Summary

In this chapter, we learned how Julia is different and how an LLVM-based JIT compiler enables Julia to approach the performance of C/C++. We introduced you to how to download Julia, install it, and build it from source. The notable features that we found were that the language is elegant, concise, and powerful and it has amazing capabilities for numeric and scientific computing.

We worked on some examples of working with Julia via the command line (REPL) and saw how full of features the language shell is. The features found were tab-completion, reverse-search, and help functions. We also discussed why should we use Jupyter Notebook and went on to set up Jupyter with the IJulia package. We worked on a simple example to use the Jupyter Notebook and Julia's visualization package, Gadfly.

In addition, we learned about Julia's powerful built-in package management and how to add, update, and remove modules. Also, we went through the process of creating our own package and publishing it to the community. We also introduced you to one of the most powerful features of Julia—multiple dispatch—and worked on some basic examples of how to create method definitions to implement multiple dispatch.

In addition, we introduced you to the parallel computation, explaining how it is different from conventional message passing and how to make use of all the compute resources available. We also learned Julia's feature of language interoperability and how we can call a Python module or a library from the Julia program.