{"title":"Hoopl: a modular, reusable library for dataflow analysis and transformation","authors":"N. Ramsey, João Dias, S. Jones","doi":"10.1145/1863523.1863539","DOIUrl":null,"url":null,"abstract":"Dataflow analysis and transformation of control-flow graphs is pervasive in optimizing compilers, but it is typically entangled with the details of a particular compiler. We describe Hoopl, a reusable library that makes it unusually easy to define new analyses and transformations for any compiler written in Haskell. Hoopl's interface is modular and polymorphic, and it offers unusually strong static guarantees. The implementation encapsulates state-of-the-art algorithms (interleaved analysis and rewriting, dynamic error isolation), and it cleanly separates their tricky elements so that they can be understood independently.","PeriodicalId":188691,"journal":{"name":"ACM SIGPLAN Symposium/Workshop on Haskell","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGPLAN Symposium/Workshop on Haskell","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1863523.1863539","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 32
Abstract
Dataflow analysis and transformation of control-flow graphs is pervasive in optimizing compilers, but it is typically entangled with the details of a particular compiler. We describe Hoopl, a reusable library that makes it unusually easy to define new analyses and transformations for any compiler written in Haskell. Hoopl's interface is modular and polymorphic, and it offers unusually strong static guarantees. The implementation encapsulates state-of-the-art algorithms (interleaved analysis and rewriting, dynamic error isolation), and it cleanly separates their tricky elements so that they can be understood independently.