{"title":"通过执行轨迹分析OpenMP并行化遗传算法的性能","authors":"G. Andrade, M. C. Cera","doi":"10.22456/2175-2745.85091","DOIUrl":null,"url":null,"abstract":"Run tracing allows you to identify issues affecting the performance of parallel applications. This work consists in evaluating the parallelization of a Genetic Algorithm applied to the Vehicle Routing Problem with OpenMP, where the performance obtained was not ideally expected. Being that it was possible to obtain a performance increase of 1.4 times in the architecture used, however, but still below ideal. Therefore, the general objective of this work is to investigate the causes of the low performance obtained by the Genetic Algorithm, performing an analysis from the execution traces. Our results showed that the parallelization of the Genetic Algorithm is according to the model in which it was implemented and to the set of instances of the target Vehicle Routing Problem used.","PeriodicalId":82472,"journal":{"name":"Research initiative, treatment action : RITA","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of the Performance of Genetic Algorithm Parallelized with OpenMP Through Execution Traces\",\"authors\":\"G. Andrade, M. C. Cera\",\"doi\":\"10.22456/2175-2745.85091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Run tracing allows you to identify issues affecting the performance of parallel applications. This work consists in evaluating the parallelization of a Genetic Algorithm applied to the Vehicle Routing Problem with OpenMP, where the performance obtained was not ideally expected. Being that it was possible to obtain a performance increase of 1.4 times in the architecture used, however, but still below ideal. Therefore, the general objective of this work is to investigate the causes of the low performance obtained by the Genetic Algorithm, performing an analysis from the execution traces. Our results showed that the parallelization of the Genetic Algorithm is according to the model in which it was implemented and to the set of instances of the target Vehicle Routing Problem used.\",\"PeriodicalId\":82472,\"journal\":{\"name\":\"Research initiative, treatment action : RITA\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research initiative, treatment action : RITA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22456/2175-2745.85091\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research initiative, treatment action : RITA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22456/2175-2745.85091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of the Performance of Genetic Algorithm Parallelized with OpenMP Through Execution Traces
Run tracing allows you to identify issues affecting the performance of parallel applications. This work consists in evaluating the parallelization of a Genetic Algorithm applied to the Vehicle Routing Problem with OpenMP, where the performance obtained was not ideally expected. Being that it was possible to obtain a performance increase of 1.4 times in the architecture used, however, but still below ideal. Therefore, the general objective of this work is to investigate the causes of the low performance obtained by the Genetic Algorithm, performing an analysis from the execution traces. Our results showed that the parallelization of the Genetic Algorithm is according to the model in which it was implemented and to the set of instances of the target Vehicle Routing Problem used.
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