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Interactive C++ with Cling

Interactive C++ with Cling

The C++ programming language is used for many numerically intensive scientific applications. A combination of performance and solid backward compatibility has led to its use for many research software codes over the past 20 years. Despite its power, C++ is often seen as difficult to learn and inconsistent with rapid application development. Exploration and prototyping is slowed down by the long edit-compile-run cycles during development.

Cling has emerged as a recognized capability that enables interactivity, dynamic interoperability and rapid prototyping capabilities to C++ developers. Cling supports the full C++ feature set including the use of templates, lambdas, and virtual inheritance. Cling is an interactive C++ interpreter, built on top of the Clang and LLVM compiler infrastructure. The interpreter enables interactive exploration and makes the C++ language more welcoming for research.

The main tool for storage, research and visualization of scientific data in the field of high energy physics (HEP) is the specialized software package ROOT. ROOT is a set of interconnected components that assist scientists from data storage and research to their visualization when published in a scientific paper. ROOT has played a significant role in scientific discoveries such as gravitational waves, the great cavity in the Pyramid of Cheops, the discovery of the Higgs boson by the Large Hadron Collider. For the last 5 years, Cling has helped to analyze 1 EB physical data, serving as a basis for over 1000 scientific publications, and supports software run across a distributed million CPU core computing facility.

Recently we started a project aiming to leverage our experience in interactive C++, just-in-time compilation technology (JIT), dynamic optimizations, and large scale software development to greatly reduce the impedance mismatch between C++ and Python. We will generalize Cling to offer a robust, sustainable and omnidisciplinary solution for C++ language interoperability.The scope of our objectives is to:

  • advance the interpretative technology to provide a state-of-the-art C++ execution environment,
  • enable functionality which can provide native-like, dynamic runtime interoperability between C++ and Python (and eventually other languages such as Julia and Swift)
  • allow seamless utilization of heterogeneous hardware (such as hardware accelerators)

Project results will be integrated into the widely used tools LLVM, Clang and Cling. The outcome of the proposed work is a platform which provides a C++ compiler as a service (CaaS) for both rapid application development and computational performance.

The rest of this post intends to demonstrate the design and several features of Cling. Want to follow along? You can get cling from conda

conda config --add channels conda-forge
conda install cling
conda install llvmdev=5.0.0

or from docker-hub if you don’t already use conda:

docker pull compilerresearch/cling
docker run -t -i compilerresearch/cling

Either way, type “cling” to start its interactive shell:

****************** CLING ******************
* Type C++ code and press enter to run it *
*             Type .q to exit             *
[cling]$ #include "cling/Interpreter/Interpreter.h"
[cling]$ gCling->allowRedefinition(false)

We will explain the purpose for these commands, and other alternatives for using cling in further parts of this post.

Interpreting C++

Exploratory programming (or Rapid Application Development) is an effective way to gain understanding of the requirements for a project; to reduce the complexity of the problem; and to provide an early validation of the system design and implementation. In particular, interactively probing data and interfaces makes complex libraries and complex data more accessible to users. It is important in data science, computational science and debugging. It significantly reduces the time consumed by edit-run cycles during development. In practice, only few programming languages offer both a compiler and an interpreter translating them into machine code, although whether a language is to be interpreted or compiled is a property of the implementation.

Languages which enable exploratory programming tend to have interpreters which shorten the compile-link cycle; this generally has a noticeable cost in performance. Language developers who acknowledge the use case of exploratory programming may also put syntactic sugar, but that is mostly for convenience and terseness. The performance penalty is largely mitigated by using just-in-time (JIT) or ahead-of-time (AOT) compilation technology.

For the sake of this post series, interpreting C++ means enabling exploratory programming for C++ while mitigating the performance cost with JIT compilation. Figure 1 shows an illustrative example of exploratory programming. It becomes trivial to orient the shape, choose size and color or compare to previous settings. The invisible compile-link cycle aids interactive use which allows some qualitatively different approaches to program development and enhanced productivity.

Figure 1. Interactive OpenGL Demo, adapted from [here](

Design principles

Some of the design goals of cling include:

  • Do not pay for what you do not use – prioritize performance of processing correct code. For example, in order to provide error recovery do not penalize users typing syntactically and semantically correct C++; and interactive C++ transformations are only done when necessary and can be disabled.
  • Reuse Clang & LLVM at (almost) any cost – do not reinvent the wheel. If a feature is not available, then try finding a minimalistic way to implement it and propose it for a review to the LLVM community. Otherwise find the minimal patch, even at the cost of misusing API, which satisfies the requirements.
  • Continuous feature delivery – focus on a minimal feature, its integration in the main use-case (ROOT), deployment in production, repeat.
  • Library design – allow Cling to be used as a library from third party frameworks.
  • Learn and evolve – experiment with user experience. There is no formal specification or consensus on overall user experience. Apply lessons learned from the legacy from CINT.


Cling accepts partial input and ensures that the compiler process keeps running to act on code as it comes in. It includes an API providing access to the properties of recently compiled chunks of code. Cling can apply custom transformations to each chunk before execution. Cling orchestrates the existing LLVM and Clang infrastructure following a data flow described in Figure 2.

Figure 2. Information flow in Cling

In short:
  1. The tool controls the input infrastructure by interactive prompt or by an interface allowing the incremental processing of input (➀).
  2. It sends the input to the underlying clang library for compilation (➁).
  3. Clang compiles the input, possibly wrapped into a function, into an AST (➂).
  4. When necessary the AST is further transformed in order to attach specific behavior (➃).

For example, reporting execution results, or other interpreter-related features. Once the high-level AST representation is ready, it is sent for lowering to an LLVM-specific assembly format, the LLVM IR (➄). The LLVM IR is the input format for LLVM’s just-in-time compilation infrastructure. Cling instructs the JIT to run specified functions (➅), translating them into machine code (MC) targeting the underlying device architecture (eg. Intel x86 or NVPTX) (➆,➇).

The C++ standard is developed towards compilers and does not cover interactive use well. Execution of statements on the global scope, reporting execution results, and entity redefinitions are the three most important features when it comes to user friendliness. Long running interpreter sessions are prone to typing errors and make flawless error recovery essential. More advanced use-cases require extra flexibility at runtime and lookup rules extensions aiding eval-style programming. Efficient watermark-based code removal is important when C++ is used as scripting language.

Execution of statements

Cling processes C++ incrementally. Incremental input consists of one or multiple C++ statements. C++ does not allow expressions in the global scope.

[cling] #include <vector>
[cling] #include <iostream>
[cling] std::vector<int> v = {1,2,3,4,5}; v[0]++;
[cling] std::cout << "v[0]=" << v[0] <<"\n";

Instead, Cling moves each input into a unique wrapper function. Eg:

void __unique_1 () { std::vector<int> v = {1,2,3,4,5};v[0]++;; } // #1
void __unique_2 () { std::cout << "v[0]=" << v[0] <<"\n";; } // #2

After the clang AST is built, cling detects that wrapper #1 contains a declaration and moves the declaration’s AST node to the global scope, such that v can be referenced by subsequent inputs. Wrapper #2 contains a statement and is executed as is. Internally to Cling, the example is transformed to:

#include <vector>
#include <iostream>
std::vector<int> v = {1,2,3,4,5};
void __unique_1 () { v[0]++;; }
void __unique_2 () { std::cout << "v[0]=" << v[0] <<"\n";; }

Cling runs these wrappers after they are compiled to machine code.

Reporting execution results

An integral part of interactivity is printing expression values. Typing printf each time is laborious and does not naturally include object type information. Instead, omitting the semicolon of the last statement of the input tells Cling to report the expression result. When wrapping the input, Cling textually attaches a semicolon to the end of it. If an execution report is requested the corresponding wrapper AST does not contain a NullStmt (modelling extra semicolons).

[cling] #include <vector>
[cling] std::vector<int> v = {1,2,3,4,5} // Note the missing semicolon
(std::vector<int> &) { 1, 2, 3, 4, 5 }

A transformation injects extra code depending on the properties of the particular entity such as if it is copyable, if it is a wrapper temporary or an array. Cling can report information about non-copyable or temporary objects by providing a ‘managed’ storage. The managed storage (cling::Value) is also used for exchanging values between interpreted and compiled code in embedded setup.

Entity Redefinition

Name redefinition is an important scripting feature. It is also essential for notebook-based C++ as each cell is a somewhat separate computation. C++ does not support redefinitions of entities.

[cling] #include <string>
[cling] std::string v
(std::string &) ""
[cling] #include <vector>
[cling] std::vector<int> v
input_line_7:2:19: error: redefinition of 'v' with a different type: 'std::vector<int>' vs 'std::string' (aka 'basic_string<char, char_traits<char>, allocator<char> >')
 std::vector<int> v
input_line_4:2:14: note: previous definition is here
 std::string v

Cling implements entity redefinition using inline namespaces and rewires clang lookup rules to give higher priority to more recent declarations. The full description of this feature was published as a conference paper on CC 2020 (ACM conference on Compiler Construction). We enable it by calling gCling->allowRedefinition():

[cling] #include "cling/Interpreter/Interpreter.h"
[cling] gCling->allowRedefinition()
[cling] #include <vector>
[cling] std::vector<int> v
(std::vector<int> &) {}
[cling] #include <string>
[cling] std::string v
(std::string &) ""

Invalid Code. Error Recovery

When used in interactive mode, invalid C++ does not terminate the session. Instead invalid code is discarded. The underlying clang process keeps the invalid AST nodes in its internal data structures for better error diagnostics and recovery, expecting the process will end shortly after issuing the diagnostics. This particular example is more challenging because it first contains both valid and invalid constructs. The error recovery should undo a significant amount of changes in internal structures such as the name lookup and the AST. Cling is used in many high-performance environments; using checkpointing is not a viable option as it introduces overhead for correct code.

[cling] #include <vector>
[cling] std::vector<int> v; v[0].error_here;
input_line_4:2:26: error: member reference base type 'std::__1::__vector_base<int, std::__1::allocator<int> >::value_type' (aka 'int') is not a structure or union
 std::vector<int> v; v[0].error_here;

In order to handle the example, Cling models the incremental input into a Transaction. A transaction represents the delta of the changes of internal data structures of Clang. Cling listens to events coming from various Clang callbacks such as declaration creation, deserialization and macro definition. This information is sufficient to undo the changes and continue with a valid state. The implementation is very intricate and in many cases requires extra work depending on the input declaration kinds.

Cling also protects against null pointer dereferences via a code transformation, avoiding a session crash.

[cling] int *p = nullptr; *p
input_line_3:2:21: warning: null passed to a callee that requires a non-null argument [-Wnonnull]
 int *p = nullptr; *p

The implementation of error recovery and code unloading still has rough edges and it is being improved constantly.

Code Removal

Incremental, interactive C++ assumes long lived sessions where not only syntax error can happen but also semantic ones. That poses one level of extra complexity if we want to re-execute the same code with minor adjustments.

[cling] .L Adder.h // #1, similar to #include "Adder.h"
[cling] Add(3, 1) // int Add(int a, int b) {return a - b; }
(int) 2
[cling] .U Adder.h // reverts the state prior to #1
[cling] .L Adder.h
[cling] Add(3, 1) // int Add(int a, int b) {return a + b; }
(int) 4

In the example, we include a header file with the .L meta command; “uninclude” it with .U and “reinclude” it with .L to re-read the modified file. Unlike in the error recovery case, Cling cannot fence the machine code lowering infrastructure and needs to undo state changes in clang CodeGen and the llvm JIT and machine code infrastructure. The implementation of this feature requires expertise in a big portion of the LLVM toolchain.


Cling has been one of the systems enabling interactive C++ for more than a decade. Cling’s extensibility and fast prototyping features is of fundamental importance for researchers in high-energy physics, and an enabler for many of the technologies that they rely on. Cling has several unique features tailored to the challenges which come with incremental C++. Our work on interactive C++ is always evolving. In the next blog post we will focus on interactive C++ for Data Science; Eval-Style Programming; Interactive CUDA; and C++ in notebooks.

You can find out more about our activities at and


The author would like to thank Sylvain Corlay, Simeon Ehrig, David Lange, Chris Lattner, Javier Lopez Gomez, Wim Lavrijsen, Axel Naumann, Alexander Penev, Xavier Valls Pla, Richard Smith, Martin Vassilev, who contributed to this post.