Lukas Steiner, Gustavo Delazeri, Iron Prando da Silva, Matthias Jung, N. Wehn
{"title":"具有irace的自动DRAM子系统配置","authors":"Lukas Steiner, Gustavo Delazeri, Iron Prando da Silva, Matthias Jung, N. Wehn","doi":"10.1145/3579170.3579259","DOIUrl":null,"url":null,"abstract":"Nowadays, DRAM subsystem configuration includes a large number of parameters, resulting in an extensive design space. Setting these parameters is a challenging step in system design as the parameter-workload interactions are complex. Since design space exploration by exhaustive simulation is infeasible due to limited computing resources and development time, semi-automatic configuration involving both manual as well as simulation-based decisions is state-of-the-art. However, it requires a lot of expertise in the DRAM domain as well as application knowledge, and there is no guarantee for a good performance of the resulting subsystem. In this paper, we present a new framework that fully automatizes the DRAM subsystem configuration for a given parameter space and set of target applications. It is based on irace, a software package originally developed for automatic configuration of optimization algorithms. We show that the framework finds nearly-optimal configurations, while only a fraction of all application-configuration combinations has to be evaluated. In addition, all returned configurations perform better than a predefined standard configuration. Thus, our framework enables designers to automatically determine a suitable DRAM subsystem for their platform.","PeriodicalId":153341,"journal":{"name":"Proceedings of the DroneSE and RAPIDO: System Engineering for constrained embedded systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic DRAM Subsystem Configuration with irace\",\"authors\":\"Lukas Steiner, Gustavo Delazeri, Iron Prando da Silva, Matthias Jung, N. Wehn\",\"doi\":\"10.1145/3579170.3579259\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, DRAM subsystem configuration includes a large number of parameters, resulting in an extensive design space. Setting these parameters is a challenging step in system design as the parameter-workload interactions are complex. Since design space exploration by exhaustive simulation is infeasible due to limited computing resources and development time, semi-automatic configuration involving both manual as well as simulation-based decisions is state-of-the-art. However, it requires a lot of expertise in the DRAM domain as well as application knowledge, and there is no guarantee for a good performance of the resulting subsystem. In this paper, we present a new framework that fully automatizes the DRAM subsystem configuration for a given parameter space and set of target applications. It is based on irace, a software package originally developed for automatic configuration of optimization algorithms. We show that the framework finds nearly-optimal configurations, while only a fraction of all application-configuration combinations has to be evaluated. In addition, all returned configurations perform better than a predefined standard configuration. Thus, our framework enables designers to automatically determine a suitable DRAM subsystem for their platform.\",\"PeriodicalId\":153341,\"journal\":{\"name\":\"Proceedings of the DroneSE and RAPIDO: System Engineering for constrained embedded systems\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the DroneSE and RAPIDO: System Engineering for constrained embedded systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3579170.3579259\",\"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 the DroneSE and RAPIDO: System Engineering for constrained embedded systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3579170.3579259","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nowadays, DRAM subsystem configuration includes a large number of parameters, resulting in an extensive design space. Setting these parameters is a challenging step in system design as the parameter-workload interactions are complex. Since design space exploration by exhaustive simulation is infeasible due to limited computing resources and development time, semi-automatic configuration involving both manual as well as simulation-based decisions is state-of-the-art. However, it requires a lot of expertise in the DRAM domain as well as application knowledge, and there is no guarantee for a good performance of the resulting subsystem. In this paper, we present a new framework that fully automatizes the DRAM subsystem configuration for a given parameter space and set of target applications. It is based on irace, a software package originally developed for automatic configuration of optimization algorithms. We show that the framework finds nearly-optimal configurations, while only a fraction of all application-configuration combinations has to be evaluated. In addition, all returned configurations perform better than a predefined standard configuration. Thus, our framework enables designers to automatically determine a suitable DRAM subsystem for their platform.