FCUFS: Core-Level Frequency Tuning for Energy Optimization on Intel Processors

Sacrificing minor performance for better energy efficiency is effective in reducing the energy consumption of supercomputers. Recent studies have utilized some frequency tuning and power capping features of Intel processors to decrease energy consumption in supercomputers. However, two main issues persist: 1) current methods do not account for variability between cores, leading to insufficient energy savings in mixed workloads; and 2) they fail to control the extent of performance loss following frequency tuning. To address these issues, we developed the FCUFS framework, which includes two components: 1) a neural network for predicting performance and power; and 2) a strategy optimization algorithm for selecting frequencies with controllable performance loss. We evaluated FCUFS on Intel mainstream processors across 15 dedicated workloads and 5 mixed workloads. On a single dual-socket Ice Lake-SP server, at a 5% performance loss target, the average energy savings were 11.4% for dedicated workloads and 13.8% for mixed workloads, with average performance losses of 2.9% and 3.5%, respectively. At a 10% performance loss target, the energy savings increased to 14.2% for dedicated workloads and 14.4% for mixed workloads, with performance losses of 8.7% and 9.4%, respectively. When scaling up to 2,048 cores, the average energy savings were 9.7% with 4.4% performance loss. The results show that FCUFS achieves consistent energy savings across dedicated and mixed workloads while maintaining controllable performance loss.