multiprocessing – IRIC's Bioinformatics Platform

Simple multiprocessing in R (2nd edition)

The last time I spoke about this subject, I presented a really simple way to change an lapply call into its multicore sibling mclapply. Now while this is an extremely easy modification to implement in your code to gain substantial performance benefits, it kinda required you to be making use of the lapply function in the first place. So let's look at another way to introduce multiprocessing into your existing codebase with the use of the foreach and doMC packages. [...]

By Jean-Philippe|2017-04-29T16:24:45+00:00September 19, 2016|Categories: Performance, R|Tags: multiprocessing, test|0 Comments

Parallelize your Python !

This article will teach you what are multithreads, multicores, and in what circumstances each can be used. Your nerd friend keeps telling you about his professional deformation all the time? Wanting to parallelize and optimize his time? Do you wish to understand it as well and save time by parallelizing your programs in Python? Then this article is what you need! You will be able to gain big amounts of time, thanks to a small dose of parallelism [...]

By Clément Agret|2017-04-29T15:33:41+00:00March 29, 2016|Categories: Performance, Python|Tags: multiprocessing, multithreads|0 Comments

Simple multiprocessing in R

Continuing my effort to help you get the most out of your CPUs, I figured we could look into using some multiprocessing functionality available for your R scripts. While there are a few different options for running multi-core treatments on your data, we'll focus on something really simple to put in place. A while back, I was putting together a script to run a large series of logistic regressions (using the glm package) in an attempt to model some data. [...]

By Jean-Philippe|2017-04-29T15:33:26+00:00March 14, 2016|Categories: Performance, R|Tags: multiprocessing|1 Comment

Put Those CPUs to Good Use !

If you're like me, you've probably noticed that, by default, the python scripts we write only use a portion of the processing power at our disposal.. As such, you've probably said to yourself: Hey, I paid good money for a quad-core CPU ! What's happening ? While it's true that nowadays, most CPUs are multi-core, the code we write must also be tailored appropriately in order to make use of more than one at a time. So let's dive into [...]

By Jean-Philippe|2017-04-24T13:28:50+00:00July 12, 2015|Categories: Performance, Python|Tags: multiprocessing|0 Comments