{"title":"一个研究GPU性能模型的微基准","authors":"V. Volkov","doi":"10.1145/3178487.3178536","DOIUrl":null,"url":null,"abstract":"Basic microarchitectural features of NVIDIA GPUs have been stable for a decade, and many analytic solutions were proposed to model their performance. We present a way to review, systematize, and evaluate these approaches by using a microbenchmark. In this manner, we produce a brief algebraic summary of key elements of selected performance models, identify patterns in their design, and highlight their previously unknown limitations. Also, we identify a potentially superior method for estimating performance based on classical work.","PeriodicalId":193776,"journal":{"name":"Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A microbenchmark to study GPU performance models\",\"authors\":\"V. Volkov\",\"doi\":\"10.1145/3178487.3178536\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Basic microarchitectural features of NVIDIA GPUs have been stable for a decade, and many analytic solutions were proposed to model their performance. We present a way to review, systematize, and evaluate these approaches by using a microbenchmark. In this manner, we produce a brief algebraic summary of key elements of selected performance models, identify patterns in their design, and highlight their previously unknown limitations. Also, we identify a potentially superior method for estimating performance based on classical work.\",\"PeriodicalId\":193776,\"journal\":{\"name\":\"Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3178487.3178536\",\"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 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3178487.3178536","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Basic microarchitectural features of NVIDIA GPUs have been stable for a decade, and many analytic solutions were proposed to model their performance. We present a way to review, systematize, and evaluate these approaches by using a microbenchmark. In this manner, we produce a brief algebraic summary of key elements of selected performance models, identify patterns in their design, and highlight their previously unknown limitations. Also, we identify a potentially superior method for estimating performance based on classical work.