In response to a coordinated security attack on HPC sites world-wide back in 2020, it was necessary to implement some changes to enforce a higher level of authentication security. In this article, we begin with providing some useful information to understand key-based authentication, and document the process for accessing the cluster.
As a result of the large-scale shift to remote working due to the COVID-19 pandemic, we have been asked various questions relating to computational research, which we'll try to address below. We've seen an increase in the number of new account requests for the HPC service, and we realise there will be quite a few users wishing to run workloads on the cluster for the first time. Fortunately, the design of the HPC cluster service means that for many of you, your workflow may remain the same as when you were based on-campus.
Here is a round-up of recent QMUL HPC cluster news from the ITS Research team, including information about new compute nodes.
A common first program to write in a new language is a "Hello world" example where we print a simple line of output. In this tutorial we first look at examples written in C, C++ and Fortran. To run the examples we'll learn about interactive sessions on compute nodes, modules and compiling source code. We'll also look at examples in MATLAB, Python and R. For these we'll see how to use modules to select suitable interpreters.
Many people rely on compilers, for languages such as C, C++ and Fortran, to create executable programs from source code. Just like our source code, compilers themselves may have bugs. In this post we look at common forms of compiler bug, with examples, and what we can do when our work is affected by such an issue.
Since the last module update in August, we have added 22 modules to dev, added 22 modules to production and removed 4 modules.
This article presents a selection of useful tips for running successful and well-performing jobs on the QMUL Apocrita cluster.
In the ITS Research team, we spend quite a bit of time monitoring the Apocrita cluster and checking jobs are running correctly, to ensure that this valuable resource is being used effectively. If we notice a problem with your job, and think we can help, we might send you an email with some recommendations on how your job can run more effectively. If you receive such an email, please don't be offended! We realise there are users with a range of experience, and the purpose of this post is to point out some ways to ensure you get your results as quickly and correctly as possible, and to ease the learning curve a little bit.
Compression tools can significantly reduce the amount of disk space consumed by your data. In this article, we will look at the effectiveness of some compression tools on real-world data sets, make some recommendations, and perhaps persuade you that compression is worth the effort.
Since the last update in April, we have updated the following module files:
We are simplifying the way that the multi-node parallel jobs are run on the cluster.
Currently, users wishing to run multi-node MPI jobs on the public queues must choose beforehand whether to run on the nxv parallel nodes or the sdv parallel nodes, and to configure the job accordingly for the number of cores on each type of node.