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ACM Meas. Anal. Comput. Syst."],"published-print":{"date-parts":[[2017,12,19]]},"abstract":"<jats:p>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.<\/jats:p>\n          <jats:p>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.<\/jats:p>\n          <jats:p>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%.<\/jats:p>","DOI":"10.1145\/3154502","type":"journal-article","created":{"date-parts":[[2018,3,23]],"date-time":"2018-03-23T18:28:08Z","timestamp":1521829688000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["The CSI Framework for Compiler-Inserted Program Instrumentation"],"prefix":"10.1145","volume":"1","author":[{"given":"Tao B.","family":"Schardl","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}]},{"given":"Tyler","family":"Denniston","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}]},{"given":"Damon","family":"Doucet","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}]},{"given":"Bradley C.","family":"Kuszmaul","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}]},{"given":"I-Ting Angelina","family":"Lee","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}]},{"given":"Charles E.","family":"Leiserson","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}]}],"member":"320","published-online":{"date-parts":[[2017,12,19]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Compilers: Principles, Techniques, and Tools","author":"Aho A. 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