T. Schardl, Tyler Denniston, Damon Doucet, Bradley C. Kuszmaul, I. Lee, C. Leiserson
{"title":"用于编译器插入程序检测的CSI框架","authors":"T. Schardl, Tyler Denniston, Damon Doucet, Bradley C. Kuszmaul, I. Lee, C. Leiserson","doi":"10.1145/3219617.3219657","DOIUrl":null,"url":null,"abstract":"The CSI framework provides comprehensive static instrumentation that a compiler can insert into a program-under-test so that dynamic-analysis tools - memory checkers, race detectors, cache simulators, performance profilers, code-coverage analyzers, etc. - can observe and investigate runtime behavior. Heretofore, tools based on compiler instrumentation would each separately modify the compiler to insert their own instrumentation. In contrast, CSI inserts a standard collection of instrumentation hooks into the program-under-test. Each CSI-tool is implemented as a library that defines relevant hooks, and the remaining hooks are \"nulled\" out and elided during either compile-time or link-time optimization, resulting in instrumented runtimes on par with custom instrumentation. CSI allows many compiler-based tools to be written as simple libraries without modifying the compiler, lowering the bar for the development of dynamic-analysis tools. We have defined a standard API for CSI and modified LLVM to insert CSI hooks into the compiler's internal representation (IR) of the program. The API organizes IR objects - such as functions, basic blocks, and memory accesses - into flat and compact ID spaces, which not only simplifies the building of tools, but surprisingly enables faster maintenance of IR-object data than do traditional hash tables. CSI hooks contain a \"property\" parameter that allows tools to customize behavior based on static information without introducing overhead. CSI provides \"forensic\" tables that tools can use to associate IR objects with source-code locations and to relate IR objects to each other. To evaluate the efficacy of CSI, we implemented six demonstration CSI-tools. One of our studies shows that compiling with CSI and linking with the \"null\" CSI-tool produces a tool-instrumented executable that is as fast as the original uninstrumented code. Another study, using a CSI port of Google's ThreadSanitizer, shows that the CSI-tool rivals the performance of Google's custom compiler-based implementation. All other demonstration CSI tools slow down the execution of the program-under-test by less than 70%.","PeriodicalId":210440,"journal":{"name":"Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"The CSI Framework for Compiler-Inserted Program Instrumentation\",\"authors\":\"T. Schardl, Tyler Denniston, Damon Doucet, Bradley C. Kuszmaul, I. Lee, C. Leiserson\",\"doi\":\"10.1145/3219617.3219657\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The CSI framework provides comprehensive static instrumentation that a compiler can insert into a program-under-test so that dynamic-analysis tools - memory checkers, race detectors, cache simulators, performance profilers, code-coverage analyzers, etc. - can observe and investigate runtime behavior. Heretofore, tools based on compiler instrumentation would each separately modify the compiler to insert their own instrumentation. In contrast, CSI inserts a standard collection of instrumentation hooks into the program-under-test. Each CSI-tool is implemented as a library that defines relevant hooks, and the remaining hooks are \\\"nulled\\\" out and elided during either compile-time or link-time optimization, resulting in instrumented runtimes on par with custom instrumentation. CSI allows many compiler-based tools to be written as simple libraries without modifying the compiler, lowering the bar for the development of dynamic-analysis tools. We have defined a standard API for CSI and modified LLVM to insert CSI hooks into the compiler's internal representation (IR) of the program. The API organizes IR objects - such as functions, basic blocks, and memory accesses - into flat and compact ID spaces, which not only simplifies the building of tools, but surprisingly enables faster maintenance of IR-object data than do traditional hash tables. CSI hooks contain a \\\"property\\\" parameter that allows tools to customize behavior based on static information without introducing overhead. CSI provides \\\"forensic\\\" tables that tools can use to associate IR objects with source-code locations and to relate IR objects to each other. To evaluate the efficacy of CSI, we implemented six demonstration CSI-tools. One of our studies shows that compiling with CSI and linking with the \\\"null\\\" CSI-tool produces a tool-instrumented executable that is as fast as the original uninstrumented code. Another study, using a CSI port of Google's ThreadSanitizer, shows that the CSI-tool rivals the performance of Google's custom compiler-based implementation. 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The CSI Framework for Compiler-Inserted Program Instrumentation
The CSI framework provides comprehensive static instrumentation that a compiler can insert into a program-under-test so that dynamic-analysis tools - memory checkers, race detectors, cache simulators, performance profilers, code-coverage analyzers, etc. - can observe and investigate runtime behavior. Heretofore, tools based on compiler instrumentation would each separately modify the compiler to insert their own instrumentation. In contrast, CSI inserts a standard collection of instrumentation hooks into the program-under-test. Each CSI-tool is implemented as a library that defines relevant hooks, and the remaining hooks are "nulled" out and elided during either compile-time or link-time optimization, resulting in instrumented runtimes on par with custom instrumentation. CSI allows many compiler-based tools to be written as simple libraries without modifying the compiler, lowering the bar for the development of dynamic-analysis tools. We have defined a standard API for CSI and modified LLVM to insert CSI hooks into the compiler's internal representation (IR) of the program. The API organizes IR objects - such as functions, basic blocks, and memory accesses - into flat and compact ID spaces, which not only simplifies the building of tools, but surprisingly enables faster maintenance of IR-object data than do traditional hash tables. CSI hooks contain a "property" parameter that allows tools to customize behavior based on static information without introducing overhead. CSI provides "forensic" tables that tools can use to associate IR objects with source-code locations and to relate IR objects to each other. To evaluate the efficacy of CSI, we implemented six demonstration CSI-tools. One of our studies shows that compiling with CSI and linking with the "null" CSI-tool produces a tool-instrumented executable that is as fast as the original uninstrumented code. Another study, using a CSI port of Google's ThreadSanitizer, shows that the CSI-tool rivals the performance of Google's custom compiler-based implementation. All other demonstration CSI tools slow down the execution of the program-under-test by less than 70%.