{"title":"外交家:使用静态数据流抽象的多内核应用的映射","authors":"Bruno Bodin, Luigi Nardi, P. Kelly, M. O’Boyle","doi":"10.1109/MASCOTS.2016.35","DOIUrl":null,"url":null,"abstract":"In this paper we propose a novel approach to heterogeneous embedded systems programmability using a task-graph based framework called Diplomat. Diplomat is a task-graph framework that exploits the potential of static dataflow modeling and analysis to deliver performance estimation and CPU/GPU mapping. An application has to be specified once, and then the framework can automatically propose good mappings. We evaluate Diplomat with a computer vision application on two embedded platforms. Using the Diplomat generation we observed a 16% performance improvement on average and up to a 30% improvement over the best existing hand-coded implementation.","PeriodicalId":129389,"journal":{"name":"2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Diplomat: Mapping of Multi-kernel Applications Using a Static Dataflow Abstraction\",\"authors\":\"Bruno Bodin, Luigi Nardi, P. Kelly, M. O’Boyle\",\"doi\":\"10.1109/MASCOTS.2016.35\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a novel approach to heterogeneous embedded systems programmability using a task-graph based framework called Diplomat. Diplomat is a task-graph framework that exploits the potential of static dataflow modeling and analysis to deliver performance estimation and CPU/GPU mapping. An application has to be specified once, and then the framework can automatically propose good mappings. We evaluate Diplomat with a computer vision application on two embedded platforms. Using the Diplomat generation we observed a 16% performance improvement on average and up to a 30% improvement over the best existing hand-coded implementation.\",\"PeriodicalId\":129389,\"journal\":{\"name\":\"2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MASCOTS.2016.35\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MASCOTS.2016.35","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Diplomat: Mapping of Multi-kernel Applications Using a Static Dataflow Abstraction
In this paper we propose a novel approach to heterogeneous embedded systems programmability using a task-graph based framework called Diplomat. Diplomat is a task-graph framework that exploits the potential of static dataflow modeling and analysis to deliver performance estimation and CPU/GPU mapping. An application has to be specified once, and then the framework can automatically propose good mappings. We evaluate Diplomat with a computer vision application on two embedded platforms. Using the Diplomat generation we observed a 16% performance improvement on average and up to a 30% improvement over the best existing hand-coded implementation.