Online frequency-based performance and power estimation for clustered multi-processor systems

Modern Chip Multi-Processors (CMPs) are required to be increasingly power efficient while also offering higher performance and lower costs. A combination of Dynamic Voltage?Frequency Scaling (DVFS) and sophisticated resource-aware scheduling is needed to address the underlying problem of maximizing performance-per-Watt of CMP architectures. In this paper, we propose a methodology to predict the power consumption and performance for groups of concurrently executing applications at all available frequencies of a CMP. The methodology uses a combina-tion of hardware-based application profiling, contention-aware scheduling, and artificial neural networks. Experimental results on an Odroid-XU3 board demonstrate an increase in average performance per Watt of 30.5% (A15 cluster) and 11.4% (A7 cluster) over Linux?s Completely Fair Scheduler (CFS) and power governors. In addition, our methodology outperforms three state-of-the-art resource managers, yielding the highest performance per Watt in all evaluated use cases. Modern Chip Multi-Processors (CMPs) are required to be increasingly power efficient while also offering higher performance and lower costs. A combination of Dynamic Voltage?Frequency Scaling (DVFS) and sophisticated resource-aware scheduling is needed to address the underlying problem of maximizing performance-per-Watt of CMP architectures. In this paper, we propose a methodology to predict the power consumption and performance for groups of concurrently executing applications at all available frequencies of a CMP. The methodology uses a combina-tion of hardware-based application profiling, contention-aware scheduling, and artificial neural networks. Experimental results on an Odroid-XU3 board demonstrate an increase in average performance per Watt of 30.5% (A15 cluster) and 11.4% (A7 cluster) over Linux?s Completely Fair Scheduler (CFS) and power governors. In addition, our methodology outperforms three state-of-the-art resource managers, yielding the highest performance per Watt in all evaluated use cases.

Automated summary

This paper presents a methodology to predict power consumption and performance for groups of concurrently executing applications on Chip Multi-Processors (CMPs) at all available frequencies. By combining DVFS, resource-aware scheduling, and artificial neural networks, the methodology achieves significant performance improvements and outperforms state-of-the-art resource managers.