|tagger||Tom Stellard <email@example.com>||Wed Nov 25 23:11:07 2020 -0800|
|author||Aaron Puchert <firstname.lastname@example.org>||Sun Nov 22 20:51:00 2020 +0100|
|committer||Tom Stellard <email@example.com>||Wed Nov 25 23:00:54 2020 -0800|
Build reproducible tarballs for releases Currently the tarballs contain superfluous metadata, like the user name of the packager and via Pax headers even the PID of the tar process that packaged the files. We build the monorepo projects directly from the git repo using "git archive" and for the test-suite we add some flags as recommended by https://reproducible-builds.org/docs/archives/. We don't use numeric owners though to be compatible with "git archive". The advantage of "git archive" is that the releaser doesn't have to download the tar ball and extract it, rather the archive is built directly from the repository. This is probably what GitHub uses internally to produce the tarballs, so I wouldn't expect a difference. Reviewed By: tstellar Differential Revision: https://reviews.llvm.org/D91494 (cherry picked from commit 1a009296a4e9a50e85908f9141c3c1ea860d73e4)
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.
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 converts it 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.
The LLVM Getting Started documentation may be out of date.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 -G <generator> [options] ../llvm
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, libcxx, libcxxabi, libunwind, lldb, compiler-rt, lld, polly, or debuginfo-tests.
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
-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 . [-- [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, e.g. the number of CPUs you have.
For more information see CMake