[NFC] Refactor symbol table parsing.

Symbol table parsing has evolved over the years and many plug-ins contained duplicate code in the ObjectFile::GetSymtab() that used to be pure virtual. With this change, the "Symbtab *ObjectFile::GetSymtab()" is no longer virtual and will end up calling a new "void ObjectFile::ParseSymtab(Symtab &symtab)" pure virtual function to actually do the parsing. This helps centralize the code for parsing the symbol table and allows the ObjectFile base class to do all of the common work, like taking the necessary locks and creating the symbol table object itself. Plug-ins now just need to parse when they are asked to parse as the ParseSymtab function will only get called once.

This is a retry of the original patch https://reviews.llvm.org/D113965 which was reverted. There was a deadlock in the Manual DWARF indexing code during symbol preloading where the module was asked on the main thread to preload its symbols, and this would in turn cause the DWARF manual indexing to use a thread pool to index all of the compile units, and if there were relocations on the debug information sections, these threads could ask the ObjectFile to load section contents, which could cause a call to ObjectFileELF::RelocateSection() which would ask for the symbol table from the module and it would deadlock. We can't lock the module in ObjectFile::GetSymtab(), so the solution I am using is to use a llvm::once_flag to create the symbol table object once and then lock the Symtab object. Since all APIs on the symbol table use this lock, this will prevent anyone from using the symbol table before it is parsed and finalized and will avoid the deadlock I mentioned. ObjectFileELF::GetSymtab() was never locking the module lock before and would put off creating the symbol table until somewhere inside ObjectFileELF::GetSymtab(). Now we create it one time inside of the ObjectFile::GetSymtab() and immediately lock it which should be safe enough. This avoids the deadlocks and still provides safety.

Differential Revision: https://reviews.llvm.org/D114288
22 files changed
tree: 467ea783215069b45fabfe238ffd806f3d92e45a
  1. .github/
  2. clang/
  3. clang-tools-extra/
  4. cmake/
  5. compiler-rt/
  6. cross-project-tests/
  7. flang/
  8. libc/
  9. libclc/
  10. libcxx/
  11. libcxxabi/
  12. libunwind/
  13. lld/
  14. lldb/
  15. llvm/
  16. mlir/
  17. openmp/
  18. polly/
  19. pstl/
  20. runtimes/
  21. utils/
  22. .arcconfig
  23. .arclint
  24. .clang-format
  25. .clang-tidy
  26. .git-blame-ignore-revs
  27. .gitignore
  28. .mailmap
  30. README.md
  31. SECURITY.md

The LLVM Compiler Infrastructure

This directory and its sub-directories contain source code for LLVM, a toolkit for the construction of highly optimized compilers, optimizers, and run-time environments.

The README briefly describes how to get started with building LLVM. For more information on how to contribute to the LLVM project, please take a look at the Contributing to LLVM guide.

Getting Started with the LLVM System

Taken from https://llvm.org/docs/GettingStarted.html.


Welcome to the LLVM project!

The LLVM project has multiple components. The core of the project is itself called “LLVM”. This contains all of the tools, libraries, and header files needed to process intermediate representations and convert them into object files. Tools include an assembler, disassembler, bitcode analyzer, and bitcode optimizer. It also contains basic regression tests.

C-like languages use the Clang front end. This component compiles C, C++, Objective-C, and Objective-C++ code into LLVM bitcode -- and from there into object files, using LLVM.

Other components include: the libc++ C++ standard library, the LLD linker, and more.

Getting the Source Code and Building LLVM

The LLVM Getting Started documentation may be out of date. The Clang Getting Started page might have more accurate information.

This is an example work-flow and configuration to get and build the LLVM source:

  1. Checkout LLVM (including related sub-projects like Clang):

    • git clone https://github.com/llvm/llvm-project.git

    • Or, on windows, git clone --config core.autocrlf=false https://github.com/llvm/llvm-project.git

  2. Configure and build LLVM and Clang:

    • cd llvm-project

    • cmake -S llvm -B build -G <generator> [options]

      Some common build system generators are:

      • Ninja --- for generating Ninja build files. Most llvm developers use Ninja.
      • Unix Makefiles --- for generating make-compatible parallel makefiles.
      • Visual Studio --- for generating Visual Studio projects and solutions.
      • Xcode --- for generating Xcode projects.

      Some common options:

      • -DLLVM_ENABLE_PROJECTS='...' --- semicolon-separated list of the LLVM sub-projects you'd like to additionally build. Can include any of: clang, clang-tools-extra, compiler-rt,cross-project-tests, flang, libc, libclc, libcxx, libcxxabi, libunwind, lld, lldb, mlir, openmp, polly, or pstl.

        For example, to build LLVM, Clang, libcxx, and libcxxabi, use -DLLVM_ENABLE_PROJECTS="clang;libcxx;libcxxabi".

      • -DCMAKE_INSTALL_PREFIX=directory --- Specify for directory the full path name of where you want the LLVM tools and libraries to be installed (default /usr/local).

      • -DCMAKE_BUILD_TYPE=type --- Valid options for type are Debug, Release, RelWithDebInfo, and MinSizeRel. Default is Debug.

      • -DLLVM_ENABLE_ASSERTIONS=On --- Compile with assertion checks enabled (default is Yes for Debug builds, No for all other build types).

    • cmake --build build [-- [options] <target>] or your build system specified above directly.

      • The default target (i.e. ninja or make) will build all of LLVM.

      • The check-all target (i.e. ninja check-all) will run the regression tests to ensure everything is in working order.

      • CMake will generate targets for each tool and library, and most LLVM sub-projects generate their own check-<project> target.

      • Running a serial build will be slow. To improve speed, try running a parallel build. That's done by default in Ninja; for make, use the option -j NNN, where NNN is the number of parallel jobs, e.g. the number of CPUs you have.

    • For more information see CMake

Consult the Getting Started with LLVM page for detailed information on configuring and compiling LLVM. You can visit Directory Layout to learn about the layout of the source code tree.