Zhong Zheng

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.

Modern multi GPU HPC systems expose substantial computational capacity, yet inefficient GPU allocation often leads to wasted energy and underutilization. In practice, GPU applications exhibit heterogeneous and nonlinear scaling, making it inefficient to always use all available GPUs. We present EcoSched, an online scheduler that jointly optimizes GPU count selection and application coscheduling to improve workload level efficiency on multi GPU systems. EcoSched uses lightweight runtime profiling to estimate relative performance across GPU counts, applies a score based policy to balance energy efficiency and idle resources, and incorporates NUMA aware placement to mitigate interference. We implement EcoSched on heterogeneous CPU GPU platforms and evaluate it with diverse workloads on H100, A100, and V100 systems. EcoSched achieves up to 14.8% energy savings, 30.1% makespan improvement, and 40.4% EDP reduction over baseline schedulers, with modest performance overhead. These results show that jointly selecting GPU counts and coscheduling actions is essential for efficient multi GPU workload execution.

Large-scale systems face strict power limits when running heterogeneous CPU–GPU applications that exhibit varying responses to power caps. Existing power capping methods either target GPUs or CPUs alone or require extensive per-application profiling, making them impractical for production environments. We present SmartCap, an online framework that coordinates CPU–GPU power capping to reduce energy consumption while bounding performance degradation under a user specified threshold. SmartCap combines an offline phase, which builds a performance model and initializes a performance–power matrix, with an online phase that employs lightweight profiling and collaborative filtering to infer performance across the CPU–GPU cap space. Performance evaluation on three heterogeneous systems with a diverse workload suite shows that SmartCap achieves 95% mean prediction accuracy and as much as 20% energy savings, with negligible runtime overhead for most applications, demonstrating its practicality for performance-aware energy optimization in production environments.

High-performance computing (HPC) systems are essential for scientific discovery and engineering innovation. However, their growing power demands pose significant challenges, particularly as systems scale to the exascale level. Prior uncore frequency tuning studies have primarily focused on conventional HPC workloads running on CPU-only systems. As HPC advances toward heterogeneous computing, integrating diverse GPU workloads on heterogeneous CPU-GPU systems, it becomes imperative to revisit and enhance uncore scaling. Our investigation reveals that uncore frequency scales down only when CPU power approaches its thermal design power (TDP), which is rare in GPU-dominant applications. As a result, modern computing systems experience unnecessary power waste. In this study, we present MAGUS, a user-transparent uncore frequency scaling runtime for heterogeneous computing. MAGUS dynamically adjusts uncore frequencies according to distinct application execution phases, effectively minimizing power waste caused by consistently using maximum uncore frequencies. Our design incorporates several key techniques, including real-time monitoring and prediction of memory accesses, intelligent handling of frequent phase transitions, and leveraging vendor-provided power management features. We evaluate MAGUS with various GPU benchmarks and applications on multiple heterogeneous systems with different CPU and GPU architectures. Experimental results demonstrate that MAGUS achieves up to 27% energy savings compared to the default settings, while maintaining a performance loss of less than 5% and an overhead of under 1%.