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.
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.
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.