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.
As part of our commitment to providing stable and manageable systems, here is a round-up of some recent updates we have been working on behind the scenes:
Fortran provides a variety of intrinsic representations of real numbers. In this post we look at what these representations are and how we choose a particular representation for our work.
Since the last update in March, we have updated the following module files:
We are pleased to announce a new scratch storage array that is based on fast NvME based hardware. This will hopefully make I/O related tasks much faster
We have installed the NAG Fortran compiler on Apocrita for use by researchers from the School of Economics and Finance. In this post we look at how to access the compiler, why we may want to use it, and what we have to pay special attention to.
Since the last update (on 29/11/2018), we have updated the following module files:
On Wednesday 2019-02-20 at 14:00 we will be applying an upgrade to our GitHub Enterprise instance to version 2.16.2, which includes bug fixes and the latest security updates.
At any one time, a typical HPC cluster is usually full. This is not such a bad thing, since it means the substantial investment is working hard for the money, rather than sitting idle. A less ideal situation is having to wait too long to get your research results. However, jobs are constantly starting and finishing, and many new jobs get run shortly after being added to the queue. If your resource requirements are rather niche, or very large, then you will be competing with other researchers for a more scarce resource. In any case, whatever sort of jobs you run, it is important to choose resources optimally, in order to get the best results. Using fewer cores, although increasing the eventual run time, may result in a much shorter queuing time.
We have deployed the latest version of Environment Modules (4.2.1) across the cluster on all frontend and compute nodes.