Power-constrained HPC systems increasingly run heterogeneous CPU--GPU applications under strict cluster-wide power limits. Existing cluster-wide power management policies rely on fair-share or utilization heuristics and do not capture application-specific sensitivity to CPU and GPU power caps, leading to inefficient use of reclaimed power. We present EcoShift, a performance-aware cluster-wide power management framework. EcoShift combines online performance prediction with a dynamic-programming-based allocator to distribute reclaimed power across CPU--GPU applications for maximum average performance improvement. Through emulation-based evaluation on two heterogeneous Intel CPU and NVIDIA A100/H100 GPU platforms with diverse CPU--GPU workloads, EcoShift consistently outperforms state-of-the-art policies, achieving up to 6% average performance improvement while preserving the cluster-wide power constraint.

翻译：功耗受限的高性能计算（HPC）系统日益在严格的集群级功率限额下运行异构CPU-GPU应用。现有集群级功耗管理策略依赖公平共享或利用率启发式方法，未能捕捉应用对CPU和GPU功率上限的特定敏感度，导致回收功率的低效利用。我们提出EcoShift，一个性能感知的集群级功耗管理框架。EcoShift将在线性能预测与基于动态规划的分配器相结合，跨CPU-GPU应用分配回收的功率以实现最大平均性能提升。通过在包含Intel CPU和NVIDIA A100/H100 GPU的两类异构平台以及多样化的CPU-GPU工作负载上的仿真评估，EcoShift始终优于最先进的策略，在维持集群级功率约束的同时实现高达6%的平均性能提升。