and user satisfaction. However, traditional rigid job scheduling often results in underutilized resources and increased job waiting times. This work evaluates the benefits of resource elasticity, where the job scheduler dynamically adjusts the resource allocation of malleable jobs at runtime. Using real workload traces from the Cori, Eagle, and Theta supercomputers, we simulate varying proportions (0-100%) of malleable jobs with the ElastiSim software. We evaluate five job scheduling strategies, including a novel one that maintains malleable jobs at their preferred resource allocation when possible. Results show that, compared to fully rigid workloads, malleable jobs yield significant improvements across all key metrics. Considering the best-performing scheduling strategy for each supercomputer, job turnaround times decrease by 37-67%, job makespan by 16-65%, job wait times by 73-99%, and node utilization improves by 5-52%. Although improvements vary, gains remain substantial even at 20% malleable jobs. This work highlights important correlations between workload characteristics (e.g., job runtimes and node requirements), malleability proportions, and scheduling strategies. These findings confirm the potential of malleability to address inefficiencies in current HPC practices and demonstrate that even limited adoption can provide substantial advantages, encouraging its integration into HPC resource management.

翻译：优化高性能计算（HPC）集群的资源利用率对于最大化系统效率和用户满意度至关重要。然而，传统的刚性作业调度往往导致资源利用不足和作业等待时间增加。本研究评估了资源弹性的优势，即作业调度器在运行时动态调整可塑作业的资源分配。利用来自Cori、Eagle和Theta超级计算机的真实工作负载轨迹，我们通过ElastiSim软件模拟了不同比例（0-100%）的可塑作业。我们评估了五种作业调度策略，包括一种新颖的策略，该策略尽可能将可塑作业维持在首选资源分配状态。结果表明，与完全刚性的工作负载相比，可塑作业在所有关键指标上均带来显著改善。考虑每台超级计算机上表现最佳的调度策略，作业周转时间减少了37-67%，作业完工时间减少了16-65%，作业等待时间减少了73-99%，节点利用率提高了5-52%。尽管改善程度有所差异，但即使在20%的可塑作业比例下，收益仍然可观。本研究揭示了工作负载特征（如作业运行时间和节点需求）、可塑性比例与调度策略之间的重要关联。这些发现证实了可塑性在解决当前HPC实践中低效问题的潜力，并表明即使有限采用也能带来显著优势，从而鼓励其融入HPC资源管理体系。