Trade-off between low power and energy efficiency in benchmarking

Abstract

Microprocessor architecture and its hardware implementation have a great influence on computer performance, power consumption, and energy efficiency. Nowadays, energy efficiency is a key point of Green Computing. However improving the performance by rising the CPU clock rate usually increases both the power and the energy consumptions. In this paper, we formulate experiments to study the relation between the CPU execution time, the power consumption, and the dissipated energy over a 0.2 GHz to 1.7 GHz range of CPU frequencies. These experiments are implemented on batterypowered mobile systems, for specific Dynamic Voltage and Frequency Scaling (DVFS) settings, by executing custom-made and standard benchmark tools for Intel and ARM processors. We report the results of the experiments and we demonstrate that we can achieve a minimum energy consumption but with a limited performance improvement. On the other hand, the performance optimization always increases the power. So other design factors, rather than clock speed, have to be considered for a low-power achievement.

On the Power Consumption of Serverless Functions: An Evaluation of OpenFaaS

“…Powerstat is open-source software designed for measuring power consumption by using the Advanced Configuration and Power Interface (ACPI) to read the amount of power consumed from the machine's battery [10]. This tool was used in [11] to measure the power consumption of a laptop running Linux OS. Similarly, PowerTop is an open-source software measuring power consumption by either reading the battery information, or using a mathematical model to estimate the power consumed by each process [12].…”

Power-Energy Simulation for Multi-Core Processors in Bench-marking

“…These settings demand the resetting of the power management policy, the disabling of some cores; and the setting of the only enabled cores with one of its frequency values in parallel with its upper frequency limit. The experiments are implemented on three different battery-powered mobile systems shown in Table 1: two Intel Laptops (Acer and Dell) and one ARM Smartphone (Samsung A5), on different Operating Systems Ubuntu and Android respectively in [1] and extended to multi-core on the two Laptops only. The offered workloads were CoreMark, the standard benchmark tool for Laptops and a custom-made Fibonacci benchmark for the Smartphone and also for the Acer and Dell Laptops.…”

“…In this paper, we make the case for exploring the trade-off between low power and energy efficiency over a wide range of clock frequencies. We do the experiments on different battery-powered Laptops and Smartphones in [1] on a single core. We enface two problems: the choice of power measure-* Mona A. Abou-Of, mona.abouof@pua.edu.eg Vol.…”