Two exciting talks:

Easy peasy massive parallel computing in R

by Mikkel Krogsholm

Wouldn’t it be nice to be able to write simple R-code that very simply scales to massive parallel computing?

The future and the furrr package in R provides a framework that makes it possible for you to write code, that works seamlessly on your laptop or on a supercomputer. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines.

There is no need to modify any code in order switch from sequential on the local machine to distributed processing on a remote compute cluster. Global variables and functions are also automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures.

This R-talk shows you how. We will run through a concrete example that we first execute on a local machine and then on a much more powerful server.

R at scale on the Google Cloud Platform

by Mark Edmonson

This talk covers my current thinking on what I consider the optimal way to work with R on the Google Cloud Platform (GCP). It seems this has developed into my niche, and I get questions about it so would like to be able to point to a URL.

Both R and the GCP rapidly evolve, so this will have to be updated I guess at some point in the future, but even as things stand now you can do some wonderful things with R, and can multiply those out to potentially billions of users with GCP. The only limit is ambition.

The common scenarios I want to cover are: Scaling a standalone R script and
Scaling Shiny apps and R APIs