{"title":"多核语法演化(MCGE)演化多核并行程序的效率研究","authors":"Gopinath Chennupati, Jeannie Fitzgerald, C. Ryan","doi":"10.1109/NaBIC.2014.6921885","DOIUrl":null,"url":null,"abstract":"In this paper we investigate a novel technique that optimizes the execution time of Grammatical Evolution through the usage of on-chip multiple processors. This technique, Multicore Grammatical Evolution (MCGE) evolves natively parallel programs with the help of OpenMP primitives through the grammars, such that not only can we exploit parallelism while evolving individuals, but the final individuals produced can also be executed on parallel architectures even outside the evolutionary system. We test MCGE on two difficult benchmark GP problems and show its efficiency in exploiting the power of the multicore architectures. We further discuss that, on these problems, the system evolves longer individuals while they are evaluated quicker than their serial implementation.","PeriodicalId":209716,"journal":{"name":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"On the efficiency of Multi-core Grammatical Evolution (MCGE) evolving multi-core parallel programs\",\"authors\":\"Gopinath Chennupati, Jeannie Fitzgerald, C. Ryan\",\"doi\":\"10.1109/NaBIC.2014.6921885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we investigate a novel technique that optimizes the execution time of Grammatical Evolution through the usage of on-chip multiple processors. This technique, Multicore Grammatical Evolution (MCGE) evolves natively parallel programs with the help of OpenMP primitives through the grammars, such that not only can we exploit parallelism while evolving individuals, but the final individuals produced can also be executed on parallel architectures even outside the evolutionary system. We test MCGE on two difficult benchmark GP problems and show its efficiency in exploiting the power of the multicore architectures. We further discuss that, on these problems, the system evolves longer individuals while they are evaluated quicker than their serial implementation.\",\"PeriodicalId\":209716,\"journal\":{\"name\":\"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NaBIC.2014.6921885\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NaBIC.2014.6921885","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the efficiency of Multi-core Grammatical Evolution (MCGE) evolving multi-core parallel programs
In this paper we investigate a novel technique that optimizes the execution time of Grammatical Evolution through the usage of on-chip multiple processors. This technique, Multicore Grammatical Evolution (MCGE) evolves natively parallel programs with the help of OpenMP primitives through the grammars, such that not only can we exploit parallelism while evolving individuals, but the final individuals produced can also be executed on parallel architectures even outside the evolutionary system. We test MCGE on two difficult benchmark GP problems and show its efficiency in exploiting the power of the multicore architectures. We further discuss that, on these problems, the system evolves longer individuals while they are evaluated quicker than their serial implementation.