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 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.

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.

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.