Book Image

Julia Cookbook

By : Raj R Jalem, Jalem Raj Rohit
Book Image

Julia Cookbook

By: Raj R Jalem, Jalem Raj Rohit

Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Table of Contents (12 chapters)

Parallel maps and loop operations

In this recipe, you will learn a bit about the famous Map-Reduce framework and why it is one of the most important ideas in the domains of big data and parallel computing. You will learn how to parallelize loops and use reducing functions on them through several CPUs and machines and you will further explore the concept of parallel computing, which you learned about in the previous recipes.

Getting ready

Just like the previous sections, Julia just needs to be running in multiprocessing mode to work through the following examples. This can be done through the instructions given in the first section.

How to do it...

  1. Firstly, we will write a function that takes and adds n random bits. The writing of this function has nothing to do with multiprocessing. So, it has simple Julia functions and loops. This function can be written as follows:

  2. Now, we will use the @spawn macro, which we learned about previously, to run the count_heads() function as separate processes...