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The LLVM Getting Started documentation may be out of date.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='...' --- 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).

   • 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.

Credits and License

This repository is a copy from the original LLVM-project but additionally contains an extension for SPARROW AI acceleration module. More information can be found in the related repositories:

• Main SPARROW repository
• GCC compiler

This project has been developed by Marc Solé Bonet under the supervision of Dr. Leonidas Kosmidis. The work was presented as Master Thesis at the Universitat Politècnica de Catalunya (UPC) for the Master in Innovation and Research in Informatics (MIRI) - High Performance Computing (HPC). This project has been developed while working at the Barcelona Supercomputing Center (BSC).

The Thesis has been awarded by the Spanish Chapter of the IEEE AESS as best Master Thesis. Also, a prior iteration of this project received the first place at the Xilinx Open Hardware 2021 competition in the Student category.

The project is open source under a GPL license. If you use this work or any part of it, please cite it as:

Marc Solé Bonet, Leonidas Kosmidis, SPARROW: A Low-Cost Hardware/Software Co-designed SIMD Microarchitecture for AI Operations in Space Processors. Design, Automation and Test in Europe Conference, DATE 2022