A. Gebremedhin, I. G. Lassous, J. Gustedt, J. A. Telle
{"title":"并行资源优化计算模型","authors":"A. Gebremedhin, I. G. Lassous, J. Gustedt, J. A. Telle","doi":"10.1109/HPCSA.2002.1019141","DOIUrl":null,"url":null,"abstract":"We present a new parallel computation model that enables the design of resource-optimal scalable parallel algorithms and simplifies their analysis. The model rests on the novel idea of incorporating relative optimality as an integral part and measuring the quality of a parallel algorithm in terms of granularity.","PeriodicalId":111862,"journal":{"name":"Proceedings 16th Annual International Symposium on High Performance Computing Systems and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"PRO:a model for parallel resource-optimal computation\",\"authors\":\"A. Gebremedhin, I. G. Lassous, J. Gustedt, J. A. Telle\",\"doi\":\"10.1109/HPCSA.2002.1019141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a new parallel computation model that enables the design of resource-optimal scalable parallel algorithms and simplifies their analysis. The model rests on the novel idea of incorporating relative optimality as an integral part and measuring the quality of a parallel algorithm in terms of granularity.\",\"PeriodicalId\":111862,\"journal\":{\"name\":\"Proceedings 16th Annual International Symposium on High Performance Computing Systems and Applications\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 16th Annual International Symposium on High Performance Computing Systems and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPCSA.2002.1019141\",\"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 16th Annual International Symposium on High Performance Computing Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCSA.2002.1019141","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PRO:a model for parallel resource-optimal computation
We present a new parallel computation model that enables the design of resource-optimal scalable parallel algorithms and simplifies their analysis. The model rests on the novel idea of incorporating relative optimality as an integral part and measuring the quality of a parallel algorithm in terms of granularity.