The CSI Framework for Compiler-Inserted Program Instrumentation

Abstract

The CSI framework [15] 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

Cilkmem: Algorithms for Analyzing the Memory High-Water Mark of Fork-Join Parallel Programs

“…We implemented Cilkmem as a CSI tool [35] written in C++ for the Tapir compiler [37]. The following discussion describes how these facilities are used to implement Cilkmem's algorithms for MHWM analysis.…”

“…Both of Cilkmem's algorithms run efficiently in practice. We implemented Cilkmem using the CSI framework for compiler instrumentation [35] embedded in the Tapir/L-LVM compiler [37]. In Section 6, we measure the efficiency of Cilkmem on a suite of ten Cilk application benchmarks.…”