Modules are the centralised method of accessing different software on an HPC cluster. By using a variety of modules you can quickly and easily access different versions of applications and create work flows that suit particular projects. The modules offered on Apocrita cover a wide range of applications but there will always be situations that require something unusual or a relatively niche version of a piece of software.
The ITSR support team often receive tickets from Anaconda users concerned that creating environments and installing packages is taking quite a long time. We recently installed Miniconda as a module on Apocrita, which enables users to install packages using the Mamba libsolver.
Since the last module update in December 2021, we have:
Sometimes you may find yourself needing to filter a large amount of output
using the grep command. However, grep can sometimes struggle when you try
to filter files with an incredibly large number of lines, as it loads each line
into RAM line-by-line. This can mean you can quickly exhaust even large amounts
of requested RAM. There are a few ways around this.
Following up from part one of our R tutorial we'll be taking a look at the differences between R - the command-line language which can be loaded as a module and used in your Apocrita batch jobs - and Rstudio - the graphical development environment, accessed via a web server and provided via the OnDemand service.
The ITSR support team often receive tickets from
R users that cover similar
ground. So we thought we would collate our most frequent responses into some
"Top Tips"! The tips below apply equally to Rscript but this article only
covers the interactive R program.
As the current 2 PetaByte Research Data Storage on Apocrita reaches end of life this summer, we have procured a new storage system, providing 5PB of capacity. This means faster, bigger and cheaper storage for you, the researcher! Read on to discover the benefits...but first, an important notice.
The RSE team in ITS Research has had a busy few years since we started sharing our work in this blog. In this post we look at some highlights of recent activity and what we have to look forward to.
You may wonder why some jobs start immediately but some wait in the queue for hours or days, even if your job is quite simple. If you notice your job has been queueing for a while, you may want to consider adjusting the requested resources to reduce queueing time and reduce any potential resource wastage as the job runs. Below, we outline two useful tools for you to check the resource usage of previous jobs.
Polytomous variables can be used to model data that has two or more possible outcomes. For example, a survey with multiple-choice questions is polytomous. The R package, poLCA, does statistical clustering of polytomous variables. For example, grouping together survey results that are similar to each other.