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Julia is a high-level programming language well suited for scientific computing and data science. Just-in-time compilation, among other things, makes Julia really fast yet interactive. For heavy computations, Julia supports multi-threaded and multi-process parallelism, both natively and via a number of external packages. It also supports memory arrays distributed across multiple processes either on the same or different nodes.

In this hands-on course, we will start with a quick review of Julia’s multi-threading features but will focus primarily on Distributed standard library and its large array of tools. We will demo parallelization using two problems: a slowly converging series and a Julia set. We will run examples on a multi-core laptop and an HPC cluster.

Prerequisites: Basic knowledge of Julia and familiarity with the Alliance HPC cluster environment, in particular, with the Slurm scheduler.