About This Project

This is a fork of the LLVM project where we aim to improve the choice of loop vectorization factors using an explorative approach. The work started as a master's thesis “Determining the Perfect Vectorization Factor” by Anna Welker at the Compiler Design Lab at Saarland University. Because the results were not as good as expected, Nils Husung picked up this project in his bachelor's thesis “Improving the Choice of Vectorization Factors,” also at the Compiler Design Lab.

The main idea is to extract innermost loops into functions and compile them ahead via a clone of the optimization and the code generation pipeline. This is done by the ExplorativeLV pass implemented in llvm/lib/Transforms/Vectorize/ExplorativeLV.cpp. To evaluate the costs, there are three metrics available: a simple one focused on the instruction count (implementation in llvm/lib/CodeGen/MachineCodeExplorer.cpp), one based on llvm-mca and a benchmarking metric. The benchmarking metric automatically infers “valid” inputs for the extracted loops and benchmarks them. The focus of the Bachelor's thesis was on this metric. The MCA metric is currently a little buggy because it does not reliably isolate the assembly of the vectorized loop.

To enable our explorative loop vectorization (XLV) with the benchmarking metric, invoke clang as follows:

clang -O3 -mllvm --xlv-metric=benchmark [<other flags>] <input>

If you are interested in the debugging output, add -mllvm --debug-only=explorative-lv. Also remember to select the appropriate target (e.g. -march=x86-64-v3 for machines with AVX2). The ExplorativeLV pass has lots of configuration options. If you have compiled the opt tool, you can use opt --help-hidden to see all the options. These are prefixed with --xlv. A particularly helpful option for debugging/evaluation purposes might be --xlv-artifacts-dir=<dir>, which places all build artifacts created by the ExplorativeLV pass (optimized IR of the loop functions, benchmarking executable or assembly code with for llvm-mca) in the given directory.

If you are interested in the evaluation of this tool, have a look at https://gitlab.cs.uni-saarland.de/s8nihusu/xlv-evaluation.

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From here on follows the original readme:

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:

1. 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

2. Configure and build LLVM and Clang:

   • 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:

     • -DLLVM_ENABLE_PROJECTS='...' and -DLLVM_ENABLE_RUNTIMES='...' --- semicolon-separated list of the LLVM sub-projects and runtimes you'd like to additionally build. LLVM_ENABLE_PROJECTS can include any of: clang, clang-tools-extra, cross-project-tests, flang, libc, libclc, lld, lldb, mlir, openmp, polly, or pstl. LLVM_ENABLE_RUNTIMES can include any of libcxx, libcxxabi, libunwind, compiler-rt, libc or openmp. Some runtime projects can be specified either in LLVM_ENABLE_PROJECTS or in LLVM_ENABLE_RUNTIMES.

       For example, to build LLVM, Clang, libcxx, and libcxxabi, use -DLLVM_ENABLE_PROJECTS="clang" -DLLVM_ENABLE_RUNTIMES="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). Be careful if you install runtime libraries: if your system uses those provided by LLVM (like libc++ or libc++abi), you must not overwrite your system's copy of those libraries, since that could render your system unusable. In general, using something like /usr is not advised, but /usr/local is fine.

     • -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. ninja or make) 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 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, where NNN is the number of parallel jobs, e.g. the number of CPUs you have.

   • For more information see CMake

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.