PPEP: Online Performance, Power, and Energy Prediction Framework and DVFS Space Exploration

Abstract

Performance, power, and energy (PPE) are critical aspects of modern computing. It is challenging to accurately predict, in real time, the effect of dynamic voltage and frequency scaling (DVFS) on PPE across a wide range of voltages and frequencies. This results in the use of reactive, iterative, and inefficient algorithms for dynamically finding good DVFS states. We propose PPEP, an online PPE prediction framework that proactively and rapidly searches the DVFS space. PPEP uses hardware events to implement both a cycles-per-instruction (CPI) model as well as a per-core power model in order to predict PPE across all DVFS states. We verify on modern AMD CPUs that the PPEP power model achieves an average error of 4.6% (2.8% standard deviation) on 152 benchmark combinations at 5 distinct voltage-frequency states. Predicting average chip power across different DVFS states achieves an average error of 4.2% with a 3.6% standard deviation. Further, we demonstrate the usage of PPEP by creating and evaluating a highly responsive power capping mechanism that can meet power targets in a single step. PPEP also provides insights for future development of DVFS technologies. For example, we find that it is important to carefully consider background workloads for DVFS policies and that enabling north bridge DVFS can offer up to 20% additional energy saving or a 1.4x performance improvement.