{"title":"优化数据流分析的稀疏表示","authors":"Erik Ruf","doi":"10.1145/202529.202535","DOIUrl":null,"url":null,"abstract":"Sparse program representations allow inter-statement dependences to be represented explicitly, enabling dataflow analyzers to restrict the propagation of information to paths where it could potentially affect the dataflow solution. This paper describes the use of a single sparse program representation, the value dependence graph, in both general and analysis-specific contexts, and demonstrates its utility in reducing the cost of dataflow analysis. We find that several semantics-preserving transformations are beneficial in both contexts.","PeriodicalId":398799,"journal":{"name":"ACM SIGPLAN Workshop on Intermediate Representations","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Optimizing sparse representations for dataflow analysis\",\"authors\":\"Erik Ruf\",\"doi\":\"10.1145/202529.202535\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sparse program representations allow inter-statement dependences to be represented explicitly, enabling dataflow analyzers to restrict the propagation of information to paths where it could potentially affect the dataflow solution. This paper describes the use of a single sparse program representation, the value dependence graph, in both general and analysis-specific contexts, and demonstrates its utility in reducing the cost of dataflow analysis. We find that several semantics-preserving transformations are beneficial in both contexts.\",\"PeriodicalId\":398799,\"journal\":{\"name\":\"ACM SIGPLAN Workshop on Intermediate Representations\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGPLAN Workshop on Intermediate Representations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/202529.202535\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGPLAN Workshop on Intermediate Representations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/202529.202535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing sparse representations for dataflow analysis
Sparse program representations allow inter-statement dependences to be represented explicitly, enabling dataflow analyzers to restrict the propagation of information to paths where it could potentially affect the dataflow solution. This paper describes the use of a single sparse program representation, the value dependence graph, in both general and analysis-specific contexts, and demonstrates its utility in reducing the cost of dataflow analysis. We find that several semantics-preserving transformations are beneficial in both contexts.