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.
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.
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:
Now, we will use the
@spawnmacro, which we learned about previously, to run thecount_heads()function as separate processes...