{"title":"超越数据并行范式:问题和选项","authors":"G. Gao, Vivek Sarkar, L. A. Vazquez","doi":"10.1109/PMMP.1993.315541","DOIUrl":null,"url":null,"abstract":"Currently, the predominant approach in compiling a program for parallel execution on a distributed memory multiprocessor is driven by the data parallel paradigm, in which user-specified data mappings are used to derive computation mappings via ad hoc rules such as owner-computes. We explore a more general approach which is driven by the selection of computation mappings from the program dependence constraints, and by the selection of dynamic data mappings from the localization constraints in different computation phases of the program. We state the optimization problems addressed by this approach and outline the solution methods that can be used. We believe that this approach provides promising solutions beyond what can be achieved by the data parallel paradigm. The paper outlines the general program model assumed for this work, states the optimization problems addressed by the approach and presents solutions to these problems.<<ETX>>","PeriodicalId":220365,"journal":{"name":"Proceedings of Workshop on Programming Models for Massively Parallel Computers","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Beyond the data parallel paradigm: issues and options\",\"authors\":\"G. Gao, Vivek Sarkar, L. A. Vazquez\",\"doi\":\"10.1109/PMMP.1993.315541\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Currently, the predominant approach in compiling a program for parallel execution on a distributed memory multiprocessor is driven by the data parallel paradigm, in which user-specified data mappings are used to derive computation mappings via ad hoc rules such as owner-computes. We explore a more general approach which is driven by the selection of computation mappings from the program dependence constraints, and by the selection of dynamic data mappings from the localization constraints in different computation phases of the program. We state the optimization problems addressed by this approach and outline the solution methods that can be used. We believe that this approach provides promising solutions beyond what can be achieved by the data parallel paradigm. The paper outlines the general program model assumed for this work, states the optimization problems addressed by the approach and presents solutions to these problems.<<ETX>>\",\"PeriodicalId\":220365,\"journal\":{\"name\":\"Proceedings of Workshop on Programming Models for Massively Parallel Computers\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Workshop on Programming Models for Massively Parallel Computers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PMMP.1993.315541\",\"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 of Workshop on Programming Models for Massively Parallel Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PMMP.1993.315541","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Beyond the data parallel paradigm: issues and options
Currently, the predominant approach in compiling a program for parallel execution on a distributed memory multiprocessor is driven by the data parallel paradigm, in which user-specified data mappings are used to derive computation mappings via ad hoc rules such as owner-computes. We explore a more general approach which is driven by the selection of computation mappings from the program dependence constraints, and by the selection of dynamic data mappings from the localization constraints in different computation phases of the program. We state the optimization problems addressed by this approach and outline the solution methods that can be used. We believe that this approach provides promising solutions beyond what can be achieved by the data parallel paradigm. The paper outlines the general program model assumed for this work, states the optimization problems addressed by the approach and presents solutions to these problems.<>
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