Runtime Energy Adaptation with Low-Impact Instrumented Code in a Power-Scalable Cluster System

  • Authors:

  • Hideaki Kimura;Takayuki Imada;Mitsuhisa Sato

  • Affiliations:

  • -;-;-

  • Venue:
  • CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
  • Year:
  • 2010

Quantified Score

Hi-index 0.01

Visualization

Abstract

Recently, improving the energy efficiency of high performance PC clusters has become important. In order to reduce the energy consumption of the microprocessor, many high performance microprocessors have a Dynamic Voltage and Frequency Scaling (DVFS) mechanism. This paper proposes a new DVFS method called the Code-Instrumented Runtime (CIRuntime) DVFS method, in which a combination of voltage and frequency, which is called a P-State, is managed in the instrumented code at runtime. The proposed CI-Runtime DVFS method achieves better energy saving than the Interrupt based Runtime DVFS method, since it selects the appropriate P-State in each defined region based on the characteristics of program execution. Moreover, the proposed CI-Runtime DVFS method is more useful than the Static DVFS method, since it does not acquire exhaustive profiles for each P-State. The method consists of two parts. In the first part of the proposed CI-Runtime DVFS method, the instrumented codes are inserted by defining regions that have almost the same characteristics. The instrumented code must be inserted at the appropriate point, because the performance of the application decreases greatly if the instrumented code is called too many times in a short period. A method for automatically defining regions is proposed in this paper. The second part of the proposed method is the energy adaptation algorithm which is used at runtime. Two types of DVFS control algorithms energy adaptation with estimated energy consumption and energy adaptation with only performance information, are compared. The proposed CIRuntime DVFS method was implemented on a power-scalable PC cluster. The results show that the proposed CI-Runtime with energy adaptation using estimated energy consumption could achieve an energy saving of 14.2% which is close to the optimal value, without obtaining exhaustive profiles for every available P-State setting.