Overview

Learn how to write efficient, maintainable C++ code for data-intensive applications in this hands-on course. Key application areas include scientific simulation software, statistical data processing, and computer graphics. You’ll begin by exploring how modern C++ supports high-efficiency programming and review the principles of a central piece of hardware in data-intensive applications: computer memory. Next, the course shifts to software design. You’ll examine performance shortcomings of traditional object-oriented programming and dive into data-oriented perspective, which improves performance by structuring code around the data itself. You’ll learn how this paradigm overcomes bottlenecks and leads to more scalable, high-performance solutions. Finally, you’ll apply these principles to modern multi-threaded systems. Through hands-on experience you will learn to use C++'s built-in parallel features to target both multi-core CPUs and GPUs.

Syllabus

Refresher: Basics of modern C++ and class-based polymorphism

To follow this MOOC, you should be familiar with the fundamentals of the C++ language. In this first module however, you will find a refresher on the most important concepts needed to complete the course. You will (re)discover variable types (stack variables, references, smart pointers), containers and algorithms, classes and polymorphism. If you already master these notions but your knowledge predates the C++11 standard, we still encourage you to follow this module for up-to-date recommendations on the efficient use of C++. In particular, have a look at Lesson 4 if you are not yet familiar with the notion of ranges in C++20 and later versions.

Compile-time code execution

Part of the work of processing data efficiently consists in reducing the amount of computations and the complexity of the execution path. A complex execution path is often implemented to express design choices in the organization of software components. In the form of a compile-time execution framework, C++ offers a way to offload this complexity to the compilation stage and keep the execution environment simple and efficient.

Memory

Memory is one of the most critical resources of a computing platform. In this module, you will learn how memory is used in the C++ language and how memory access works on different types of machines. You will translate this knowledge into code that is at the same time clearly structured and efficient.

Data-oriented design

Learn to think about your program in a way that puts the critical hardware resource, memory, in the center of your thoughts. Apply these principles to discrete-event systems, a problem solving strategy with applications in all applied areas of science. Understand that data-oriented programming is not opposed to object-oriented programming but rather offers a way to model your objects in a way that accounts for your data structure, your algorithms, and your hardware.

Using Parallel algorithms

This module introduces you to an amazing feature available in the C++17 and onward: the ability to parallelize an algorithm with almost no effort, using C++ parallel algorithms. You will learn that you can gain a manifold speedup with simple changes by running your code on multi-core CPUs and on GPUs. You will dive once more into the working principles of computer memories to understand how to reach substantial performance improvements, and you will link the concepts of data-oriented programming to efficient paralellism.