Automatic differentiation is a set of techniques to efficiently and accurately compute the derivative of a function represented by a computer program. Existing C++ libraries for automatic differentiation (e.g. Adept, Stan Math Library), however, exhibit large memory consumptions and runtime performance issues. This paper introduces FastAD, a new C++ template library for automatic differentiation, that overcomes all of these challenges in existing libraries by using vectorization, simpler memory management using a fully expression-template-based design, and other compile-time optimizations to remove some run-time overhead. Benchmarks show that FastAD performs 2-10 times faster than Adept and 2-19 times faster than Stan across various test cases including a few real-world examples.
@article{arxiv.2102.03681,
title = {FastAD: Expression Template-Based C++ Library for Fast and Memory-Efficient Automatic Differentiation},
author = {James Yang},
journal= {arXiv preprint arXiv:2102.03681},
year = {2021}
}