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计算机系统应用:2020,29(2):129-139
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嵌入式软件编译时能耗演化优化算法
(1.福建师范大学 数学与信息学院, 福州 350117;2.福建师范大学 福建省公共服务大数据挖掘与应用工程技术研究中心, 福州 350117;3.武汉纺织大学 数学与计算机学院, 武汉 430073)
Evolutionary Optimization Algorithm for Embedded Software Energy Consumption at GCC Compiling
(1.College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China;2.Fujian Provincial Engineering Technology Research Center for Public Service Big Data Mining and Application, Fujian Normal University, Fuzhou 350117, China;3.College of Mathematics and Computer Science, Wuhan Textile University, Wuhan 430073, China)
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投稿时间:2019-07-31    修订日期:2019-09-02
中文摘要: 编译选项的选择优化为降低嵌入式软件能耗提供了一种可行且有效的解决方案.GA-FP算法将频繁模式挖掘应用到演化过程中并已取得了较好的结果.然而,GA-FP还存在事务表规模较大、频繁选项模式的启发信息不全和时效性不好以及单点变异效率不高的缺点,潜在地影响了解质量和收敛速度.针对这些问题,文中提出一种嵌入式软件编译时能耗演化优化算法GA-MFPM.GA-MFPM借助逐代替换参考点和事务表的机制以降低事务表大小;在此基础上提出可获取更多启发式信息的频繁编译选项挖掘算法,并采用逐代挖掘的策略以保持频繁选项模式的时效性;进一步设计最大频繁模式匹配算法进行多点变异,以提高优化质量和收敛速度.通过与GA-FP在5个不同领域的8个典型案例下实验对比的结果表明:本文GA-MFPM获取了较GA-FP更低的软件能耗(平均降低2.4%,最高降低16.1%)和更快的收敛速度(平均加快57.6%,最高加快97.5%).
Abstract:The optimization of compilation options provides a feasible and effective solution to reduce the energy consumption of embedded software. GA-FP algorithm applies frequent pattern mining into the evolutionary process and has achieved better results than other algorithms. However, GA-FP still has the disadvantages, such as the large size of transaction table, incomplete and obsolete heuristic information provided by frequent option patterns and inefficient single point mutation, which potentially affects the solution quality and convergence rate. Aiming to these problems, this study proposes an evolutionary optimization algorithm for embedded software energy consumption at GCC compile time, called GA-MFPM. GA-MFPM replaces the reference point and transaction table mechanism by generation to reduce size of the transaction table. Further, a frequent compilation option mining algorithm is designed to acquire more heuristic information. It adopts a generation-by-generation mining strategy to help maintain the timeliness of frequent option patterns. Based on the frequent option patterns, a maximum frequent pattern matching algorithm is designed to perform multi-point mutation to improve optimization quality and convergence rate. The comparative experiments are done on 8 typical cases in 5 different fields between GA-MFPM and GA-FP. The experimental results indicate that the GA-MFPM can not only reduce the energy consumption of software more effectively (the average and maximal reduction ratios are 2.4% and 16.1% respectively), but also converge faster (the average of 57.6% faster and up to 97.5% faster) than GA-FP in this study.
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基金项目:福建省新世纪优秀人才项目(2017年);福建省自然科学基金(2015J01235,2017J01498);福建省教育厅JK类项目(JK2015006);湖北省自然科学基金(2018CFB689)
引用文本:
姚万庆,倪友聪,杜欣,叶鹏,肖如良.嵌入式软件编译时能耗演化优化算法.计算机系统应用,2020,29(2):129-139
YAO Wan-Qing,NI You-Cong,DU Xin,YE Peng,XIAO Ru-Liang.Evolutionary Optimization Algorithm for Embedded Software Energy Consumption at GCC Compiling.COMPUTER SYSTEMS APPLICATIONS,2020,29(2):129-139

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