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 webinar, 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 three problems: a slowly converging series, a Julia set, and an N-body solver. We will run examples on a multi-core laptop and an HPC cluster.
- Alex Razoumov, Visualization and Training Coordinator, WestGrid
- Marie-Hélène Burle, Research Computing Training Assistant, WestGrid
- Click the green "Register' button on this page. All registrants will be emailed the connection instructions for this webinar.
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