Contemporary computer chips have multiple cores, but R will run your programs on only of them, even if your task could performed in parallel. In this workshop we will see how to identify which tasks can be done in parallel and show how make them go faster by spreading their computation across cores on your machine. 

This is the first of three workshops on how to speed up your R code. In this workshop we make minimal changes to existing code and operate only with your machine. In later workshops we will show how to spread your computation across multiple machines in the university's clusters, and also how to rewrite slow code as C++.

Prerequisites

You should be familiar with loops and have some basic understanding of the lapply function in R.

Audience

This workshop is open to anyone interested in speeding up their R code without making use of university clusters or translating code into other languages. If you are not a politics graduate student, please send email to [email protected] that you are planning to attend, so we can ensure enough space in the room.

Materials: parallel-local.zip