Dihia Belkacemi, M. Daoui, S. Bouzefrane, Y. Bouchebaba
{"title":"基于异构mpsoc的并行应用映射到片上网络的混合算法","authors":"Dihia Belkacemi, M. Daoui, S. Bouzefrane, Y. Bouchebaba","doi":"10.4018/IJDST.2019040103","DOIUrl":null,"url":null,"abstract":"Mapping parallel applications onto a network on chip (NoC) that is based on heterogeneous MPSoCs is considered as an instance of an NP-hard and a multi-objective problem. Various multi-objective algorithms have been proposed in the literature to handle this issue. Metaheuristics stand out as highly appropriate approaches to deal with this kind of problem. These metaheuristics are classified into two sets: population-based metaheuristics and single solution-based ones. To take advantage of the both sets, the trend is to use hybrid solutions that have shown to give better results. In this article, the authors propose to hybridize these two metaheuristics sets to find good Pareto mapping solutions to optimize the execution time and the energy consumption simultaneously. The experimental results have shown that the proposed hybrid algorithms give high quality non-dominated mapping solutions in a reasonable runtime.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parallel Applications Mapping onto Network on Chip Based on Heterogeneous MPSoCs Using Hybrid Algorithms\",\"authors\":\"Dihia Belkacemi, M. Daoui, S. Bouzefrane, Y. Bouchebaba\",\"doi\":\"10.4018/IJDST.2019040103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mapping parallel applications onto a network on chip (NoC) that is based on heterogeneous MPSoCs is considered as an instance of an NP-hard and a multi-objective problem. Various multi-objective algorithms have been proposed in the literature to handle this issue. Metaheuristics stand out as highly appropriate approaches to deal with this kind of problem. These metaheuristics are classified into two sets: population-based metaheuristics and single solution-based ones. To take advantage of the both sets, the trend is to use hybrid solutions that have shown to give better results. In this article, the authors propose to hybridize these two metaheuristics sets to find good Pareto mapping solutions to optimize the execution time and the energy consumption simultaneously. The experimental results have shown that the proposed hybrid algorithms give high quality non-dominated mapping solutions in a reasonable runtime.\",\"PeriodicalId\":118536,\"journal\":{\"name\":\"Int. J. Distributed Syst. Technol.\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Distributed Syst. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJDST.2019040103\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Distributed Syst. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJDST.2019040103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallel Applications Mapping onto Network on Chip Based on Heterogeneous MPSoCs Using Hybrid Algorithms
Mapping parallel applications onto a network on chip (NoC) that is based on heterogeneous MPSoCs is considered as an instance of an NP-hard and a multi-objective problem. Various multi-objective algorithms have been proposed in the literature to handle this issue. Metaheuristics stand out as highly appropriate approaches to deal with this kind of problem. These metaheuristics are classified into two sets: population-based metaheuristics and single solution-based ones. To take advantage of the both sets, the trend is to use hybrid solutions that have shown to give better results. In this article, the authors propose to hybridize these two metaheuristics sets to find good Pareto mapping solutions to optimize the execution time and the energy consumption simultaneously. The experimental results have shown that the proposed hybrid algorithms give high quality non-dominated mapping solutions in a reasonable runtime.