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.
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.
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