cling

The cling C++ interpreter

4,136 stars C++ #c-plus-plus#clang#interpreter#jupyter#llvm
RAW Doc

File: README.md

Cling - The Interactive C++ Interpreter

The main repository is at https://github.com/root-project/cling

Overview

Cling is an interactive C++ interpreter, built on top of Clang and LLVM compiler
infrastructure. Cling implements the read-eval-print loop
(REPL)
concept, in order to leverage rapid application development. Implemented as a
small extension to LLVM and Clang, the interpreter reuses their strengths such
as the praised concise and expressive compiler diagnostics.

See also cling's web page.

Please note that some of the resources are rather old and most of the stated
limitations are outdated.

Installation

Release Notes

See our release notes to find what's new.

Binaries

Our nightly binary snapshots are currently unavailable.

Building from Source

See also the instructions on the webpage.

Building Cling as a Standalone Project

If Clang and LLVM (cling-latest version) are not installed, you need to build them first:

bash
git clone https://github.com/root-project/llvm-project.git
cd llvm-project
git checkout cling-latest
cd ..
mkdir llvm-build && cd llvm-build
cmake -DLLVM_ENABLE_PROJECTS="clang" -DLLVM_TARGETS_TO_BUILD="host;NVPTX" -DCMAKE_BUILD_TYPE=Release ../llvm-project/llvm
cmake --build .

Once Clang and LLVM (cling-latest version) are installed, you can build Cling. If they are already installed, you can skip the previous step and proceed with the following:

Note: Ensure you are outside the llvm-project and llvm-build directories before proceeding, as LLVM, Clang, and Cling do not allow building inside the source directory.

bash
git clone https://github.com/root-project/cling.git
mkdir cling-build && cd cling-build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH="The root of your LLVM build directory" -DLLVM_DIR="The directory containing LLVM's CMake modules" ../cling
cmake --build .

Example CMake command:

bash
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH=../llvm-build -DLLVM_DIR=../llvm-build/lib/cmake/llvm ../cling

If Clang and LLVM are not installed, you can build them together with Cling:

bash
git clone https://github.com/root-project/llvm-project.git
cd llvm-project
git checkout cling-latest
cd ..
git clone https://github.com/root-project/cling.git
mkdir cling-build && cd cling-build
cmake -DLLVM_EXTERNAL_PROJECTS=cling -DLLVM_EXTERNAL_CLING_SOURCE_DIR=../cling/ -DLLVM_ENABLE_PROJECTS="clang" -DLLVM_TARGETS_TO_BUILD="host;NVPTX" -DCMAKE_BUILD_TYPE=Release ../llvm-project/llvm
cmake --build . --target clang cling

Jupyter notebooks

To enable support for cling in Jupyter notebooks, after building cling, run:

bash
cmake --build . --target libclingJupyter

Usage

Assuming we're in the build folder.

If Cling is built as a standalone project, you need to specify the include directory for headers:

bash
./bin/cling -I"../cling/include" '#include <stdio.h>' 'printf("Hello World!\n");'

If build Cling as part of LLVM:

bash
./bin/cling '#include <stdio.h>' 'printf("Hello World!\n");'

To get started run:

bash
./bin/cling --help

or

bash
./bin/cling
[cling]$ .help

Debugging and Profiling JITted Code

Cling provides support for debugging and profiling interpreted (JITted) code.

  • CLING_DEBUG=1 enables debug symbol emission on interpreted code, allowing
    the use of a standard debugger. Debugging is aided by switching off
    optimisations and adding frame pointers for better stack traces.
  • CLING_PROFILE=1 enables perf profiling:
    • When jitlink is enabled (CLING_JITLINK=1, soon the default), profiling
      requires a perf inject step:
      bash
      perf record -k 1 <cling>
perf inject -j -i perf.data -o perf.jitted.data
perf report -i perf.jitted.data
    • When jitlink is disabled, perf support is enabled with "perf map" (legacy)
      instead of "JIT dump" and there is no need for an inject step.

Debugging and Profiling, both have a runtime cost, and is therefore disabled by
default.

Jupyter

Cling comes with a Jupyter kernel. After building cling,
install Jupyter and cling's kernel by following the README.md in
tools/Jupyter. Make sure cling is in your PATH when you start jupyter!

Citing Cling

latex
% Peer-Reviewed Publication
%
% 19th International Conference on Computing in High Energy and Nuclear Physics (CHEP)
% 21-25 May, 2012, New York, USA
%
@inproceedings{Cling,
  author = {Vassilev,V. and Canal,Ph. and Naumann,A. and Moneta,L. and Russo,P.},
  title = {{Cling} -- The New Interactive Interpreter for {ROOT} 6}},
  journal = {Journal of Physics: Conference Series},
  year = 2012,
  month = {dec},
  volume = {396},
  number = {5},
  pages = {052071},
  doi = {10.1088/1742-6596/396/5/052071},
  url = {https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071/pdf},
  publisher = {{IOP} Publishing}
}

Developers' Corner

Cling's latest doxygen documentation

Contributions

Every contribution is considered a donation and its copyright and any other
related rights become exclusive ownership of the person who merged the code or
in any other case the main developers of the "Cling Project".

We warmly welcome external contributions to the Cling! By providing code,
you agree to transfer your copyright on the code to the "Cling project".
Of course you will be duly credited and your name will appear on the
contributors page, the release notes, and in the CREDITS file
shipped with every binary and source distribution. The copyright transfer is
necessary for us to be able to effectively defend the project in case of
litigation.

License

Please see our LICENSE.

Releases

Our release steps to follow when cutting a new release:

  1. Update release notes
  2. Remove ~dev suffix from VERSION
  3. Add a new entry in the news section of our website
  4. Commit the changes.
  5. git tag -a v0.x -m "Tagging release v0.x"
  6. Tag cling-patches of clang.git:
    git tag -a cling-v0.x -m "Tagging clang for cling v0.x"
  7. Create a draft release in github and copy the contents of the release notes.
  8. Wait for green builds.
  9. Upload binaries to github (Travis should do this automatically).
  10. Publish the tag and announce it on the mailing list.
  11. Increment the current version and append ~dev.

File: docs/chapters/applications.rst

Applications

  1. C++ in Jupyter Notebook - Xeus Cling:

The Jupyter Notebook <https://jupyter.org/>_ technology allows users to create
and share documents that contain live code, equations, visualizations and
narrative text. It enables data scientists to easily exchange ideas or
collaborate by sharing their analyses in a straight-forward and reproducible
way. Jupyter’s official C++ kernel(Xeus-Cling <https://github.com/jupyter-xeus/xeus-cling>) relies on Xeus, a C++
implementation of the kernel protocol, and Cling. Using C++ in the Jupyter
environment yields a different experience to C++ users. For example, Jupyter’s
visualization system can be used to render rich content such as images,
therefore bringing more interactivity into the Jupyter’s world. You can find
more information on Xeus Cling's Read the Docs <https://xeus-cling.readthedocs.io/en/latest/> webpage.

  1. Interactive CUDA C++ with Cling:

CUDA <https://blogs.nvidia.com/blog/2012/09/10/what-is-cuda-2/>_ is a platform
and Application Programming Interface (API) created by NVIDIA <https://www.nvidia.com/en-us/>. It controls GPU <https://en.wikipedia.org/wiki/Graphics_processing_unit> (Graphical Processing
Unit) for parallel programming, enabling developers to harness the power of
graphic processing units (GPUs) to speed up applications. As an example,
PIConGPU <https://github.com/ComputationalRadiationPhysics/picongpu>_ is a
CUDA-based plasma physics application to solve the dynamics of a plasma by
computing the motion of electrons and ions in the plasma field. Interactive GPU
programming was made possible by extending Cling functionality to compile CUDA
C++ code. The new Cling-CUDA C++ can be used on Jupyter Notebook platform, and
enables big, interactive simulation with GPUs, easy GPU development and
debugging, and effective GPU programming learning.

  1. Clad:

Clad <https://compiler-research.org/clad/>_ enables automatic differentiation
(AD) for C++. It was first developed as a plugin for Cling, and is now a plugin
for Clang compiler. Clad is based on source code transformation. Given C++
source code of a mathematical function, it can automatically generate C++ code
for computing derivatives of the function. It supports both forward-mode and
reverse-mode AD.

  1. Cling for live coding music and musical instruments:

The artistic live coding community has been growing steadily since around the
year 2000. The Temporary Organisation for the Permanence of Live Art Programming
(TOPLAP) has been around since 2004, Algorave (algorithmic rave parties)
recently celebrated its tenth birthday, and six editions of the International
Conference on Live Coding (ICLC) have been held. A great many live coding
systems have been developed during this time, many of them exhibiting exotic and
culturally specific features that professional software developers are mostly
unaware of. In this framework, Cling has been used as the basis for a C++ based
live coding synthesiser (TinySpec-Cling <https://github.com/nwoeanhinnogaehr/tinyspec-cling>). In another example,
Cling has been installed on a BeagleBoard to bring live coding to the Bela
interactive audio platform (Using the Cling C++ Interpreter on the Bela Platform <https://gist.github.com/jarmitage/6e411ae8746c04d6ecbee1cbc1ebdcd4>). These
two examples show the potential mutual benefits for increased engagement between
the Cling community and the artistic live coding community.

  1. Clion: The CLion <https://www.jetbrains.com/clion/>_ platform is a
    Integrating Development Environment (`IDE

https://en.wikipedia.org/wiki/Integrated_development_environment_) for C and C++ by JetBrains https://www.jetbrains.com/_. It was developed with the aim to enhance developer's productivity with a smart editor, code quality assurance, automated refactorings and deep integration with the CMake build system. CLion integrates Cling, which can be found by clicking on Tool. Cling enables prototyping and learning C++ in CLion. You can find more information on CLion's
building instructions
https://www.jetbrains.com/help/clion/cling-integration.html#install-cling`_.

File: docs/chapters/background.rst

When and why was Cling developed?

Cling was first released in 2014 as the interactive, C++ interpreter in
ROOT. ROOT <https://root.cern/>_ is an open-source program written primarily
in C++, developed by research groups in high-energy physics including CERN <https://home.cern/>, FERMILAB <https://www.fnal.gov/> and Princeton <https://www.princeton.edu/>. ROOT is nowadays used by most high-energy
physics experiments. CERN is an European research organization that operates the
largest particle physics laboratory in the world. Its experiments collect
petabytes of data per year to be serialized, analyzed, and visualized as C++
objects. In this framework, Cling was developed with the aim to facilitate the
processing of scientific data in the field of high-energy physics . Cling is a
core component of ROOT: it provides essential functionality for the analysis of
vast amounts of very complex data produced by the experimental high-energy
physics community by enabling (1) interactive exploration in C++, (2) dynamic
interoperability (see cppyy <https://cppyy.readthedocs.io/en/latest/>, an
automatic, runtime Python/C++ binder), and (3) rapid prototyping capabilities.

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