Book Image

Scientific Computing with Scala

By : Vytautas Jancauskas
Book Image

Scientific Computing with Scala

By: Vytautas Jancauskas

Overview of this book

Scala is a statically typed, Java Virtual Machine (JVM)-based language with strong support for functional programming. There exist libraries for Scala that cover a range of common scientific computing tasks – from linear algebra and numerical algorithms to convenient and safe parallelization to powerful plotting facilities. Learning to use these to perform common scientific tasks will allow you to write programs that are both fast and easy to write and maintain. We will start by discussing the advantages of using Scala over other scientific computing platforms. You will discover Scala packages that provide the functionality you have come to expect when writing scientific software. We will explore using Scala's Breeze library for linear algebra, optimization, and signal processing. We will then proceed to the Saddle library for data analysis. If you have experience in R or with Python's popular pandas library you will learn how to translate those skills to Saddle. If you are new to data analysis, you will learn basic concepts of Saddle as well. Well will explore the numerical computing environment called ScalaLab. It comes bundled with a lot of scientific software readily available. We will use it for interactive computing, data analysis, and visualization. In the following chapters, we will explore using Scala's powerful parallel collections for safe and convenient parallel programming. Topics such as the Akka concurrency framework will be covered. Finally, you will learn about multivariate data visualization and how to produce professional-looking plots in Scala easily. After reading the book, you should have more than enough information on how to start using Scala as your scientific computing platform
Table of Contents (16 chapters)
Scientific Computing with Scala
About the Author
About the Reviewer

Chapter 1. Introducing Scientific Computing with Scala

Scala was first publicly released in 2004 by Martin Odersky, then working at École Polytechnique Fédérale de Lausanne in Switzerland. Odersky took part in designing the current generation of the Java compiler javac as well. Scala programs, when compiled, run on the Java Virtual Machine (JVM). Scala is the most popular of all the JVM languages (except for Java.) Like Java, Scala is statically typed. From the perspective of a programmer, this means that variable types will have to be declared (unless they can be inferred by the compiler) and they cannot change during the execution of the program. This is in contrast to dynamic languages, such as Python, where you don't have to specify a variable's type and can assign anything to any variable at runtime. Unlike Java, Scala has strong support for functional programming. Scala draws inspiration from languages such as Haskell, Erlang, and others in this regard.

In this chapter, we will talk about why you would want to use Scala as your primary scientific computing environment. We will consider the advantages it has over other popular programming languages that are used in the scientific computing context. We will then go over Scala packages meant specifically for scientific computing. These will be considered briefly and will be divided into categories depending on what they are used for. Some of these we will consider in detail in later chapters.

Finally, we provide a small introduction on best practices for how to structure, build, test, and distribute your Scala software. This is important even to people who know how to program in Scala already. This chapter introduces concepts that I consider essential, to write scientific software successfully. They are, however, often overlooked by scientists. The reason is that scientists often don't concern themselves with software development techniques; instead, they prefer to get the job done quickly. For example, it is not uncommon to neglect build systems, which are very important no matter what language you are using and are essential when writing software in statically typed, compiled languages. Testing is another area of software development criminally overlooked by the scientists I have had the pleasure to work with. The same is usually true of IDE's, debuggers, and profilers. All of these following topics will be discussed:

  • Why Scala for scientific computing?

  • Numerical computing packages for Scala

  • Data analysis packages for Scala

  • Other scientific software

  • Alternatives for carrying out plotting

  • Using Emacs as the Scala IDE

  • Profiling Scala code

  • Debugging Scala code

  • Building, testing, and distributing your Scala software

  • Mixing Java and Scala code