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标题：Investigation and Evaluation of Program Code Optimization Algorithms for Reducing Power Consumption by Combining Genetic and K-means Algorithms
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作者：Sajad Bahrekazemi ; Asadollah Shahbahrami
期刊名称：International Journal of Mechatronics, Electrical and Computer Technology
印刷版ISSN：2305-0543
出版年度：2018
卷号：8
期号：29
页码：3959-3965
出版社：Austrian E-Journals of Universal Scientific Organization
摘要：Since 1990, power has been one of the major restrictions in computer systems. Due to lower energy reserves, the importance of saving power is increasingly felt. Today this issue is much more evident in computer science, especially computer programs. Further savings in power is more acceptable in different software aspects because power saving in software aspects is more cost effective than the hardware aspects. Power consumption is a major challenge in programs, thus code optimization algorithms accelerate application's navigation and optimizes reduced power consumption. In the applications, by reducing the consumed power, the processor will have free time and its waste of time is prevented. For example, when a program runs in a CPU with slow clock, it consumes less energy than is the case when it runs with higher clock speed. The purpose of this paper is to reduce power consumption by applications. The power consumption has become a major challenge in computer systems and knowledge and access to parts of the program that have higher power consumption is very important because, by having access to these parts, it is possible to examine their improvement methods. Here, genetic and K-means algorithms are combined and an algorithm is proposed for code optimization that uses the loop interchange optimization technique which can reduce the power consumption by the program. The implementation results indicate the reduced power consumption in implementation of various matrix sizes. Program processing. The purpose of code optimization algorithm is to improve the performance of programs to reduce their power consumption.
关键词：Code optimization algorithms; reducing power consumption; genetic algorithm; K;means algorithm