{"title":"利用扩展LogP模型进行冗余优化","authors":"Jörn Eisenbiegler, Welf Löwe, A. Wehrenpfennig","doi":"10.1109/APDC.1997.574026","DOIUrl":null,"url":null,"abstract":"We present a strategy for optimizing parallel algorithms introducing redundant computations. In order to calculate the optimal amount of redundancy, we generalize the LogP model to capture messages of varying sizes using functions instead of constants for the machine parameters. We validate our method for a wave simulation algorithm on a Parsytec PowerXplorer with eight processors and a workstation cluster with four workstations.","PeriodicalId":413925,"journal":{"name":"Proceedings. Advances in Parallel and Distributed Computing","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"On the optimization by redundancy using an extended LogP model\",\"authors\":\"Jörn Eisenbiegler, Welf Löwe, A. Wehrenpfennig\",\"doi\":\"10.1109/APDC.1997.574026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a strategy for optimizing parallel algorithms introducing redundant computations. In order to calculate the optimal amount of redundancy, we generalize the LogP model to capture messages of varying sizes using functions instead of constants for the machine parameters. We validate our method for a wave simulation algorithm on a Parsytec PowerXplorer with eight processors and a workstation cluster with four workstations.\",\"PeriodicalId\":413925,\"journal\":{\"name\":\"Proceedings. Advances in Parallel and Distributed Computing\",\"volume\":\"78 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. Advances in Parallel and Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APDC.1997.574026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Advances in Parallel and Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APDC.1997.574026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the optimization by redundancy using an extended LogP model
We present a strategy for optimizing parallel algorithms introducing redundant computations. In order to calculate the optimal amount of redundancy, we generalize the LogP model to capture messages of varying sizes using functions instead of constants for the machine parameters. We validate our method for a wave simulation algorithm on a Parsytec PowerXplorer with eight processors and a workstation cluster with four workstations.
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