Chapter 1. Julia is Fast

In many ways, the history of programming languages has often been driven by, and certainly intertwined, with the needs of numerical and scientific computing. The first high-level programming language, Fortran, was created with scientific computing in mind, and continues to be important in the field even to this day. In recent years, the rise of data science as a specialty has brought additional focus to scientific computing, particularly for statistical uses. In this area, somewhat counterintuitively, both specialized languages such as R and general-purpose languages such as Python are in widespread use. The rise of Hadoop and Spark has spread the use of Java and Scala respectively among this community. In the midst of all this, Matlab has had a strong niche within engineering and communities, while Mathematica remains unparalleled for symbolic operations.

A new language for scientific computing therefore has a very high barrier to overcome. It's been only a few short years since the Julia language was introduced into the world. In this time, it's innovative features, which make it a dynamic language, based on multiple dispatch as its defining paradigm, has created growing niche within the numerical computing world. However, it's the claim of high performance that excited its early adopters the most.

This, then, is a book that celebrates writing high-performance programs. With Julia, this is not only possible, but also reasonably straightforward, within a low-overhead, dynamic language.

As a reader of this book, you have likely already written your first few Julia programs. We will assume that you have successfully installed Julia, and have a working programming environment available. We expect you are familiar with very basic Julia syntax, but we will discuss and review many of those concepts throughout the book as we introduce them.