{"title":"用MACS进行分层性能建模:凸型C-240的案例研究","authors":"E. Boyd, E. Davidson","doi":"10.1109/ISCA.1993.698561","DOIUrl":null,"url":null,"abstract":"The MACS performance model introduced here can be applied to a Machine and Application of interest, the Compiler-generated workload, and the Scheduling of the workload by the compiler. The Ma, MAC, and MACS bounds each fix the named subset of M, A, C, and S while freeing the bound from the constraints imposed by the others. A/X performance measurement is used to measure access-only and execute-only code performance. Such hierarchical performance modeling exposes the gaps between the various bounds, the A/X measurements, and the actual performance, thereby focusing performance optimization at the appropriate levels in a systematic and goal-directed manner. A simple, but detailed, case study of the Convex C-240 vector mini-supercomputer illustrates the method.","PeriodicalId":410022,"journal":{"name":"Proceedings of the 20th Annual International Symposium on Computer Architecture","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1993-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Hierarchical Performance Modeling With MACS: A Case Study Of The Convex C-240\",\"authors\":\"E. Boyd, E. Davidson\",\"doi\":\"10.1109/ISCA.1993.698561\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The MACS performance model introduced here can be applied to a Machine and Application of interest, the Compiler-generated workload, and the Scheduling of the workload by the compiler. The Ma, MAC, and MACS bounds each fix the named subset of M, A, C, and S while freeing the bound from the constraints imposed by the others. A/X performance measurement is used to measure access-only and execute-only code performance. Such hierarchical performance modeling exposes the gaps between the various bounds, the A/X measurements, and the actual performance, thereby focusing performance optimization at the appropriate levels in a systematic and goal-directed manner. A simple, but detailed, case study of the Convex C-240 vector mini-supercomputer illustrates the method.\",\"PeriodicalId\":410022,\"journal\":{\"name\":\"Proceedings of the 20th Annual International Symposium on Computer Architecture\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 20th Annual International Symposium on Computer Architecture\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCA.1993.698561\",\"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 20th Annual International Symposium on Computer Architecture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCA.1993.698561","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hierarchical Performance Modeling With MACS: A Case Study Of The Convex C-240
The MACS performance model introduced here can be applied to a Machine and Application of interest, the Compiler-generated workload, and the Scheduling of the workload by the compiler. The Ma, MAC, and MACS bounds each fix the named subset of M, A, C, and S while freeing the bound from the constraints imposed by the others. A/X performance measurement is used to measure access-only and execute-only code performance. Such hierarchical performance modeling exposes the gaps between the various bounds, the A/X measurements, and the actual performance, thereby focusing performance optimization at the appropriate levels in a systematic and goal-directed manner. A simple, but detailed, case study of the Convex C-240 vector mini-supercomputer illustrates the method.