Want to speed up your R analyses?

To really unlock the power of HPC, you need to know how to make your code run in parallel. Parallel programming lets you split tasks across many processors at once instead of running them one by one. The result? You can train bigger models, crunch larger datasets, and finish analyses in hours instead of days — all directly from your R scripts.

In this sessions, we will focus on R, one of the most used languages for statistical computing, machine learning (ML), artificial intelligence (AI), and natural language processing (NLP).

More specifically, you will:

• Gain a theoretical introduction to the basics of parallel programming.
• Learn how to apply parallel programming within R.
• Work through an example of parallelizing a machine learning model using the Tidymodels framework.
• Learn how to set up and run an R script on a multi-machine Slurm cluster via UCloud.

After the session, you will know how to run your own workflows efficiently on HPC resources — you will also have a much clearer picture of what happens “under the hood” in popular frameworks such as Tidymodels, Torch, and TensorFlow.

Prerequisites

• Intermediate experience in R, including writing scripts, handling datasets, and applying standard libraries for data analysis or machine learning.

No prior HPC experience is required, but attending the introductory HPC session (“Speed Up Your Research with High Performance Computing”) is strongly recommended before this one.