08074cc965
This commit sets up the infrastructure for auto-generating <arm_mve.h> and doing clang-side code generation for the builtins it relies on, and demonstrates that it works by implementing a representative sample of the ACLE intrinsics, more or less matching the ones introduced in LLVM IR by D67158,D68699,D68700. Like NEON, that header file will provide a set of vector types like uint16x8_t and C functions with names like vaddq_u32(). Unlike NEON, the ACLE spec for <arm_mve.h> includes a polymorphism system, so that you can write plain vaddq() and disambiguate by the vector types you pass to it. Unlike the corresponding NEON code, I've arranged to make every user- facing ACLE intrinsic into a clang builtin, and implement all the code generation inside clang. So <arm_mve.h> itself contains nothing but typedefs and function declarations, with the latter all using the new `__attribute__((__clang_builtin))` system to arrange that the user- facing function names correspond to the right internal BuiltinIDs. So the new MveEmitter tablegen system specifies the full sequence of IRBuilder operations that each user-facing ACLE intrinsic should translate into. Where possible, the ACLE intrinsics map to standard IR operations such as vector-typed `add` and `fadd`; where no standard representation exists, I call down to the sample IR intrinsics introduced in an earlier commit. Doing it like this means that you get the polymorphism for free just by using __attribute__((overloadable)): the clang overload resolution decides which function declaration is the relevant one, and _then_ its BuiltinID is looked up, so by the time we're doing code generation, that's all been resolved by the standard system. It also means that you get really nice error messages if the user passes the wrong combination of types: clang will show the declarations from the header file and explain why each one doesn't match. (The obvious alternative approach would be to have wrapper functions in <arm_mve.h> which pass their arguments to the underlying builtins. But that doesn't work in the case where one of the arguments has to be a constant integer: the wrapper function can't pass the constantness through. So you'd have to do that case using a macro instead, and then use C11 `_Generic` to handle the polymorphism. Then you have to add horrible workarounds because `_Generic` requires even the untaken branches to type-check successfully, and //then// if the user gets the types wrong, the error message is totally unreadable!) Reviewers: dmgreen, miyuki, ostannard Subscribers: mgorny, javed.absar, kristof.beyls, cfe-commits Tags: #clang Differential Revision: https://reviews.llvm.org/D67161 |
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clang | ||
clang-tools-extra | ||
compiler-rt | ||
debuginfo-tests | ||
libc | ||
libclc | ||
libcxx | ||
libcxxabi | ||
libunwind | ||
lld | ||
lldb | ||
llgo | ||
llvm | ||
openmp | ||
parallel-libs | ||
polly | ||
pstl | ||
.arcconfig | ||
.clang-format | ||
.clang-tidy | ||
.git-blame-ignore-revs | ||
.gitignore | ||
README.md |
README.md
The LLVM Compiler Infrastructure
This directory and its subdirectories contain source code for LLVM, a toolkit for the construction of highly optimized compilers, optimizers, and runtime environments.
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 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.
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 workflow and configuration to get and build the LLVM source:
-
Checkout LLVM (including related subprojects like Clang):
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git clone https://github.com/llvm/llvm-project.git
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Or, on windows,
git clone --config core.autocrlf=false https://github.com/llvm/llvm-project.git
-
-
Configure and build LLVM and Clang:
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cd llvm-project
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mkdir build
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cd build
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cmake -G <generator> [options] ../llvm
Some common 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 subprojects 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
-DLLVM_ENABLE_PROJECTS="clang;libcxx;libcxxabi"
. -
-DCMAKE_INSTALL_PREFIX=directory
--- Specify for directory the full pathname 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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Run your build tool of choice!
-
The default target (i.e.
ninja
ormake
) will build all of LLVM. -
The
check-all
target (i.e.ninja check-all
) will run the regression tests to ensure everything is in working order. -
CMake will generate build 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
, usemake -j NNN
(NNN is the number of parallel jobs, use e.g. 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.