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Some Pleasingly Parallel GPU Case Studies in Machine Learning

In a previous blog, we discussed ways we could use multiprocessing and mpi4py together to use multiple nodes of GPUs. We will cover some machine learning principles and two examples of pleasingly parallel machine learning problems. Also known as embarrassingly parallel problems, I rather call them pleasingly because there isn't anything embarrassing when you design your problem to be run in parallel. When doing so, you could launch very similar functions to each GPU and collate their results when needed.

Some Strategies for Using Multiple Nodes of GPUs

Using multiple GPUs is one option to speed up your code. On Apocrita, we have V100, A100 and H100 GPUs available, with up to 4 GPUs per node. On other compute clusters, JADE2 has 8 V100 GPUs per node and Sulis has 3 A100 GPUs per node. If your problem is pleasingly parallel, you can distribute identical or similar tasks to each GPU on a node, or even on multiple nodes.

R Workflow

Nowadays, there seems to be an R package for anything and everything. While this makes starting a project in R seem quick and easy, there are considerations to take into account that will make your life easier in the long run.

A Large Scale Ancestry Study | Living with Machines

Living with Machines is a funded project at The Alan Turing Institute (aka the Turing), bringing together academics from different disciplines, to answer research questions such as how did historical newspapers tell the political landscape, how were accidents in factories reported, how did road and settlement names change, how did people change occupations during the industrial revolution...

Pythonic Parallel Processing for HPC: Your Gauss is as good as mine

There are many strategies and tools for improving the performance of Python code, for a comprehensive treatment see High Performance Python by Gorelick and Ozsvald (institutional access is available to QM staff). However, there are some subtleties when using them in an HPC environment. More bluntly, requesting processor cores does not automatically mean your code will use them effectively, and that cannot happen if it doesn't know how many of them there are!

Intel Inspector 2022.2 on Apocrita

As the complexity of HPC applications increases, the management of memory and threading scopes becomes increasingly important. Tools like Intel Inspector are crucial in this context, to effectively identify and resolve a wide array of memory errors and thread synchronisation issues.

RSE team activities up to June 2022

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.

Assessing code quality with the NAG Fortran compiler

The NAG Fortran compiler, like other compilers, has diagnostic capabilities which can help us write correct and portable Fortran programs. In this post we'll look at these, comparing with those of the GCC and Intel compilers, and see how the compiler can be a valuable tool when developing or maintaining Fortran code.