f26deb4e6b
The new component is a symmetric response to SymbolicRangeInferrer. While the latter is the unified component, which answers all the questions what does the solver knows about a particular symbolic expression, assignor associates new constraints (aka "assumes") with symbolic expressions and can imply additional knowledge that the solver can extract and use later on. - Why do we need it and why is SymbolicRangeInferrer not enough? As it is noted before, the inferrer only helps us to get the most precise range information based on the existing knowledge and on the mathematical foundations of different operations that symbolic expressions actually represent. It doesn't introduce new constraints. The assignor, on the other hand, can impose constraints on other symbols using the same domain knowledge. - But for some expressions, SymbolicRangeInferrer looks into constraints for similar expressions, why can't we do that for all the cases? That's correct! But in order to do something like this, we should have a finite number of possible "similar expressions". Let's say we are asked about `$a - $b` and we know something about `$b - $a`. The inferrer can invert this expression and check constraints for `$b - $a`. This is simple! But let's say we are asked about `$a` and we know that `$a * $b != 0`. In this situation, we can imply that `$a != 0`, but the inferrer shouldn't try every possible symbolic expression `X` to check if `$a * X` or `X * $a` is constrained to non-zero. With the assignor mechanism, we can catch this implication right at the moment we associate `$a * $b` with non-zero range, and set similar constraints for `$a` and `$b` as well. Differential Revision: https://reviews.llvm.org/D105692 |
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| .github | ||
| clang | ||
| clang-tools-extra | ||
| compiler-rt | ||
| cross-project-tests | ||
| flang | ||
| libc | ||
| libclc | ||
| libcxx | ||
| libcxxabi | ||
| libunwind | ||
| lld | ||
| lldb | ||
| llvm | ||
| mlir | ||
| openmp | ||
| parallel-libs | ||
| polly | ||
| pstl | ||
| runtimes | ||
| utils | ||
| .arcconfig | ||
| .arclint | ||
| .clang-format | ||
| .clang-tidy | ||
| .git-blame-ignore-revs | ||
| .gitignore | ||
| .mailmap | ||
| CONTRIBUTING.md | ||
| README.md | ||
| 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.
Overview
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:
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Checkout LLVM (including related sub-projects like Clang):
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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
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-
Configure and build LLVM and Clang:
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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:
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-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 cross-project-tests.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).
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cmake --build build [-- [options] <target>]or your build system specified above directly.-
The default target (i.e.
ninjaormake) will build all of LLVM. -
The
check-alltarget (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, whereNNNis the number of parallel jobs, e.g. the number of CPUs you have.
-
-
For more information see CMake
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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.