Automating CPU Dynamic Thermal Control for High Performance Computing

In a production high-performance computing (HPC) data center, numerous factors, including workload compute in-tensity, cooling infrastructure failure, and the use of economized cooling can substantially increase the CPU temperature. CPU thermal design-related studies have shown that slight variances in the operational temperature can significantly impact the lifetime, durability, and performance of a CPU. Therefore, it is critical to monitor and control the operating temperature of the CPU. In this study, we design an automated and continuous CPU thermal monitoring and control methodology to maintain and control a healthy CPU thermal state. This research utilizes the Redfish protocol to monitor the CPU temperature and dynamic voltage frequency scaling to control the temperature. We developed a reference implementation and evaluated our methodology using a cluster of 150 Raspberry Pi3 nodes. We performed extensive CPU thermal analyses in different scenarios. We analyzed how quickly a CPU can attain the maximum temperature under 100% load at room temperature. Based on our experiments, the temperature of a CPU with 100% load can increase to ~72°C (161.6°F) and ~86°C (186.8°F) with the lowest and highest CPU frequency configurations, respectively. We analyzed the impact of applying thermal control at eight temperature configurations on the thermal and frequency scaling behavior of a CPU. We observed that applying thermal control at lower temperature configurations (e.g., 70°C (158°F)) is a better configuration for healing an overheated CPU. As a result of the proposed model, the CPU operating at normal temperature will consume comparatively less energy, deliver higher performance, and augment its durability.