Y. Caniou, E. Caron, G. Charrier, Andréea Chis, F. Desprez, E. Maisonnave
{"title":"网格上的海洋-大气模型化","authors":"Y. Caniou, E. Caron, G. Charrier, Andréea Chis, F. Desprez, E. Maisonnave","doi":"10.1109/ICPP.2008.37","DOIUrl":null,"url":null,"abstract":"In this paper, we tackle the problem of scheduling an Ocean-Atmosphere application used for climate prediction on the grid. An experiment is composed of several 1D-meshes of identical DAGs composed of parallel tasks. To obtain a good completion time, we divide groups of processors into sets each working on parallel tasks. The group sizes are chosen by computing the best makespan for several grouping possibilities. We improved this heuristic method by different means. The improvement yielding to the best makespan is the representation of the problem as an instance of the Knapsack problem. As this heuristic is firstly designed for homogeneous platforms, we present its adaptation to heterogeneous platforms. Simulations show improvements of the makespan up to 12\\%.","PeriodicalId":388408,"journal":{"name":"2008 37th International Conference on Parallel Processing","volume":"244 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Ocean-Atmosphere Modelization over the Grid\",\"authors\":\"Y. Caniou, E. Caron, G. Charrier, Andréea Chis, F. Desprez, E. Maisonnave\",\"doi\":\"10.1109/ICPP.2008.37\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we tackle the problem of scheduling an Ocean-Atmosphere application used for climate prediction on the grid. An experiment is composed of several 1D-meshes of identical DAGs composed of parallel tasks. To obtain a good completion time, we divide groups of processors into sets each working on parallel tasks. The group sizes are chosen by computing the best makespan for several grouping possibilities. We improved this heuristic method by different means. The improvement yielding to the best makespan is the representation of the problem as an instance of the Knapsack problem. As this heuristic is firstly designed for homogeneous platforms, we present its adaptation to heterogeneous platforms. Simulations show improvements of the makespan up to 12\\\\%.\",\"PeriodicalId\":388408,\"journal\":{\"name\":\"2008 37th International Conference on Parallel Processing\",\"volume\":\"244 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 37th International Conference on Parallel Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPP.2008.37\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 37th International Conference on Parallel Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPP.2008.37","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we tackle the problem of scheduling an Ocean-Atmosphere application used for climate prediction on the grid. An experiment is composed of several 1D-meshes of identical DAGs composed of parallel tasks. To obtain a good completion time, we divide groups of processors into sets each working on parallel tasks. The group sizes are chosen by computing the best makespan for several grouping possibilities. We improved this heuristic method by different means. The improvement yielding to the best makespan is the representation of the problem as an instance of the Knapsack problem. As this heuristic is firstly designed for homogeneous platforms, we present its adaptation to heterogeneous platforms. Simulations show improvements of the makespan up to 12\%.