It is common experience to upgrade firmware of mobile devices and obtain longer battery life, living proof of how software affects power consumption of a device. Despite this empirical observation, there is a lack for models and methodologies correlating computations with power consumption. In this paper we propose a methodology for conducting measures which result independent of the underlying system running the algorithm/software to be tested. Early experimental results are presented and discussed, showing that this methodology is robust and can be used in many settings. We thus adopt it to study the impact of computation and pattern of memory accesses onto the energy-profile of an algorithm when executed on different processors and architectures, thus achieving some surprising insights on green algorithm design.
Information Processing at Work: On Energy-Aware Algorithm Design
CISTERNINO, ANTONIO;FERRAGINA, PAOLO;MORELLI, DAVIDE;COPPOLA, MASSIMO
2010-01-01
Abstract
It is common experience to upgrade firmware of mobile devices and obtain longer battery life, living proof of how software affects power consumption of a device. Despite this empirical observation, there is a lack for models and methodologies correlating computations with power consumption. In this paper we propose a methodology for conducting measures which result independent of the underlying system running the algorithm/software to be tested. Early experimental results are presented and discussed, showing that this methodology is robust and can be used in many settings. We thus adopt it to study the impact of computation and pattern of memory accesses onto the energy-profile of an algorithm when executed on different processors and architectures, thus achieving some surprising insights on green algorithm design.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
