|tagger||Tom Stellard <email@example.com>||Sat Mar 11 07:29:00 2023|
|author||Zahira Ammarguellat <firstname.lastname@example.org>||Thu Mar 09 14:24:01 2023|
|committer||Tom Stellard <email@example.com>||Sat Mar 11 06:55:02 2023|
Revert "Currently the control of the eval-method is mixed with fast-math." Setting __FLT_EVAL_METHOD__ to -1 with fast-math will set __GLIBC_FLT_EVAL_METHOD to 2 and long double ends up being used for float_t and double_t. This creates some ABI breakage with various C libraries. See details here: https://github.com/llvm/llvm-project/issues/60781 This reverts commit bbf0d1932a3c1be970ed8a580e51edf571b80fd5. (cherry picked from commit 2f1264260b37e9bd79737181e459ae20e10c5fea)
This directory and its sub-directories contain the 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.
Taken from here.
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 frontend. This component compiles C, C++, Objective-C, and Objective-C++ code into LLVM bitcode -- and from there into object files, using 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The LLVM Getting Started documentation may be out of date.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:
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
Configure and build LLVM and Clang:
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_RUNTIMES='...' --- semicolon-separated list of the LLVM sub-projects and runtimes you'd like to additionally build.
LLVM_ENABLE_PROJECTS can include any of: clang, clang-tools-extra, cross-project-tests, flang, libc, libclc, lld, lldb, mlir, openmp, polly, or pstl.
LLVM_ENABLE_RUNTIMES can include any of libcxx, libcxxabi, libunwind, compiler-rt, libc or openmp. Some runtime projects can be specified either in
LLVM_ENABLE_PROJECTS or in
For example, to build LLVM, Clang, libcxx, and libcxxabi, use
-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). Be careful if you install runtime libraries: if your system uses those provided by LLVM (like libc++ or libc++abi), you must not overwrite your system's copy of those libraries, since that could render your system unusable. In general, using something like
/usr is not advised, but
/usr/local is fine.
-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.
make) will build all of LLVM.
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
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 to run. In most cases, you get the best performance if you specify the number of CPU threads you have. On some Unix systems, you can specify this with
For more information see CMake.
The LLVM project has adopted a code of conduct for participants to all modes of communication within the project.