Book Image

Julia 1.0 Programming Cookbook

By : Bogumił Kamiński, Przemysław Szufel
Book Image

Julia 1.0 Programming Cookbook

By: Bogumił Kamiński, Przemysław Szufel

Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt

Distributed computing with Julia

Julia provides built-in language functionality to run a program across many processes that can run locally, across a distributed network, or in a computational cluster. In the Multiprocessing with Julia and Sending parameters to remote Julia processes recipes, we saw how can you run data and exchange data between Julia processes.


A typical scenario for distributed computing is running a parameter sweep over a significantly large set of computations. In this recipe, we will show how to create a distributed cluster that performs a parameter sweep over a numerical simulation model.

We explain how to use the--machine-fileJulia options to run Julia workers across many nodes. However, the computational example can also be run on a single machine using the multiprocessing mode (for example, in Julia launched with thejulia -p 4command).

Getting ready

In this example, we will run a distributed cluster in Julia. You can run the cluster on a single laptop or across a cluster...