|author||wlei <firstname.lastname@example.org>||Thu Nov 04 00:08:37 2021 -0700|
|committer||wlei <email@example.com>||Tue Nov 30 15:57:59 2021 -0800|
[FS-AFDO][llvm-profgen] Generate profile with FS-AFDO discriminator In order to support generating profile with FS discriminator, three kind of changes are done in llvm-profgen: 1) Dissassemble .rodata section to check if FS discriminator var ('"__llvm_fs_discriminator__"') exists and set the corresponding flag in the binary. 2) Change the discriminator decoding in `getBaseDiscriminator` and `getDuplicationFactor`. 3) set true for `FunctionSamples::ProfileIsFS` to enable FS functionality in ProfileData. Reviewed By: xur, hoy, wenlei Differential Revision: https://reviews.llvm.org/D113296
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 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.
The LLVM Getting Started documentation may be out of date.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_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
-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 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