OpenMP Record Replay Reproducibility

This repository contains all source code required to reproduce the experiments of our work. It contains LLVM extensions, OpenMP offload runtime record-replay mechanism and all the required scripts to perform the analysis of our work.

Requirements

• Ninja
• CMake@3.20 or CMake@3.23
• cuda@11.6.1 (For NVIDIA systems)
• ROCm@5.4.3 (For AMD systems)
• GCC@8.3.1 or GCC@10.3.1 (For NVIDIA)
• GCC@10.3.1 (For AMD)
• python@3.9.12
• pip@23.2.1

Installation

We provide an installation script setup.sh the remaining requirements of our experimentation using spack. The script will clone spack in the root directory create an environment and install all necessary packages. Once everything is installed it moves forward to install the provided Clang/LLVM. Below you can see a snippet of code invoking the setup.sh script

In our Power 9 system the setup script takes a little less than 3 hours.

module load gcc/8.3.1
module load ninja
module load cmake/3.20.1
module load cuda/11.6.1
./setup.sh nvidia 40
...

The first argument of setup.sh describes the GPU vendor so it takes the values of nvidia or amd (lower case letters). The second value takes as an argument the number of threads to be used for the installation. Once the installation finishes please source the created environment file env_${SUFFIX}.sh. SUFFIX can take the value of either nvidia or amd. The scripts must be sourced every time we start the record-replay mechanism.

Record-Replay optimizations

To continue with our reproducibility you can follow the instructions under this README

Contributors

The source code extensions written on top of Clang/LLVM were performed by Konstantinos Parasyris , Giorgis Georgakoudis, Johannes Doerfert.

The LLVM Compiler Infrastructure

This directory and its sub-directories contain the 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 here.

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 frontend. 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 to run. In most cases, you get the best performance if you specify the number of CPU threads you have. On some Unix systems, you can specify this with -j$(nproc).

   • 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.

Getting in touch

Join LLVM Discourse forums, discord chat or #llvm IRC channel on OFTC.

The LLVM project has adopted a code of conduct for participants to all modes of communication within the project.