In this webinar, we focus on GPU-accelerated computing with Julia, one of the most popular high-level, general-purpose dynamic programming languages for science, engineering, data analytics, and deep learning applications. Starting from the basic syntax of Julia, we will span topics on multiple-dispatch paradigm, metaprogramming, and then additional special features of Julia for classic machine learning and deep learning, with a focus on their unique features and capabilities for high-performance computing.
In the past decade, Graphics Processing Units (GPUs) have ignited the dynamic evolution of data science. But GPUs can do a lot more than machine learning – these powerful devices can accelerate and massively parallelise any general-purpose computational load in domains involving big data and heavy number crunching. You can use the GPU in your personal computer, or scale up your application to run on a supercomputer. How can you get started?
The GPU programming using Julia webinar is for data scientists, software developers and researchers who are:
After attending this seminar, you will be able to:
The ENCCS Practical Intro webinar series aims to provide concise and condensed introductions to key topics in high-performance computing and related technologies. Webinar materials, such as notebooks and code examples, are made available online on GitHub.
For more thorough tutorials and self-study materials, please visit the library of ENCCS lessons (https://enccs.se/lessons/).
Register by visiting this link https://events.prace-ri.eu/event/1580/registrations/1142/
Due to EuroCC2 regulations, we CAN NOT ACCEPT generic or private email addresses. Please use your official university or company email address for registration.
This training is intended for users established in the European Union or a country associated with Horizon 2020. You can read more about the countries associated with Horizon2020 here https://ec.europa.eu/info/research-and-innovation/statistics/framework-programme-facts-and-figures/horizon-2020-country-profiles_e