Understanding the Efficacy of Power Profiles: A Case Study of AMD Instinct MI100 GPU

Graphics Processing Units (GPUs) have become pivotal for modern high-performance computing (HPC) and artificial intelligence workloads due to their substantial computational prowess. However, this computational prowess comes at a cost, as GPUs consume vast amounts of power, presenting a challenge for high-end computing systems, including those aimed at achieving exascale computing capabilities. In response to the power efficiency problem, modern GPUs typically offer the ability to adjust clock frequencies and cap power consumption. However, the AMD Instinct MI100 takes a unique approach by introducing a set of predefined power profiles that internally manipulate clock frequencies to manage power. This study evaluates the effectiveness of these power profiles through a comparative analysis of various power and performance metrics. It indicates that, for most of the selected workloads and during significant portions of their execution, the GPU consumes power exceeding its specified Thermal Design Power (TDP). For instance, the GROMACS workload exceeded its TDP by one-third during almost half of its execution time. Furthermore, the study notes a significant increase in temperature reaching as high as 80°C. Moreover, DGEMM and STREAM workloads exhibit similar power consumption patterns, suggesting that the underlying power management scheme does not adapt power allocation based on the computational intensity of the workload. Thus, the study demonstrates that changing the power profile does not significantly impact crucial metrics such as performance, clock frequency, voltage, GPU utilization, or temperature. In summary, this research sheds light on the power dynamics of the AMD Instinct MI100 GPU, emphasizing the challenges associated with power, performance, and thermal management in HPC environments. The findings underscore the importance of fine-tuning power management strategies to enhance energy efficiency while maintaining optimal performance in GPUs.