{"title":"大规模并行分层结构的高性能映射","authors":"Sotirios G. Ziavras","doi":"10.1109/FMPC.1990.89467","DOIUrl":null,"url":null,"abstract":"Techniques for mapping image processing and computer vision algorithms onto a class of hierarchically structured systems are presented. In order to produce mappings of maximum efficiency, objective functions that measure the quality of given mappings with respect to particular optimization goals are proposed. The effectiveness and the computation complexity of mapping algorithms that yield very high performance by minimizing the objective functions are discussed. Performance results are also presented.<<ETX>>","PeriodicalId":193332,"journal":{"name":"[1990 Proceedings] The Third Symposium on the Frontiers of Massively Parallel Computation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1990-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High performance mapping for massively parallel hierarchical structures\",\"authors\":\"Sotirios G. Ziavras\",\"doi\":\"10.1109/FMPC.1990.89467\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Techniques for mapping image processing and computer vision algorithms onto a class of hierarchically structured systems are presented. In order to produce mappings of maximum efficiency, objective functions that measure the quality of given mappings with respect to particular optimization goals are proposed. The effectiveness and the computation complexity of mapping algorithms that yield very high performance by minimizing the objective functions are discussed. Performance results are also presented.<<ETX>>\",\"PeriodicalId\":193332,\"journal\":{\"name\":\"[1990 Proceedings] The Third Symposium on the Frontiers of Massively Parallel Computation\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1990-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1990 Proceedings] The Third Symposium on the Frontiers of Massively Parallel Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FMPC.1990.89467\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1990 Proceedings] The Third Symposium on the Frontiers of Massively Parallel Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FMPC.1990.89467","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High performance mapping for massively parallel hierarchical structures
Techniques for mapping image processing and computer vision algorithms onto a class of hierarchically structured systems are presented. In order to produce mappings of maximum efficiency, objective functions that measure the quality of given mappings with respect to particular optimization goals are proposed. The effectiveness and the computation complexity of mapping algorithms that yield very high performance by minimizing the objective functions are discussed. Performance results are also presented.<>