Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware.AAD recently became a centerpiece of modern financial systems, and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters programs in finance.Wiley’s Computational Finance books, written by some of the very people who wrote Danske Bank’s award-winning systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives software.This volume is a complete, self-contained learning reference for AAD, and its application in finance. AAD is explained in deep detail throughout chapters that gently lead readers from the theoretical foundations to the most delicate areas of an efficient implementation, such as memory management, parallel implementation and acceleration with expression templates.The book also covers the design of generic, parallel simulation libraries, and modern and parallel programming in C++. It comes with the complete source code of a professional, efficient, up to date AAD library in C++. The publication builds the code step by step, while the code demonstrates the practical use of the concepts and notions.