|author||Amara Emerson <email@example.com>||Tue Sep 29 14:39:54 2020 -0700|
|committer||Hans Wennborg <firstname.lastname@example.org>||Wed Sep 30 13:05:48 2020 +0200|
[GlobalISel] Fix multiply with overflow intrinsics legalization generating invalid MIR. During lowering of G_UMULO and friends, the previous code moved the builder's insertion point to be after the legalizing instruction. When that happened, if there happened to be a "G_CONSTANT i32 0" immediately after, the CSEMIRBuilder would try to find that constant during the buildConstant(zero) call, and since it dominates itself would return the iterator unchanged, even though the def of the constant was *after* the current insertion point. This resulted in the compare being generated *before* the constant which it was using. There's no need to modify the insertion point before building the mul-hi or constant. Delaying moving the insert point ensures those are built/CSEd before the G_ICMP is built. Fixes PR47679 Differential Revision: https://reviews.llvm.org/D88514 (cherry picked from commit 1d54e75cf26a4c60b66659d5d9c62f4bb9452b03)
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The LLVM Getting Started documentation may be out of date.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