An introduction to the build pipeline, and how we can orchestrate it manually

Before working with CMake, it's useful to understand the tools it orchestrates. This lesson guides you through setting up a C++ toolchain.

Building C++ Programs

Walking through the steps of the C++ compilation process, from source code to machine code.

The Compilation Pipeline

The linker's role in the build process, how it resolves symbols to combine object files, and how to troubleshoot common errors.

Object Files and the Linking Process

The difference between static and dynamic libraries, how to create them, and the trade-offs between them.

Static and Shared Libraries

The C++ Build Process

Recommended approaches for managing large projects and the libraries that they depend on

Learn how to structure your C++ projects for scalability and maintainability, from basic directory layouts to managing multiple modules.

Organizing a C++ Project

Learn to manage third-party libraries in C++, covering include/library paths, static vs. shared libraries, versioning, and platform differences.

Managing Libraries and Dependencies

An introduction to traditional build tools like Makefiles and IDE projects, highlighting the maintenance, scalability, and cross-platform challenges that result

Automated Build Systems and Their Limitations

Project Structure and Dependency Management

Setting up CMake and creating your first project

An introduction to CMake, the cross-platform, open-source meta-build system that solves the core challenges of C++ project management.

Why CMake?

A step-by-step guide to installing CMake. We'll explore command-line, GUI, and IDE workflows, and how to configure your compiler.

Setting Up a CMake Environment

Creating the bare minimum `CMakeLists.txt` file and build up to a project with an executable and a library, learning the fundamental commands along the way.

Writing a CMakeLists File

Learn the two-stage process of building a CMake project. This lesson covers configuring, generating, building, running, and troubleshooting common errors.

Building and Running CMake Projects

Learn how to organize large C++ projects in CMake using subdirectories and the `add_subdirectory()` command to create modular, maintainable builds.

CMake Project Structure and Subdirectories

Introducing CMake

Exploring CMake's programming features, caching mechanisms, and interactions with the file system

A primer on the fundamental building blocks of the CMake language: variables, string interpolation, and logging messages.

CMake Variables and Logging

Discover the CMake Cache, the mechanism for storing persistent user-configurable options, and learn how to create and manage cached variables.

The CMake Cache

Exploring CMake's command syntax and its list data type, including how to create, manipulate, and use lists in your projects.

CMake Commands, Arguments, and Lists

Transform your static build descriptions into dynamic scripts with CMake's control flow commands, if() and foreach().

CMake Conditionals and Loops

Exploring CMake's `file()` command, covering how to read, write, copy, and glob files, and how to manipulate paths.

Working with the File System

Learn to create reusable logic with CMake functions and macros, and how to organize them into modules for clean, scalable build systems.

Modularizing CMake Code

CMake Language and Fundamentals

Learn more about targets, and how their properties and links are the foundation of modern CMake

Learn to work with target properties - understanding their purpose, setting and updating them, and retrieving them when your build requires it

Target Properties

Learn about target types, and how the `PUBLIC`, `PRIVATE`, and `INTERFACE` keywords control how properties are shared.

Relationships Between Targets

Discover how CMake's `target_link_libraries()` command builds a dependency graph, and how it uses this graph to automatically manage transitive dependencies and link order.

Indirect Relationships and the Dependency Graph

Learn to use abstract target types like `INTERFACE`, `ALIAS`, and `IMPORTED` to model complex project needs, organize build properties, and integrate pre-compiled binaries.

Using `INTERFACE`, `ALIAS`, and `IMPORTED` Libraries

Adding support for user-configurable library types and an initial introduction to target installation.

Using Shared Libraries

Targets and Dependencies

Learn how to manage multiple build configurations within the same project, and how to create scripts that adapt to the environment

Learn how CMake manages different build configurations like Debug and Release, the difference between generator types, and how to apply settings conditionally using modern techniques.

Build Configurations (Debug, Release, etc.)

Learn the tools and patterns needed to write portable CMakeLists.txt files that work seamlessly across platforms and compilers

Cross-Platform Configurations

Learn to write architecture-aware CMake scripts for 32/64-bit systems and Apple's Universal Binaries (Intel/ARM).

Handling Different Architectures

Learn to build for different operating systems and architectures using CMake's cross-compilation support and toolchain files.

Cross-Compilation and Toolchain Files

Platforms and Build Configurations

Learn how to add external libraries to our build, and how to create our own packages for others to use

Learn to find and use pre-installed libraries with `find_package()`, understanding Module vs. Config mode and the power of imported targets

Finding Dependencies using `find_package()`

Learn how to make your libraries consumable by other projects using CMake by turning your build-tree into a distributable install-tree.

Creating a Consumable Package

Learn how to consume a CMake package you've installed, completing the producer-consumer cycle. We'll cover using `find_package()` and how to point CMake to your library's location

Using Installed Packages

Learn how to create self-contained, reproducible builds by fetching dependencies from source using `FetchContent()` and `ExternalProject_Add()`.

Fetching External Dependencies

Creating and Consuming Packages

Integrating the vcpkg and Conan package managers to handle external dependencies

Learn to manage C++ dependencies effortlessly with the vcpkg package manager and its seamless CMake integration using toolchain files and manifests.

Integrating CMake and vcpkg

Learn the day-to-day commands for managing dependencies with vcpkg and how to guarantee reproducible builds using baselines and version overrides

vcpkg Commands and Versioning

Combine vcpkg with CMake's cross-compilation capabilities. Learn to use triplets to target different architectures and chain-load toolchain files

Cross-Compiling with vcpkg

Discover Conan, the binary-first C++ package manager, and learn how to integrate it with CMake using profiles and lockfiles for fast, reproducible builds.

Using Conan with CMake

Learn to cross-compile C++ projects with Conan, using build and host profiles to manage dependencies for different platforms.

Cross-Compilation with Conan

Package Managers

Introducing presets, allowing us to simplify our workflow and share common configurations across our team

Learn the fundamentals of CMake Presets. This lesson introduces `CMakePresets.json` to simplify complex build commands.

CMake Presets

Learn to structure your CMake presets with inheritance and customize them for your local environment using `CMakeUserPresets.json`.

User Presets and Inheritance

Learn to make your CMake presets portable by using environment variables and conditional logic to adapt to different machine configurations.

Environment Variables and Conditional Presets

Learn how to extend the build process beyond simple compilation by integrating external tools and custom commands

Learn how to use generator expressions `$<...>` for build-time conditional logic.

Generator Expressions and Conditional Logic

Learn how to attach commands to specific points in a target's build lifecycle using `add_custom_command(TARGET)` for tasks like post-build file copying.

Integrating Tools with `add_custom_command()`

Learn to integrate code generators like Protobuf into your build process using `add_custom_command()`, creating automated file-level dependencies in CMake.

Generating Files with Protobuf and `add_custom_command()`

Custom Commands

An introduction to automating common workflow chores by creating custom targets

Learn to create standalone, non-code targets using add_custom_target() for utility tasks like code formatting and documentation generation.

Custom Targets and Build Utilities

Using the Clang-Format tool to automatically enforce a consistent coding style, and integrating it into our build using a custom CMake target

Using Clang-Format

An introduction to file sets and the `target_sources()` command, allowing us to manage our header files in a more controlled way

Using File Sets

A step-by-step guide to integrating Doxygen into your CMake build process for automated C++ documentation.

Generating Documentation with Doxygen

Custom Targets

Integrate automated tests into a CMake project using CTest, GoogleTest, and GoogleMock

Learn the fundamentals of automated testing in C++ with CTest. Create your first test suite, register tests with `add_test()`, and run them with the `ctest` command.

Automated Testing and CTest

Learn to refactor manual C++ tests into a professional test suite using the GoogleTest framework, managed by vcpkg and integrated with CTest.

Integrating GoogleTest with CMake

Learn to use a wide range of assertions, write data-driven parameterized tests, and isolate dependencies with Google Mock.

Assertions and Parameterized Tests

Learn how to isolate dependencies that our project uses, and then how to replace those dependencies at test time using Google Mock.

Dependency Injection and Mocking

Learn the standard pattern for testing application logic by refactoring it into a linkable library

Testing Executables

Move beyond individual tests to manage a full test suite. Learn to organize tests, run tests in parallel, filter by labels, and measure our code coverage

Managing a Test Suite

Automated Testing

Using static and dynamic analysis in CMake projects to automatically detect memory bugs, performance issues, and problematic code

Learn to find memory bugs at runtime by integrating AddressSanitizer (ASan) into a CMake project

Using AddressSanitizer (ASan)

Adding benchmarks to our build to measure the performance of our code, and track how it changes over time

Using Google Benchmark

Integrating Clang-Tidy to enforce good practices and find bugs before we even compile

Using Clang-Tidy

Automated Analysis

Automating the entire development lifecycle using Git for souce control, GitHub Actions for cloud builds, and CPack to create distributable packages and installers.

An introduction to Git, the version control system that underpins modern collaborative software development and automated build pipelines.

Source Control with Git

Learn how to automate your build and test process with Continuous Integration (CI) and a build server. This lesson provides a guide to creating a GitHub Actions workflow for a CMake project.

Build Servers and Continuous Integration

Learn to package your C++ projects using CMake's CPack. This lesson covers creating developer ZIP archives and professional Windows MSI installers with components.

Packaging with CPack

Learn to automate the packaging process by integrating CPack with a multi-platform GitHub Actions workflow

Packaging with GitHub Actions

Learn to automate the creation of a professional Windows installer by integrating CPack, the WiX Toolset, and a multi-platform GitHub Actions workflow.

Creating Installers with CPack and GitHub Actions

Learn to automate the release process using a GitHub Actions workflow that builds, packages, and publishes artifacts to a GitHub Release

Deployment with GitHub Releases

Continuous Integration and Packaging

If you have a basic grasp of C++ but find the build process intimidating, this course is for you. We assume you know the language, but not how to manage large projects.

Manage complex, cross-platform builds using industry-standard tools. Create scalable build systems that automate the entire development lifecycle.

This course is a comprehensive deep-dive into the entire C++ build ecosystem, centered around the power and flexibility of modern CMake. We leave no stone unturned.

Start with a foundational understanding of compilers, linkers, static vs. shared libraries, and object files.

The Full Build Pipeline

Learn every aspect of the CMake language, from variables and control flow to functions, modules, and generator expressions.

Complete CMake Language Mastery

Master the modern target-centric paradigm, including properties, usage requirements, and abstract targets.

Advanced Target Management

Explore every method for handling dependencies, from `find_package` and `FetchContent` to vcpkg and Conan, including versioning and cross-compilation.

In-Depth Dependency Management

Integrate a full suite of professional tools for testing (CTest, GoogleTest), analysis (ASan, Clang-Tidy), and packaging (CPack).

Total Workflow Automation

Build a fully automated deployment pipeline that takes your code from a Git push to a published GitHub Release with multi-platform installers.

End-to-End CI/CD and Deployment

What You'll Learn

Become a software engineer with C++.  Starting from the basics, we guide you step by step along the way

Intro to C++ Programming

If you're completely new to programming or want a thorough introduction, consider starting with our free introductory course:

New to Programming?

Managing C++ Projects Using CMake

Our tests tell us if our code is correct, but not if it's fast. To measure performance, we use a **benchmark**. A benchmark is a program designed to run a piece of code repeatedly under controlled conditions to produce stable and comparable performance metrics.


Specifically, we'll focus on **micro-benchmarks**.  A micro-benchmark is an automated script that measures the performance of a specific part of our program - perhaps a single function or algorithm.


**Google Benchmark** is the de-facto standard for C++ micro-benchmarking.  It is a library that provides a framework for quickly creating these benchmarks, and it takes care of details like running the code enough times to get a stable result and preventing the compiler from optimizing the code away.


## Integrating Google Benchmark


First, let's add `benchmark` to our `vcpkg.json` manifest.


<CodeFile path="vcpkg.json" status="Updated">


```json
{
  "name": "greeter",
  "dependencies": [
    "gtest",
    "spdlog",
    "benchmark"//<h>
  ]
}
```


</CodeFile>


Next, we'll create a new `benchmarks/` directory for our benchmark code and its `CMakeLists.txt`.


<CodeFile path="benchmarks/CMakeLists.txt" status="New">


```c++
#<cmake>
cmake_minimum_required(VERSION 3.23)

find_package(benchmark CONFIG REQUIRED)

add_executable(GreeterBenchmarks bench_main.cpp)

target_link_libraries(GreeterBenchmarks PRIVATE
  GreeterLib
  benchmark::benchmark
)
```


</CodeFile>


Finally, we add this new directory to our root `CMakeLists.txt`:


<CodeFile path="CMakeLists.txt" status="Updated">


```c++
#<cmake>
cmake_minimum_required(VERSION 3.23)
project(Greeter)

include(cmake/Coverage.cmake)
include(cmake/Sanitize.cmake)

add_subdirectory(app)
add_subdirectory(greeter)

enable_testing()
add_subdirectory(tests)

add_subdirectory(benchmarks) #<h>
```


</CodeFile>


## Writing a Benchmark


A benchmark looks very similar to a GoogleTest case. Let's write one in `benchmarks/bench_main.cpp` to measure our `Greeter::greet()` method.


<CodeFile path="benchmarks/bench_main.cpp" status="New">


```c++
#include <benchmark/benchmark.h>
#include <greeter/Greeter.h>

static void BM_Greeter_Greet(benchmark::State& state) {
  Greeter g;
  // This loop is the core of the benchmark
  for (auto _ : state) {
    // This code gets timed
    std::string result = g.greet();
    // Prevent the result from being optimized away
    benchmark::DoNotOptimize(result);
  }
}

// Register the function as a benchmark
BENCHMARK(BM_Greeter_Greet);

// Run all benchmarks
BENCHMARK_MAIN();
```


</CodeFile>


## Running the Benchmark


To get useful benchmarking results, we should build our project in "Release" mode.  This prevents any debugging helpers from degrading our performance.


Let's add some presets for this, if we haven't already:


<CodeFile path="CMakePresets.json" status="Updated">


```json
{
  "version": 3,
  "configurePresets": [
    // ... other presets
    {
      "name": "release",
      "inherits": "default",
      "cacheVariables": {
        "CMAKE_BUILD_TYPE": "Release"
      }
  ],
  "buildPresets": [
    // ... other presets
    {
      "name": "release",
      "configurePreset": "release",
      "configuration": "Release"
    }
  ]
}
```


</CodeFile>


We can now configure and build our project with these new presets.  From the project root:


```c++
//<ti>
cmake --preset=release
```


```c++
//<ti>
cmake --build --preset=release
```


Our `GreeterBenchmarks` target should have generated a `GreeterBenchmarks` (or `GreeterBenchmarks.exe`) in the `build/benchmarks` directory.  We can run it in the usual way from the project root:


```c++
//<ti>
./build/benchmarks/GreeterBenchmarks
```


Google Benchmark will run the test and produce a detailed report showing the average time taken per iteration, CPU time, and other useful metrics.


```c++
//<o>
-----------------------------------------------
Benchmark            Time      CPU   Iterations
-----------------------------------------------
BM_Greeter_Greet  72.3 ns  69.8 ns      8960000
```


<Pw>


## Parameterized Benchmarks


A simple benchmark is useful, but often we'll want to run the same benchmark across many cases.  For example, we might want to see how our code behaves across a variety of different input sizes, or to compare the performance of multiple options.


Much like Google Test, Google Benchmark includes utilitities to help us create **parameterized benchmarks** that run the same code with different arguments.


Let's modify our `Greeter` class to greet a specific person by name, and then benchmark how the `greet()` method performs with names of different lengths.


<CodeViewer>


<CodeFile path="greeter/include/greeter/Greeter.h" status="Updated">


```c++
#pragma once
#include <string>

class Greeter {
public:
  explicit Greeter(std::string name); //<h>
  std::string greet() const;

private:
  std::string name_; //<h>
};
```


</CodeFile>


<CodeFile path="greeter/src/Greeter.cpp" status="Updated">


```c++
#include <greeter/Greeter.h>

Greeter::Greeter(std::string name)
  : name_(std::move(name)) {}

std::string Greeter::greet() const {
  return "Hello, " + name_ + "!";
}
```


</CodeFile>


<CodeFile path="tests/greeter/test_greeter.cpp" status="Updated">


```c++
#include <gtest/gtest.h>
#include <greeter/Greeter.h>
#include <tuple>

class GreeterNameTest :
  public testing::TestWithParam<
    std::tuple<std::string, std::string>
  > {};

TEST_P(GreeterNameTest, GreetsCorrectlyForName) {
  auto [name, expected_greeting]{GetParam()};
  Greeter greeter{name.empty() ? Greeter() : Greeter(name)};
  EXPECT_EQ(greeter.greet(), expected_greeting);
}

INSTANTIATE_TEST_SUITE_P(
  NamedGreetings,
  GreeterNameTest,
  testing::Values(
    std::make_tuple("", "Hello, User!"),
    std::make_tuple("John", "Hello, John!"),
    std::make_tuple("Jane", "Hello, Jane!")
  )
);
```


</CodeFile>


</CodeViewer>


Now, we can update our benchmarking. We'll pass arguments to the benchmark function using the `Arg()` method, representing the length of the name we want to test:


<CodeFile path="benchmarks/bench_main.cpp" status="Updated">


```c++
#include <benchmark/benchmark.h>
#include <greeter/Greeter.h>
#include <string>

static void BM_Greeter_Greet(benchmark::State& state) {
  // state.range(0) is the first argument to the benchmark
  std::string name(state.range(0), 'x');
  Greeter g(name);

  for (auto _ : state) {
    std::string result = g.greet();
    benchmark::DoNotOptimize(result);
  }
}

// Register benchmarks with different arguments
BENCHMARK(BM_Greeter_Greet)->Arg(8);//<h>
BENCHMARK(BM_Greeter_Greet)->Arg(64);//<h>
BENCHMARK(BM_Greeter_Greet)->Arg(512);//<h>
BENCHMARK(BM_Greeter_Greet)->Arg(4096);//<h>
BENCHMARK(BM_Greeter_Greet)->Arg(32768);//<h>

BENCHMARK_MAIN();
```


</CodeFile>


This `Arg()` approach is generally flexible, but the specific case of testing different input sizes is so common that a shortcut is available in the form of the `Range()` method:


```c++
// Before:
BENCHMARK(BM_Greeter_Greet)->Arg(8);//<d>
BENCHMARK(BM_Greeter_Greet)->Arg(64);//<d>
BENCHMARK(BM_Greeter_Greet)->Arg(512);//<d>
BENCHMARK(BM_Greeter_Greet)->Arg(4096);//<d>
BENCHMARK(BM_Greeter_Greet)->Arg(32768);//<d>

// After:
BENCHMARK(BM_Greeter_Greet)->Range(8, 32768);//<h>
```


This use of `Range(8, 32768)` tells Google Benchmark to run this benchmark multiple times. It will start with an argument of 8, and for each subsequent run, it will multiply the argument by 8 until it reaches or exceeds $32,768$ (which is $8^5$).


If we run this new benchmark, we'll get a table of results showing how the performance scales with the input size:


```c++
//<ti>
cmake --preset release
```


```c++
//<ti>
cmake --build --preset release
```


```c++
//<ti>
./build/benchmarks/GreeterBenchmarks
```


```c++
//<o>
------------------------------------------------------
Benchmark                  Time        CPU  Iterations
------------------------------------------------------
BM_Greeter_Greet/8      15.7 ns    15.7 ns    44800000
BM_Greeter_Greet/64     91.6 ns    92.1 ns     7466667
BM_Greeter_Greet/512     106 ns     106 ns     5600000
BM_Greeter_Greet/4096    167 ns     167 ns     4480000
BM_Greeter_Greet/32768  1507 ns    1507 ns      497778
```


## Other Benchmark Features


Google Benchmark has a wide range of features to support benchmarking. Here are a few other capabilities you might find useful:

- **Fixtures**: Just like in GoogleTest, you can create a fixture class (by inheriting from `benchmark::Fixture`) to handle complex setup and teardown logic that can be shared across multiple benchmarks.
- **Time Units**: You can control the time unit reported in the output (nanoseconds, microseconds, etc.) by calling `Unit(benchmark::kMillisecond)` on your benchmark registration.
- **Complexity Analysis**: Google Benchmark can automatically compute the asymptotic complexity of your code - e.g., $O(n)$, $O(n^2)$ - if you provide it with the input size via the `Arg()` or `Range()` methods.
- **Custom Counters**: You can report your own custom metrics (e.g., "bytes processed per second") from within your benchmark loop.

For a complete guide to all the features, the [official Google Benchmark documentation](https://github.com/google/benchmark/blob/main/docs/user_guide.md) is the best resource.


## Summary


In this lesson, we've seen how to to start measuring performance within our build.

- **Micro-benchmarking**: A benchmark is a program for measuring the performance of a small piece of code in a controlled environment.
- **Google Benchmark**: This is an extremely popular library for C++ benchmarking. We integrated it into our project using vcpkg and a dedicated `benchmarks` target.
- **Writing Benchmarks**: We learned the basic structure of a benchmark, including the `for (auto _ : state)` loop and the importance of `benchmark::DoNotOptimize()` to prevent the compiler from removing the code being tested.
- **Parameterized Benchmarks**: We used the `Arg()` and `Range()` method to run our benchmark with a variety of inputs, allowing us to see how performance scales.

<Preview>


### Static Analysis using Clang-Tidy


We've now covered dynamic analysis (ASan) to find runtime errors and performance analysis (benchmarking) to measure speed. But what about bugs and style issues that can be caught before the code is even compiled?


In the next lesson, we will explore **static analysis**. We'll integrate **Clang-Tidy**, a **linter**, into our CMake build to automatically check our code for common programming errors, style violations, and adherence to modern best practices.


</Preview>


</Pw>

Algorithm Analysis and Big O Notation

Google Benchmark GitHub Repository

Google Benchmark User Guide

google-benchmark port

Benchmarking C++ Code - Bryce Adelstein Lelbach

Online C++ Benchmarking Tool

How to use google benchmark with cmake?

Learn how to measure C++ code performance by integrating Google Benchmark with CMake. This tutorial covers writing micro-benchmarks, using parameterized tests with ->Range(), and creating a dedicated benchmark target for reproducible results.

Using Google Benchmark

Understanding the Basics

Practical Applications

Common Mistakes

Next Steps

Additional Resources

Assessment Preparation

Managing C++ Projects Using CMake